Essential Graphing
Version 12
Essential Graphing
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JMP® 12 Essential Graphing
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Contents
Essential Graphing
1
Learn about JMP
Documentation and Additional Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Formatting Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
JMP Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
JMP Documentation Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
JMP Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Additional Resources for Learning JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Sample Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Learn about Statistical and JSL Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Learn JMP Tips and Tricks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Tooltips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMP User Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMPer Cable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMP Books by Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
The JMP Starter Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2
Introduction to Interactive Graphing
Overview of Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3
Graph Builder
Explore and Visualize Data Interactively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Overview of Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Example Using Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
The Graph Area and Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Element Type Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Buttons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Graph Builder Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Graph Builder Right-Click Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
10
Essential Graphing
Add Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Move Grouping Variable Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Separate Variables into Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Change Variable Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Remove Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Add Multiple Variables to the X or Y Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Merge Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Order Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Replace Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Create a Second Y Axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Add Multiple Variables to Grouping Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Modify the Legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Create Map Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Additional Examples Using Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Example of Adding Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Example of Adding Multiple Variables to the X or Y Zone . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Example of Merging Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Example of Ordering Variables Using a Second Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Example of Adding Multiple Variables to Grouping Zones . . . . . . . . . . . . . . . . . . . . . . . . . 59
Example of Replacing Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Example of Overlaying Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Measure Global Oil Consumption and Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Analyze Popcorn Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Examine Diamond Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4
Overlay Plots
Plot Several Numeric Y Variables against One X Variable . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Example of an Overlay Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
The Overlay Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Overlay Plot Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
General Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Y Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Additional Examples of the Overlay Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Function Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Essential Graphing
11
Plotting Two or More Variables with a Second Y-axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5
Scatterplot 3D
Create a Rotating Three-Dimensional View of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Example of a 3D Scatterplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Launch the Scatterplot 3D Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
The Scatterplot 3D Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Spin the 3D Scatterplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Change Variables on the Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Adjust the Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Assign Colors and Markers to Data Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Assign Colors and Markers in the Data Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Scatterplot 3D Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Normal Contour Ellipsoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Nonparametric Density Contours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Context Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Additional Examples of the Scatterplot 3D Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Example of an Ungrouped Normal Contour Ellipsoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Example of Grouped Normal Contour Ellipsoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Example of a Grouped Nonparametric Density Contour . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6
Contour Plots
View Multidimensional Relationships in Two Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . 111
Example of a Contour Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Launch the Contour Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
The Contour Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Contour Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Fill Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Contour Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Contour Plot Save Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Use Formulas for Specifying Contours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Additional Examples of Contour Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Example of Triangulation, Transform, and Alpha Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . 122
12
7
Essential Graphing
Bubble Plots
View Patterns in Multidimensional Data Using Bubble Plots . . . . . . . . . . . . . . . . . . . . . . 125
Example of a Dynamic Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Launch the Bubble Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Specifying Two ID Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Specifying a Time Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Interact with the Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Control Animation for Dynamic Bubble Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Select Bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Use the Brush Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Bubble Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Show Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Additional Examples of the Bubble Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Example of Specifying Only a Time Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Example of Specifying Only ID Variables and Splitting a Bubble . . . . . . . . . . . . . . . . . . . 139
Example of a Static Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Example of a Bubble Plot with a Categorical Y Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
8
Parallel Plots
View Patterns in Multidimensional Data by Plotting Parallel Coordinates . . . . . . . . . . 147
Example of a Parallel Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Launch the Parallel Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
The Parallel Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Interpreting Parallel Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Parallel Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Additional Examples of the Parallel Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Examine Iris Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Examine Student Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
9
Cell Plots
View Color-Intensity Plots of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Example of a Cell Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Launch the Cell Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
The Cell Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Cell Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Essential Graphing
13
Context Menu for Cell Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Additional Example of the Cell Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
10 Treemaps
View Multi-Level Categorical Data in a Rectangular Layout . . . . . . . . . . . . . . . . . . . . . . . 165
Example of Treemaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Launch the Treemap Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
The Treemap Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Treemap Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Context Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Additional Examples of the Treemap Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Example Using a Sizes Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Example Using an Ordering Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Example Using Two Ordering Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Example of a Continuous Coloring Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Example of a Categorical Coloring Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
Examine Pollution Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Examine Causes of Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Examine Patterns in Car Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
11 Scatterplot Matrix
View Multiple Bivariate Relationships Simultaneously . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Example of a Scatterplot Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Launch the Scatterplot Matrix Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Change the Matrix Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
The Scatterplot Matrix Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Scatterplot Matrix Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Example Using a Grouping Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Create a Grouping Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
14
Essential Graphing
12 Ternary Plots
View Plots for Compositional or Mixture Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Example of a Ternary Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Launch the Ternary Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
The Ternary Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
Mixtures and Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Ternary Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Additional Examples of the Ternary Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Example Using Mixture Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Example Using a Contour Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
13 Summary Charts
Create Charts of Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Example of the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Launch the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Plot Statistics for Y Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Use Categorical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Use Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Adding Error Bars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
The Chart Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Legends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Coloring Bars in a Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Chart Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
General Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Y Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Additional Examples of the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Example Using Two Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Example Using Two Grouping Variables and Two Category Variables . . . . . . . . . . . . . . 221
Plot a Single Statistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Plot Multiple Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Plot Counts of Variable Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
Plot Multiple Statistics with Two X Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Create a Stacked Bar Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Essential Graphing
15
Create a Pie Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Create a Range Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Create a Chart with Ranges and Lines for Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
14 Create Maps
Add Maps or Custom Shapes to Enhance Data Visualization . . . . . . . . . . . . . . . . . . . . . . 231
Overview of Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Example of Creating A Map in Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Map Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
Custom Map Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Background Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Images in Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
Change Map Colors and Transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
Examples of Creating Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Louisiana Parishes Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Hurricane Tracking Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
Office Temperature Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
Index
Essential Graphing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
16
Essential Graphing
Chapter 1
Learn about JMP
Documentation and Additional Resources
This chapter includes the following information:
•
book conventions
•
JMP documentation
•
JMP Help
•
additional resources, such as the following:
‒ other JMP documentation
‒ tutorials
‒ indexes
‒ Web resources
Figure 1.1 The JMP Help Home Window on Windows
Contents
Formatting Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
JMP Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
JMP Documentation Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
JMP Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Additional Resources for Learning JMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Sample Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Learn about Statistical and JSL Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Learn JMP Tips and Tricks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Tooltips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMP User Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMPer Cable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
JMP Books by Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
The JMP Starter Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Chapter 1
Essential Graphing
Learn about JMP
Formatting Conventions
19
Formatting Conventions
The following conventions help you relate written material to information that you see on
your screen.
•
Sample data table names, column names, pathnames, filenames, file extensions, and
folders appear in Helvetica font.
•
Code appears in Lucida Sans Typewriter font.
•
Code output appears in Lucida Sans Typewriter italic font and is indented farther than
the preceding code.
•
Helvetica bold formatting indicates items that you select to complete a task:
‒ buttons
‒ check boxes
‒ commands
‒ list names that are selectable
‒ menus
‒ options
‒ tab names
‒ text boxes
•
The following items appear in italics:
‒ words or phrases that are important or have definitions specific to JMP
‒ book titles
‒ variables
‒ script output
•
Features that are for JMP Pro only are noted with the JMP Pro icon
of JMP Pro features, visit http://www.jmp.com/software/pro/.
. For an overview
Note: Special information and limitations appear within a Note.
Tip: Helpful information appears within a Tip.
JMP Documentation
JMP offers documentation in various formats, from print books and Portable Document
Format (PDF) to electronic books (e-books).
20
Learn about JMP
JMP Documentation
Chapter 1
Essential Graphing
•
Open the PDF versions from the Help > Books menu or from the JMP online Help footers.
•
All books are also combined into one PDF file, called JMP Documentation Library, for
convenient searching. Open the JMP Documentation Library PDF file from the Help > Books
menu.
•
e-books are available at online retailers. Visit http://www.jmp.com/support/downloads/
documentation.shtml for details.
•
You can also purchase printed documentation on the SAS website:
http://support.sas.com/documentation/onlinedoc/jmp/index.html
JMP Documentation Library
The following table describes the purpose and content of each book in the JMP library.
Document Title
Document Purpose
Document Content
Discovering JMP
If you are not familiar
with JMP, start here.
Introduces you to JMP and gets you
started creating and analyzing data.
Using JMP
Learn about JMP data
tables and how to
perform basic
operations.
Covers general JMP concepts and
features that span across all of JMP,
including importing data, modifying
columns properties, sorting data, and
connecting to SAS.
Basic Analysis
Perform basic analysis
using this document.
Describes these Analyze menu platforms:
•
Distribution
•
Fit Y by X
•
Matched Pairs
•
Tabulate
How to approximate sampling
distributions using bootstrapping and
modeling utilities are also included.
Chapter 1
Essential Graphing
Learn about JMP
JMP Documentation
Document Title
Document Purpose
Document Content
Essential Graphing
Find the ideal graph
for your data.
Describes these Graph menu platforms:
•
Graph Builder
•
Overlay Plot
•
Scatterplot 3D
•
Contour Plot
•
Bubble Plot
•
Parallel Plot
•
Cell Plot
•
Treemap
•
Scatterplot Matrix
•
Ternary Plot
•
Chart
The book also covers how to create
background and custom maps.
Profilers
Learn how to use
interactive profiling
tools, which enable you
to view cross-sections
of any response
surface.
Covers all profilers listed in the Graph
menu. Analyzing noise factors is
included along with running simulations
using random inputs.
Design of
Experiments Guide
Learn how to design
experiments and
determine appropriate
sample sizes.
Covers all topics in the DOE menu and
the Screening menu item in the Analyze >
Modeling menu.
21
22
Learn about JMP
JMP Documentation
Chapter 1
Essential Graphing
Document Title
Document Purpose
Document Content
Fitting Linear Models
Learn about Fit Model
platform and many of
its personalities.
Describes these personalities, all
available within the Analyze menu Fit
Model platform:
Specialized Models
Learn about additional
modeling techniques.
•
Standard Least Squares
•
Stepwise
•
Generalized Regression
•
Mixed Model
•
MANOVA
•
Loglinear Variance
•
Nominal Logistic
•
Ordinal Logistic
•
Generalized Linear Model
Describes these Analyze > Modeling
menu platforms:
•
Partition
•
Neural
•
Model Comparison
•
Nonlinear
•
Gaussian Process
•
Time Series
•
Response Screening
The Screening platform in the Analyze >
Modeling menu is described in Design of
Experiments Guide.
Multivariate
Methods
Read about techniques
for analyzing several
variables
simultaneously.
Describes these Analyze > Multivariate
Methods menu platforms:
•
Multivariate
•
Cluster
•
Principal Components
•
Discriminant
•
Partial Least Squares
Chapter 1
Essential Graphing
Learn about JMP
JMP Documentation
Document Title
Document Purpose
Document Content
Quality and Process
Methods
Read about tools for
evaluating and
improving processes.
Describes these Analyze > Quality and
Process menu platforms:
Reliability and
Survival Methods
Consumer Research
Learn to evaluate and
improve reliability in a
product or system and
analyze survival data
for people and
products.
Learn about methods
for studying consumer
preferences and using
that insight to create
better products and
services.
•
Control Chart Builder and individual
control charts
•
Measurement Systems Analysis
•
Variability / Attribute Gauge Charts
•
Process Capability
•
Pareto Plot
•
Diagram
Describes these Analyze > Reliability and
Survival menu platforms:
•
Life Distribution
•
Fit Life by X
•
Recurrence Analysis
•
Degradation and Destructive
Degradation
•
Reliability Forecast
•
Reliability Growth
•
Reliability Block Diagram
•
Survival
•
Fit Parametric Survival
•
Fit Proportional Hazards
Describes these Analyze > Consumer
Research menu platforms:
•
Categorical
•
Multiple Correspondence Analysis
•
Factor Analysis
•
Choice
•
Uplift
•
Item Analysis
23
24
Learn about JMP
Additional Resources for Learning JMP
Chapter 1
Essential Graphing
Document Title
Document Purpose
Document Content
Scripting Guide
Learn about taking
advantage of the
powerful JMP
Scripting Language
(JSL).
Covers a variety of topics, such as writing
and debugging scripts, manipulating
data tables, constructing display boxes,
and creating JMP applications.
JSL Syntax Reference
Read about many JSL
functions on functions
and their arguments,
and messages that you
send to objects and
display boxes.
Includes syntax, examples, and notes for
JSL commands.
Note: The Books menu also contains two reference cards that can be printed: The Menu Card
describes JMP menus, and the Quick Reference describes JMP keyboard shortcuts.
JMP Help
JMP Help is an abbreviated version of the documentation library that provides targeted
information. You can open JMP Help in several ways:
•
On Windows, press the F1 key to open the Help system window.
•
Get help on a specific part of a data table or report window. Select the Help tool
from
the Tools menu and then click anywhere in a data table or report window to see the Help
for that area.
•
Within a JMP window, click the Help button.
•
Search and view JMP Help on Windows using the Help > Help Contents, Search Help, and
Help Index options. On Mac, select Help > JMP Help.
•
Search the Help at http://jmp.com/support/help/ (English only).
Additional Resources for Learning JMP
In addition to JMP documentation and JMP Help, you can also learn about JMP using the
following resources:
•
Tutorials (see “Tutorials” on page 25)
•
Sample data (see “Sample Data Tables” on page 25)
•
Indexes (see “Learn about Statistical and JSL Terms” on page 25)
Chapter 1
Essential Graphing
Learn about JMP
Additional Resources for Learning JMP
•
Tip of the Day (see “Learn JMP Tips and Tricks” on page 26)
•
Web resources (see “JMP User Community” on page 26)
•
JMPer Cable technical publication (see “JMPer Cable” on page 26)
•
Books about JMP (see “JMP Books by Users” on page 27)
•
JMP Starter (see “The JMP Starter Window” on page 27)
25
Tutorials
You can access JMP tutorials by selecting Help > Tutorials. The first item on the Tutorials menu
is Tutorials Directory. This opens a new window with all the tutorials grouped by category.
If you are not familiar with JMP, then start with the Beginners Tutorial. It steps you through the
JMP interface and explains the basics of using JMP.
The rest of the tutorials help you with specific aspects of JMP, such as creating a pie chart,
using Graph Builder, and so on.
Sample Data Tables
All of the examples in the JMP documentation suite use sample data. Select Help > Sample
Data Library to do the following actions to open the sample data directory.
To view an alphabetized list of sample data tables or view sample data within categories,
select Help > Sample Data.
Sample data tables are installed in the following directory:
On Windows: C:\Program Files\SAS\JMP\<version_number>\Samples\Data
On Macintosh: \Library\Application Support\JMP\<version_number>\Samples\Data
In JMP Pro, sample data is installed in the JMPPRO (rather than JMP) directory. In JMP
Shrinkwrap, sample data is installed in the JMPSW directory.
Learn about Statistical and JSL Terms
The Help menu contains the following indexes:
Statistics Index Provides definitions of statistical terms.
Lets you search for information about JSL functions, objects, and display
boxes. You can also edit and run sample scripts from the Scripting Index.
Scripting Index
26
Learn about JMP
Additional Resources for Learning JMP
Chapter 1
Essential Graphing
Learn JMP Tips and Tricks
When you first start JMP, you see the Tip of the Day window. This window provides tips for
using JMP.
To turn off the Tip of the Day, clear the Show tips at startup check box. To view it again, select
Help > Tip of the Day. Or, you can turn it off using the Preferences window. See the Using JMP
book for details.
Tooltips
JMP provides descriptive tooltips when you place your cursor over items, such as the
following:
•
Menu or toolbar options
•
Labels in graphs
•
Text results in the report window (move your cursor in a circle to reveal)
•
Files or windows in the Home Window
•
Code in the Script Editor
Tip: You can hide tooltips in the JMP Preferences. Select File > Preferences > General (or JMP
> Preferences > General on Macintosh) and then deselect Show menu tips.
JMP User Community
The JMP User Community provides a range of options to help you learn more about JMP and
connect with other JMP users. The learning library of one-page guides, tutorials, and demos is
a good place to start. And you can continue your education by registering for a variety of JMP
training courses.
Other resources include a discussion forum, sample data and script file exchange, webcasts,
and social networking groups.
To access JMP resources on the website, select Help > JMP User Community.
JMPer Cable
The JMPer Cable is a yearly technical publication targeted to users of JMP. The JMPer Cable is
available on the JMP website:
http://www.jmp.com/about/newsletters/jmpercable/
Chapter 1
Essential Graphing
Learn about JMP
Additional Resources for Learning JMP
27
JMP Books by Users
Additional books about using JMP that are written by JMP users are available on the JMP
website:
http://www.jmp.com/support/books.shtml
The JMP Starter Window
The JMP Starter window is a good place to begin if you are not familiar with JMP or data
analysis. Options are categorized and described, and you launch them by clicking a button.
The JMP Starter window covers many of the options found in the Analyze, Graph, Tables, and
File menus.
•
To open the JMP Starter window, select View (Window on the Macintosh) > JMP Starter.
•
To display the JMP Starter automatically when you open JMP on Windows, select File >
Preferences > General, and then select JMP Starter from the Initial JMP Window list. On
Macintosh, select JMP > Preferences > Initial JMP Starter Window.
28
Learn about JMP
Additional Resources for Learning JMP
Chapter 1
Essential Graphing
Chapter 2
Introduction to Interactive Graphing
Overview of Data Visualization
This book describes all of the different graphs and elements you can use to visualize your
data:
•
Graph Builder interactively creates many different types of graphs. See Chapter 3, “Graph
Builder”.
•
Overlay Plot produces plots of a single X column and one or more numeric Ys. See Chapter
4, “Overlay Plots”.
•
3D Scatterplot shows the values of numeric columns in the associated data table in a
rotatable, three-dimensional view. See Chapter 5, “Scatterplot 3D”.
•
Contour Plot constructs contours of a response in a rectangular coordinate system. See
Chapter 6, “Contour Plots”.
•
Bubble Plot creates a scatter plot that represents its points as circles, or bubbles. Bubble
plots can be dynamic (animated over time) or static (fixed bubbles that do not move). See
Chapter 7, “Bubble Plots”.
•
Parallel Plot draws connected line segments that represent each row in a data table. See
Chapter 8, “Parallel Plots”.
•
Cell Plot draws a rectangular array of cells where each cell corresponds to a data table
entry. See Chapter 9, “Cell Plots”.
•
Treemaps can show the magnitude of a measurement by varying the size or color of a
rectangular area. See Chapter 10, “Treemaps”.
•
Scatterplot Matrix shows ordered collection of bivariate graphs. See Chapter 11,
“Scatterplot Matrix”.
•
Ternary Plot display the distribution and variability of three-part compositional data. See
Chapter 12, “Ternary Plots”.
•
Chart plots continuous variables versus categorical variables. You can create bar charts,
pie charts, and line charts. See Chapter 13, “Summary Charts”.
•
Maps can be used in Graph Builder, but also in other platforms, as background maps. See
Chapter 14, “Create Maps”.
30
Introduction to Interactive Graphing
Chapter 2
Essential Graphing
Chapter 3
Graph Builder
Explore and Visualize Data Interactively
You can quickly create and modify graphs using Graph Builder’s interactive interface. Select
the variables that you want to graph and drag and drop them into zones. The instant feedback
encourages further exploration of the data. Using Graph Builder, you can:
•
Change the graph type with the click of a button. Graph types include bar charts, pie
charts, histograms, maps, contour plots, and many more.
•
Examine and illustrate the relationships between several variables in a graph.
The experimental nature of Graph Builder means that you can try out different variables in
different places and try out different types of graphs until you find the best fit for your data.
Figure 3.1 Example Using a Map in Graph Builder
Contents
Overview of Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Example Using Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Launch Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
The Graph Area and Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Element Type Icons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Graph Builder Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Add Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Move Grouping Variable Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Separate Variables into Groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Change Variable Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Remove Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Add Multiple Variables to the X or Y Zone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Merge Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Order Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Replace Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Create a Second Y Axis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Add Multiple Variables to Grouping Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Modify the Legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Create Map Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Additional Examples Using Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Chapter 3
Essential Graphing
Graph Builder
Overview of Graph Builder
33
Overview of Graph Builder
You can interact with Graph Builder to create visualizations of your data. You start with a
blank slate and drag and drop variables to place them where you want them. Instant feedback
encourages exploration and discovery. Change your mind and move variables to new
positions, or right-click to change your settings.
Graph Builder helps you see multi-dimensional relationships in your data with independent
grouping variables for side-by-side or overlaid views. With many combinations to compare,
you can create a trellis display of small graphs. Graph elements supported by Graph Builder
include points, lines, bars, histograms, box plots, and contours.
The underlying philosophy of Graph Builder is to see your data. To that end, the default
visualization elements impose no assumptions, such as normality. If there are not too many
observations, you see all of them as marks on the graph. A smooth trend curve follows the
data instead of an equation.
Once you see the data, you can draw conclusions directly, or decide where further analysis is
needed to quantify relationships.
Example Using Graph Builder
You have data about nutrition information for candy bars. You want to find out which factors
can best predict calorie levels. Working from a basic knowledge of food science, you believe
that the fat content is a good place to start.
1. Select Help > Sample Data Library and open Candy Bars.jmp.
2. Select Graph > Graph Builder.
3. Drag and drop Total fat g into the X zone.
4. Drag and drop Calories into the Y zone.
34
Graph Builder
Example Using Graph Builder
Chapter 3
Essential Graphing
Figure 3.2 Example of Calories versus Total fat g
As you suspected, the candy bars with higher fat grams also have higher calories. But the
relationship is not perfect. You can add other factors to try to increase the correlation.
Next, determine whether cholesterol has an effect.
5. Drag and drop Cholesterol g into the Group X zone.
Chapter 3
Essential Graphing
Graph Builder
Example Using Graph Builder
35
Figure 3.3 Example of Calories versus Total fat g by Cholesterol g in the Group X Zone
Eight levels of the variable make the graph difficult to read. Try putting Cholesterol g into
the Wrap zone instead.
6. Click on Cholesterol g in the Group X zone and drag and drop it into the Wrap zone.
36
Graph Builder
Example Using Graph Builder
Chapter 3
Essential Graphing
Figure 3.4 Example of Calories Versus Total fat g by Cholesterol g in the Wrap Zone
A scatterplot of Calories versus Total fat g is created for every level of Cholesterol g.
You can see that some of the cells have very little data; other cells have a lot of data.
Among the cells that have a lot of data (cholesterol equals 0, 5, 10), there is still
considerable variation in calories. So you decide to remove Cholesterol g.
7. Remove Cholesterol g by right-clicking on the Cholesterol g label in the Group X zone and
selecting Remove.
‒ Determine whether carbohydrates has any effect.
8. Drag and drop Carbohydrate g into the Wrap zone.
Chapter 3
Essential Graphing
Graph Builder
Example Using Graph Builder
37
Figure 3.5 Example of Calories Versus Total fat g by Carbohydrate g
Carbohydrate g is a continuous variable with many values, so Graph Builder uses the
percentiles to create five ranges of carbohydrate g levels. About the same number of points
are displayed in each group. You can see that the relationship between calories and fat is
relatively strong for each level of carbohydrate. It appears that carbohydrates adds
additional predictive ability.
Now that you have determined that carbohydrates have a significant impact on calories,
combine the five scatterplots into one scatterplot to directly compare the lines. You still
want to identify the carbohydrate levels.
9. Drag and drop the Carbohydrate g label from the Group X zone to the Overlay zone.
38
Graph Builder
Example Using Graph Builder
Chapter 3
Essential Graphing
Figure 3.6 Example of Carbohydrates Overlay
The scatterplots combine into one, and the carbohydrate levels are individually colored.
Modify the legend title.
10. Right-click on the legend title (Carbohydrate g) and select Legend Settings.
11. Rename the Title to Carbohydrate grams.
12. Click OK.
Note: For details about making changes to the legend, see “Modify the Legend” on
page 52.
13. Now that you are satisfied with this graph, click Done.
Chapter 3
Essential Graphing
Graph Builder
Example Using Graph Builder
39
Figure 3.7 Example of a Completed Graph
You now have a presentation-friendly graph that you can copy and paste outside of JMP.
To copy the entire graph:
14. Click the Selection Tool.
15. Click anywhere on the Graph Builder title bar.
The entire area is highlighted and ready to copy.
Launch Graph Builder
Launch Graph Builder by selecting Graph > Graph Builder.
40
Graph Builder
Example Using Graph Builder
Chapter 3
Essential Graphing
Figure 3.8 The Graph Builder Window
Variables
list
Buttons
Element type icons
Element
properties
panel
Graph area and zones
Note: Any rows that are excluded in the data table are also hidden in the Graph Builder.
The Graph Builder window contains the following components:
•
Graph area and zones. See “The Graph Area and Zones” on page 40.
•
Element properties panel. Options vary depending upon the selected element type. See
“Right-Click Menu for a Graph” on page 45.
•
Variables list, populated with the columns from the open data table. For details about the
options in the red triangle menu, see the Using JMP book.
•
Buttons. See “Buttons” on page 43.
•
Element type icons. See “Element Type Icons” on page 42.
The Graph Area and Zones
The primary element in the Graph Builder window is the graph area. The graph area contains
drop zones, and you can drag and drop variables from the Select Columns box into the zones.
The following table describes the Graph Builder drop zones.
Chapter 3
Essential Graphing
Graph Builder
Example Using Graph Builder
X, Y
Drop variables here to assign them the X or Y role.
Group X
Subsets or partitions the data based on the variable or variables that
you select. Displays the variable horizontally. Once a variable is
placed here, no variable can be placed in Wrap.
Group Y
Subsets or partitions the data based on the variable or variables that
you select. Displays the variable vertically.
Map Shape
Drop variables here to create map shapes. See “Create Map Shapes”
on page 53. If you have a variable in the Map Shape zone, the X and Y
zones disappear.
Wrap
Subsets or partitions the data based on the variable or variables that
you select. Wraps the data horizontally and vertically. Once a variable
is placed here, no variable can be placed in Group X.
Freq
Drop a variable here to use it as a frequency or weight for graph
elements that use statistics, such as mean or counts.
Overlay
Groups the Y variables by the selected variable, overlays the
responses, and marks the levels with different colors.
Color
Drop variables here to color the graph:
•
If you are using a map, the map shapes are colored. See “Change
Map Colors and Transparency” on page 254 in the “Create Maps”
chapter.
•
If you are using a contour plot, colored contours appear.
•
If your graph contains points, they are colored.
Tip: You can show or hide color using the Variables menu in the
element properties panel.
Size
(Use with Map Shapes) Scales map shapes according to the size
variable, minimizing distortion.
Page
Drop the By group variable to the Page zone to display each level of
the group on a separate graph.
Legend
Shows descriptions of graph elements. If you attempt to drop a
variable here, the variable defaults to Overlay. See “Modify the
Legend” on page 52.
41
42
Graph Builder
Example Using Graph Builder
Chapter 3
Essential Graphing
If you drop variables into the center area, JMP guesses the drop zone to put them into, based
on whether the variables are continuous, ordinal or nominal.
•
The X, Y, and Map Shape zones are positional, and influence the types of graph elements
that are available.
•
The Group X, Group Y, Wrap, and Overlay zones partition the data into subsets and lay out
multiple graphs by either dividing the graph space or by overlaying the graphs.
•
The Color and Freq zones modify certain graph elements.
Related Information
For the X, Y, Group X, and Group Y zones, see also:
•
“Add Variables” on page 49
•
“Change Variable Roles” on page 49
•
“Remove Variables” on page 50
•
“Add Multiple Variables to Grouping Zones” on page 52
Element Type Icons
You can change the element type by clicking on an element type icon. Use the SHIFT key to
apply multiple elements at once. Once you select an element, only compatible elements are
active.
Choose from the following element types:
The Points element shows data values.
The Smoother element shows a smooth curve through the data. The smoother is a
cubic spline with a default lambda of 0.05 and standardized X values. You can
change the value of lambda using the slider.
The Line of Fit element shows a linear regression with confidence intervals.
The Ellipse element shows a bivariate normal density ellipse.
The Contour element shows regions of density or value contours. If you specify
only one continuous variable for X or Y, a violin plot appears instead of a contour
plot.
The Line element shows a response summarized by categories.
The Bar element shows a response summarized by categories.
The Area element shows a response summarized by categories.
Chapter 3
Essential Graphing
Graph Builder
Example Using Graph Builder
43
The Box Plot element shows a compact view of a variable’s distribution, with
quartiles and outliers.
The Histogram element shows a variable’s distribution using binning. If you
specify the same variable for X and Y, then the Y role is ignored and a single
histogram appears.
Tip: To overlay histograms transparently, assign a Y variable and an Overlay
variable. Then, click the Histogram element icon.
The Heatmap element shows counts using color for X and Y categories.
Tip: Hover over a cell to see tool tips that show labels.
The Pie element shows portions of a whole.
The Treemap element shows a response summarized by categories.
The Mosaic element shows counts using size for X and Y categories.
The Caption Box element shows a summary statistic value for the data.
The Formula element shows a function defined by a column formula.
The Map Shapes element creates a map on the graph.
When applicable, properties for each element appear and can be adjusted in the Graph
Builder window. For more information, see “Right-Click Menu for a Graph” on page 45.
Buttons
There are three buttons on the Graph Builder window:
•
Undo reverses the last change made to the window.
•
Recall populates the Graph Builder window with the last graph that you created. The
Recall button becomes the Undo button once you perform an action.
•
Start Over returns the window to the default condition, removing all data and graph
elements from the window, and all variables from the drop zones.
•
Dialog opens the Graph Builder launch window. After you click it, the Dialog button
becomes the Start Over button.
•
Done hides the buttons and Select Columns box and removes all drop zone outlines. In
this presentation-friendly format, you can copy the graph to other programs. To copy the
graph, select Edit > Copy. To restore the window to the interactive mode, click Show
Control Panel on the Graph Builder red triangle menu.
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Graph Builder Options
The red triangle menu for Graph Builder contains these options:
•
Show Control Panel shows or hides the platform buttons, the Select Columns box, and the
drop zone borders.
•
Show Legend shows or hides the legend.
•
Legend Position sets the position of the legend to appear on the right or on the bottom. The
legend appears on the right by default. Putting the legend at the bottom places it in the
center below the graph. The legend items then appear horizontally instead of vertically.
•
Continuous Color Theme select the color theme that will be used for continuous variables.
•
Categorical Color Theme select the color theme that will be used for categorical variables.
For more information about color themes, see the Using JMP book.
•
Show Footer shows or hides the footer, which contains informative messages such as
missing map shapes, error bar notes, freq notes, and WHERE clauses.
•
Lock Scales prevents axis scales and gradient legend scales from automatically adjusting
in response to data or filtering changes.
•
Auto Stretching enables multiple graphs in the same window to be sized properly.
•
Sampling uses a random sample of the data to speed up graph drawing. If the sample size
is zero, or greater than or equal to the number of rows in the data table, then sampling is
turned off.
•
Include Missing Categories enables a graph to collect and display missing values for
categorical variables.
•
Launch Analysis launches the Fit Model platform with the variables on the graph placed
into roles. It launches the Distribution platform when only one variable is placed.
•
Make into Data Table creates a new data table that contains the results from the graph.
•
Script contains options that are available to all platforms. They enable you to redo the
analysis or save the JSL commands for the analysis to a window or a file. For more
information, see Using JMP.
Graph Builder Right-Click Menus
Graph Builder contains various right-click menus, depending on the area you right-click on.
Any changes that you make to a graph element apply to all graphs for that variable, across all
grouping variables.
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Graph Builder Options
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Right-Click Menu for a Graph
Right-clicking on a graph shows a menu of the available graph elements and other options.
(These options are also appear in the element properties panel, below the Variables panel.)
The first menus that appear reflect the elements that you have selected. For example, if you
have selected the Points and Line of Fit icons, the first menus are Points and Line of Fit. Each
of these elements have specific submenus. The following table describes the right-click menu
options and shows which graph elements each option is applicable to.
Note: You can drag and drop an image file to the background of a graph in Graph Builder as
described in the Using JMP book. After adding the image, the standard image options can be
used to format the image.
Note: For a description of the Rows, Graph, Customize, and Edit menus, see the Using JMP
book.
Option
Graph Element
Description
Add
All graph
elements
Add an element to the graph.
Area Style
Area
Changes the area style.
Bar Style
Bar
Changes the bar style.
Box Style
Box Plot
Changes the box plot style.
Change to
All graph
elements
Change the existing element to another element.
Confidence of Fit
Line of Fit
Shows confidence lines for the fit.
Confidence of
Prediction
Line of Fit
Shows confidence lines for the prediction.
Coverage
Ellipse
Change the percentage of the ellipse coverage.
Degree
Line of Fit
Change the degree of the line.
Error Bars
Line, Bar, and
Points
Adds error bars to the graph.
Equation
Line of Fit
Adds the regression equation for the line of fit.
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Option
Graph Element
Description
Response Axis
Mosaic,
Histogram, Box
Plot, Line, Bar,
Area
Changes the primary direction of the graph to X
(horizontal), Y (vertical), or Auto (the Graph
Builder default setting).
Jitter
Box Plot and
Points
Turns jitter on or off.
Label
Bar, Pie
Adds labels to the bar or pie chart.
Move Backward
All graph
elements
If you have multiple graph elements, you can
move them backward (or move them to the
back).
Move Forward
All graph
elements
If you have multiple graph elements, you can
move them forward (or move them to the front).
Number of Levels
Contour
Specify the number of contour levels to display.
Outliers
Box Plot
Shows or hides outliers.
Pie Style
Pie
Changes the style of the pie chart.
Remove
All graph
elements
Removes the graph element.
Root Mean Square
Error
Line of Fit
Adds the root mean square error to the graph.
Row order
Line
Connects line points in the order of their row
numbers instead of the order of their X values.
R2
Line of Fit
Adds the R square to the graph.
Show Missing
Shapes
Map Shapes
Shows or hides missing data from the map. This
option is turned off by default.
Summary Statistic
Line, Bar, Points,
Area, Pie,
Treemap, Caption
Box, Map Shapes
Provides options for changing the statistic being
plotted.
Vertical
Mosaic,
Histogram
Changes the primary direction of the mosaic plot
to vertical.
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Graph Builder Options
Option
Graph Element
Description
X
All graph
elements
Hides or shows each X variable. (In the
right-click menu, this option only appears if you
have multiple X variables.)
This option is represented in the element
properties panel using the Variables menu.
X Position
Caption box
Changes the horizontal position of the caption.
Y
All graph
elements
Hides or shows each Y variable. (In the
right-click menu, this option only appears if you
have multiple Y variables.)
This option is represented in the element
properties panel using the Variables menu.
Y Position
Caption box
Changes the vertical position of the caption.
Additional Right-Click Menus
Depending on the area that you click on in Graph Builder, you see different right-click
options.
Right-Click Area
Description
The Y or X axis
The Remove and Swap commands appear. See
“Remove Variables” on page 50 or “Change Variable
Roles” on page 49.
For descriptions of the options below the line, see the
Using JMP book.
A variable label
The Remove and Swap commands appear. See
“Remove Variables” on page 50 or “Change Variable
Roles” on page 49.
For descriptions of the options below the line, see the
Using JMP book.
Graph Spacing Line
Use the Graph Spacing Color, Graph Spacing
Transparency, Graph Spacing, and Graph Borders
commands to change the respective properties of the
line separating two graphs.
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Right-Click Area
Description
A zone
These options might appear:
Levels in View Changes the number of levels of the
grouping variable that are visible. Use the arrow
buttons to scroll forwards and backwards.
Number of Levels changes the number of levels. See
“Separate Variables into Groups” on page 49.
Show Title Shows or hides the variable title.
changes the orientation of the variable
text to horizontal or vertical.
Title Orientation
Level Orientation changes the orientation of the level
values to horizontal or vertical.
Levels per Row (use with a Wrap variable) changes the
number of columns included in the graph.
Color changes the background color of the grouping
zone.
X or Y Group Edge moves the grouping variable labels.
See “Move Grouping Variable Labels” on page 49.
swaps the position of two variables. See
“Change Variable Roles” on page 49.
Swap
removes a variable. See “Remove Variables”
on page 50.
Remove
The legend title
See “Modify the Legend” on page 52.
An item in the legend
For descriptions of these options, see the Using JMP
book.
The empty space below
Variables
See “Graph Builder Options” on page 44.
The empty space below the
legend or above or below the
graph
For descriptions of these options, see the Using JMP
book.
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Add Variables
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Add Variables
To add a variable to a drop zone, click and drag the column name from the Variables box and
drop it into the desired drop zone. Alternatively, you can highlight the column name in the
Variables box and click in the desired drop zone. Both numeric and categorical variables can
be added to all of the drop zones.
Related Information
•
“Example of Adding Variables” on page 53
Move Grouping Variable Labels
Grouping variable labels can be relocated to another position on the graph. The Group X labels
can be either on the top or the bottom of the graph. The Group Y labels can be either on the
right or the left of the graph.
•
To relocate a Group X label, right-click on the variable in the Group X zone and select X
Group Edge > Top or Bottom.
•
To relocate a Group Y label, right-click on the variable in the Group Y zone and select Y
Group Edge > Left or Right.
Separate Variables into Groups
When you add a categorical variable to the Group X or Group Y zone, a partition is created for
each level of the variable.
When you add a continuous variable to a grouping zone, Graph Builder uses quantiles of the
data to divide the variable into five groups. Once the variable is added to the display, you can
change the number of groups as follows:
1. Right-click on the grouping variable label and select Number of Levels.
2. Type in the number of levels that you want to display.
3. Click OK.
Change Variable Roles
To change variable roles, perform one of the following methods:
•
Drag and drop a variable from one zone to another.
•
Right-click on a variable in a zone and select Swap. Select the variable that you want to
switch with.
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Remove Variables
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Remove Variables
To remove a variable, perform one of the following methods:
•
Drag and drop a variable into blank space.
•
Right-click on a variable in a zone and select Remove.
Add Multiple Variables to the X or Y Zone
You can assign more than one variable to the same drop zone. By visualizing multiple Y
variables across multiple X variables, you can discover multivariate relationships and
interactions.
To add a variable to the left or right of the existing X variable, drag and drop the variable into
the X zone. Use the same method for adding a variable to the top or bottom of the existing Y
variable.
When you click and drag a variable, a blue shape appears, symbolizing where the variable is
added.
Related Information
•
“Example of Adding Multiple Variables to the X or Y Zone” on page 56
Merge Variables
Merging variables places both variables on the same axis and creates a graph for both
variables. Merging is similar to adding variables, in that a graph element is added for both
variables. But adding variables maintain separate axes and scales, and merging variables use
the same axis and the same scale for the variables.
If you have no variables in the X or Y zone, you can drag multiple variables from the Variables
list into the X or Y zone. This merges the selected variables.
If you have existing variables in the X or Y zone where you want to add variables, note the
following:
•
If you merge a categorical variable with an existing continuous variable, then the
categorical variable is transformed into integer values. For example, using the
Students.jmp data, if sex is merged with height, the values of sex (F and M) are
transformed into 0 and 1. The transformation allows the two variables to use the same axis
and scale.
•
If you merge a numeric variable with an existing categorical variable, the result is ordered.
See “Order Variables” on page 51.
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Merge Variables
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Related Information
•
“Example of Merging Variables” on page 57
Order Variables
The levels of a nominal or ordinal variable on a graph are ordered alphabetically by default.
There are two ways that you can modify this order:
•
Use the Value Ordering column property. See the Using JMP book.
•
Use a second variable. See “Use a Second Variable” on page 51.
Use a Second Variable
A nominal or ordinal variable can be ordered only by a numeric variable. The variable that
does the ordering has to be numeric so that an average can be computed for each level of the
categorical variable.
Note: If you try to order a numeric variable with another numeric variable, JMP defaults to a
merge. See “Merge Variables” on page 50.
•
To order by a second variable, drag the second (numeric) variable into the graph area next
to the existing (nominal or ordinal) variable.
•
To change the order from ascending to descending, right-click on the variable and deselect
Ascending.
•
The default ordering statistic is the mean. To use another statistic, right-click on the
variable and select Order Statistic. The available statistics are N, Mean, Median, Min, Max,
Range, Sum, and % of Total.
•
To remove the ordering, right-click and select Remove Order.
Related Information
•
“Example of Ordering Variables Using a Second Variable” on page 58
Replace Variables
You can replace an existing variable with an incoming variable, maintaining a single graph
element. If grouping variables exist, a single graph element is maintained for the incoming
variable for each combination of grouping variables.
To replace an existing variable, drag and drop the variable from the Select Columns box atop
the existing variable. Before you drop the variable, a hexagon appears.
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Create a Second Y Axis
If you have two Y variables, you can move one of the variables to create a second Y axis.
Right-click on the Y variables and select Move Right. Select the variable that you want to move.
Add Multiple Variables to Grouping Zones
You can add more than one variable to the Group X or Group Y zones. You place the incoming
variable below or above the existing variable, depending on how you want your data to be
ordered.
To add an additional variable to a grouping zone, simply drag and drop the variable into the
drop zone.
When you drag a variable, a blue shape appears, symbolizing where the variable is added.
•
“Example of Adding Multiple Variables to Grouping Zones” on page 59
•
“Example of Replacing Variables” on page 61
Modify the Legend
To modify a legend, right-click or double-click on the legend title. The following commands
appear:
•
Legend Settings opens a window where you can modify legend settings, such as the title
and title position. See Figure 3.9.
•
Revert Legend returns the legend to the default condition (if you have changed it).
Figure 3.9 The Legend Settings Window
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Create Map Shapes
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Table 3.1 Description of the Legend Settings Window
Title
The name of the legend.
Check boxes
Shows or hides items in the legend.
Up and down
arrows
Changes the order of items in the legend.
Set Color Theme
Select a different color theme.
Title Position
Places the legend title on top or to the left of the items in the
legend.
Item Direction
Displays the legend horizontally or vertically.
Item Wrap
Sets the legend to be n items tall (if vertical) or n items wide (if
horizontal).
Preview
Shows your changes to the legend.
OK
Commits your changes to the legend.
Cancel
Cancels your changes to the legend.
Help
Opens the online Help.
Create Map Shapes
Use Graph Builder to create maps using the Map Shape zone. When a column contains the
names of geographical regions (such as countries, states, provinces, counties), you can assign
the column to the Map Shape zone. This creates a map of the regions.
See “Map Shape” on page 235 in the “Create Maps” chapter for details about creating map
shapes.
Additional Examples Using Graph Builder
This section contains examples that further illustrate how to use Graph Builder.
Example of Adding Variables
To show an example of adding variables to Graph Builder, make a scatterplot of weight versus
height for males and females.
1. Select Help > Sample Data Library and open Students.jmp.
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2. Select Graph > Graph Builder.
3. Click height and drag and drop it into the X zone.
Figure 3.10 Example of a height Plot
A plot of the raw data appears. Random jitter is added in the Y direction.
4. Click weight and drag and drop it into the Y zone.
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Additional Examples Using Graph Builder
Figure 3.11 Example of a Scatterplot of weight Versus height
Notice the following additional elements:
‒ A smoother (line) shows the general pattern of weight as a function of height.
‒ A legend describes the elements on the graph (in this case, the smoother).
5. Click sex and drag and drop it into the Group X zone.
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Figure 3.12 Example of a Scatterplot for Each Level of the sex Variable
grouping variable labels
Side-by-side scatterplots (one for each level of sex) replace the initial scatterplot. You now see
weight versus height for males and females.
Example of Adding Multiple Variables to the X or Y Zone
Start from the graph in Figure 3.10. Add the weight variable to the left of the height variable.
Click weight and drag and drop it into the X axis, to the left of height.
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Figure 3.13 Dragging and Dropping the weight Variable
A graph is created for both variables, with separate axes and scales. If grouping variables
exist, a graph is created for both the existing variable and incoming variable for each
combination of grouping variables.
Example of Merging Variables
To demonstrate combining two continuous variables, start from the graph in Figure 3.10.
Merge the weight variable with the height variable. Drag and drop weight to the center of the X
zone, slightly above the axis. Before you drop the variable, a blue pentagon appears.
Figure 3.14 Merging height and weight
A graph element is added for weight that uses the same scale as height.
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Figure 3.15 Example of weight Combined with height
Example of Ordering Variables Using a Second Variable
To order a nominal or ordinal variable by a numeric variable, consider data about the sizes of
cars. You want to see the different car sizes in ascending order.
1. Select Help > Sample Data Library and open Cars.jmp.
2. Select Graph > Graph Builder.
3. Click Size and drag it into the X zone.
This variable represents the size of the vehicle. Eight levels are listed alphabetically on the
X axis: compact (comp), heavy (hev), lightweight (lt), medium (med), mini, multi-purpose
(mpv), pick-up truck (pu), and van. Since the levels are listed alphabetically, they are not
ordered by size. Heavy comes before mini and lightweight. Another variable, Wt (weight)
can act as a good substitute for size.
4. Click Wt and drag and drop it just above the X axis. Before you drop the variable, a blue
pentagon appears.
Figure 3.16 Example of Merging Wt and Size
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The levels of Size are now ordered according to the average Wt of all vehicles in that level, in
ascending order. Notice that mini and lightweight are now ordered before heavy. The axis
label is updated, signifying that an ordering variable is in use.
To verify that Size is actually ordered by Wt, click on Wt under Variables and drag and drop it
into the Y zone. Figure 3.17 shows that the average Wt increases from the left to right.
Figure 3.17 Example of Size Ordered by Wt
To change the order from ascending to descending, proceed as follows:
5. Right-click in the X zone.
6. Deselect Ascending.
The default ordering statistic is the mean. To use another statistic, select it from the Order
Statistic menu. The available statistics are N, Mean, Median, Min, Max, Range, Sum, and
% of Total.
To remove the ordering, right-click and select Remove Order.
Note: For details about the Axis and Edit menu options, see the Using JMP book.
Example of Adding Multiple Variables to Grouping Zones
You have data on popcorn. You want to see the popcorn yields, grouped by popcorn type
(gourmet or plain) and by batch size (small or large).
1. Select Help > Sample Data Library and open Popcorn.jmp.
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2. Select Graph > Graph Builder.
3. Click popcorn and drag and drop it into Group X.
This is the popcorn type.
4. Click yield and drag and drop it into the Y zone.
You can see the yield by popcorn type.
Figure 3.18 Example of yield by popcorn
To add the second variable, perform either step 5 or step 6, depending on where you want
to place the incoming variable (above or below the existing variable).
5. To place the batch variable above the popcorn variable, drag and drop batch into the left
side of the Group X zone. Before you drop the variable, a left-justified blue polygon
appears.
Figure 3.19 Example of Adding batch Above popcorn
6. To place the batch variable below the popcorn variable, drag and drop batch into the right
side of the Group X zone. Before you drop the variable, a right-justified blue polygon
appears.
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Figure 3.20 Example of Adding batch Below popcorn
7. To see the data shape more clearly, right-click in the graph and select Add > Box Plot.
Figure 3.21 Examples of Popcorn yield Grouped by popcorn and batch
Depending on where you place the incoming variable, you can look at the data in different
ways. But you could come to the same conclusion: that batch size varies for gourmet popcorn,
but batch size does not vary as much for plain popcorn.
Example of Replacing Variables
You can replace an existing variable with an incoming variable. To demonstrate replacing
variables, start from the graph in Figure 3.18. Replace popcorn with batch in the Group X zone.
Drag and drop batch into the center of the Group X zone. Before you drop the variable, a blue
quadrilateral appears.
Figure 3.22 Example of Replacing popcorn with batch
Once you drop the variable, groups are created for batch instead of popcorn.
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Example of Overlaying Histograms
You can overlay one histogram over another to display multiple at once.
1. Select Help > Sample Data Library and open Big Class.jmp.
2. Select Graph > Graph Builder.
3. Drag and drop height into the Y zone.
4. Drag and drop sex into the Overlay zone.
5. Click the Histogram element icon.
Figure 3.23 Example of Height by Sex
The histogram that represents the heights of the males overlays the histogram that represents
the heights of the females.
Measure Global Oil Consumption and Production
You have data about oil consumption and production, measured in barrels per day, for
selected countries. You want to find out which countries consume the most oil, and which
countries produce the most oil.
1. Select Help > Sample Data Library and open Oil Use.jmp.
2. Select Graph > Graph Builder.
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3. Drag and drop Country into the Y zone. You can resize the Graph Builder window and the
graph if necessary.
Note: Notice that the default ordering for Country is ascending alphabetical (starting point
is at the bottom). You can change the sorting order within a data table by using either the
Value Ordering or Row Order Levels commands. For details, see the Using JMP book.
Figure 3.24 Example of Country Assigned to the Y Zone
4. Drag and drop Production and Consumption to the X zone.
Markers appear on the graph for both variables, along with a legend that identifies the
different colors.
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Figure 3.25 Example of Country versus Production and Consumption
Because the default graph element is set to points, it is difficult to read and learn from the
graph. Change the points to bars to make the graph easier to interpret.
5. To change the points to bars, right-click on the graph and select Points > Change to > Bar.
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Additional Examples Using Graph Builder
Figure 3.26 Example of Side-by-Side Bars for Production and Consumption
Experiment with the presentation of the bar chart.
6. Change the default side-by-side bars to stacked bars by right-clicking on the graph and
selecting Bar > Bar Style > Stacked.
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Figure 3.27 Example of Stacked Bars for Production and Consumption
This still is not exactly what you want. It would be clearer to see the production and
consumption bars going in different directions.
7. Go back to the Oil Use.jmp sample data table.
8. Double-click on an empty column heading to create a new column.
9. Double-click on the new column title, and rename it Negative Consumption. Click OK.
10. With the new column highlighted, select Cols > Formula.
11. Click Consumption.
12. Click +/-.
13. Click OK.
The new Negative Consumption column contains the negative consumption values. Use
this column to see the consumption bar going in the opposite direction from the
production bar. Launch Graph Builder and create the graph again.
14. Select Graph > Graph Builder.
15. Drag and drop Country into the Y zone. You can resize the Graph Builder window and the
graph if necessary.
16. Drag and drop Production and Negative Consumption to the X zone.
17. Right-click on the graph and select Points > Change to > Bar.
18. Right-click on the graph and select Bar > Bar Style > Stacked.
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Figure 3.28 Example of Stacked Bars for Production and Negative Consumption
You can clearly see consumption on the left in red, and production on the right in blue.
Sort the countries by their oil consumption. You can use the Consumption column as an
ordering variable.
19. Drag and drop Consumption into the graph, to the right of the Y zone.
Before you drop the variable, a blue polygon appears.
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Figure 3.29 Example of Dragging Consumption into the Y Axis
The Country variables are now ordered by Consumption.
Figure 3.30 Example of Country Organized by Consumption
You can clearly see the countries that consume the most oil at the top of the graph. You can
also see each country’s oil production.
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Analyze Popcorn Yield
You have data about popcorn. The data includes popcorn type (plain or gourmet), how much
oil was used, the batch size (small or large), the yield, and the trial number. You want to
determine how these factors affect the popcorn yield: popcorn type, oil, and batch size.
Note: These data are artificial, but inspired from an experiment reported in Box, Hunter, and
Hunter (1978).
1. Select Help > Sample Data Library and open Popcorn.jmp.
2. Run the attached Fit Model script attached to the data table by clicking on the red triangle
next to Fit Model and select Run Script.
A three-way ANOVA model is fit to the data.
3. Click on the disclosure icon next to Effect Tests to open the report.
The popcorn*batch interaction has a small p-value (0.0026). From this, you conclude that
there is a significant interaction between popcorn and batch.
Figure 3.31 Example of Effect Tests Output
4. Save the model predictions to the data table. From the red triangle menu next to Response
yield, select Save Columns > Prediction Formula.
Notice that a new column is added to the data table, Pred Formula yield.
5. Save the prediction intervals to the data table. From the red triangle menu next to
Response yield, select Save Columns > Mean Confidence Interval.
Notice that two new columns are added to the data table: Lower 95% Mean yield and
Upper 95% Mean yield.
Now you can use Graph Builder to visualize the interaction between popcorn and batch.
6. Select Graph > Graph Builder.
7. Drag and drop these columns into the Y zone:
‒ Pred Formula yield
‒ Lower 95% Mean yield
‒ Upper 95% Mean yield
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8. Drag and drop popcorn into the X zone.
9. Drag and drop batch into the Group X zone.
10. Drag and drop oil amt into the Group Y zone.
11. Because the Jitter option is turned on, there are two data points per combination of factors,
and the prediction at a given combination is the same. Turn off the jitter by right-clicking
in the graph and deselecting Points > Jitter.
Figure 3.32 Example of Three Responses versus Three Factors
Format the graph to see interval bars for Lower 95% Mean yield and Upper 95% Mean yield,
and to see points for Pred Formula yield.
12. Right-click on the graph and select Add > Bar.
This adds bars for all three responses.
13. Change the bar style to interval by right-clicking on the graph and selecting Bar > Bar Style
> Interval.
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Figure 3.33 The Interval Bar Style
The interval bar style currently spans from Lower 95% Mean yield to Pred Formula yield, but
you want it to span up to Upper 95% Mean yield.
14. Remove the bar element for Pred Formula yield by right-clicking on the graph and
deselecting Bar > Y > Pred Formula yield.
Now the confidence interval spans from the lower to upper value.
Figure 3.34 Example of Correct Interval Span
Remove the point graph element for Lower 95% Mean yield and Upper 95% Mean yield.
15. Right-click on the graph and select Points > Y, and deselect the Lower 95% Mean yield and
Upper 95% Mean yield individually.
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To make the points easier to see, increase the size of the points.
16. Right-click on the graph, and select the XXL option under Graph > Marker Size. Do this for
each quadrant of the graph.
Note: You can also change the graph title and labels for the X and Y zones. Click on the
label and type in the new text.
Figure 3.35 Example of Predicted Values and Confidence Intervals
From Figure 3.35, you can see the following relationships:
•
For large batches, there is no difference between plain and gourmet popcorn.
•
For small batches, the gourmet popcorn has a higher yield than the plain popcorn.
•
For the oil amount, the relationship is the same whether the oil amount is little or lots, so
there is no three-way interaction.
Examine Diamond Characteristics
You have data about diamonds, including their carat weight and price. Examine the
relationship between carat weight and price.
1. Select Help > Sample Data Library and open Diamonds Data.jmp.
2. Select Graph > Graph Builder.
3. Drag and drop Price into the Y zone.
4. Drag and drop Carat Weight into the X zone.
Chapter 3
Essential Graphing
Graph Builder
Additional Examples Using Graph Builder
Figure 3.36 Example of Points Showing Diamond Characteristics
You can see that the points are difficult to interpret. Some points overlap, making the
density unclear.
Change the points to a contour plot.
5. Right-click the plot and select Points > Change to > Contour.
73
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Graph Builder
Additional Examples Using Graph Builder
Chapter 3
Essential Graphing
Figure 3.37 Example of Contour Plot of Diamond Characteristics
The darker the density, the more data. In Figure 3.37, notice the following:
•
Most people in the sample purchased diamonds with a carat weight of about 0.5, 0.75, and
1.0.
•
Most people who purchased diamonds with a carat weight of 0.5 paid about $850-$2,000
dollars.
•
Most people who purchased diamonds with a carat weight of 0.75 paid about
$1,600-$2,700 dollars.
•
Most people who purchased diamonds with a carat weight of 1.0 paid about $3,800-$4,800
dollars.
Chapter 4
Overlay Plots
Plot Several Numeric Y Variables against One X Variable
The Overlay Plot command in the Graph menu produces plots of a single X column and one or
more numeric Ys and does not accept non-numeric values for the y-axis. Curves can also be
shown as separate plots for each Y with a common x-axis. Plots can be modified with range
and needle options, color, log axes, and grid lines. Curves with two different scales can be
overlaid on the same plot with the addition of a right axis.
Figure 4.1 Examples of Overlay Plot Graphs
Contents
Example of an Overlay Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Launch the Overlay Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
The Overlay Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Overlay Plot Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
General Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Y Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Additional Examples of the Overlay Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Function Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Plotting Two or More Variables with a Second Y-axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Chapter 4
Essential Graphing
Overlay Plots
Example of an Overlay Plot
77
Example of an Overlay Plot
This example shows you how to plot two variables on a single y-axis.
1. Select Help > Sample Data Library and open Spring.jmp.
The table has a row for each day in the month of April. The column named April is the
numeric day of the month, and the remaining columns are various weather statistics.
2. Select the Overlay Plot command from the Graph menu.
3. Select Humid1:PM and Humid4:PM and click Y.
These two columns are the humidity measurements at 1:00 p.m. and 4:00 p.m.
4. Select April and click X.
5. Click OK.
The plot shown in Figure 4.2 appears. Initially, this platform overlays all specified Y
columns. The legend below the plot shows individual markers and colors that identify
each Y column.
Figure 4.2 Plot with Legend
To help you quickly differentiate between the Ys, select Y Options > Connect Points from the
Overlay Plot red triangle menu. Adjacent points are connected for each Y variable, as shown
in Figure 4.3.
78
Overlay Plots
Example of an Overlay Plot
Figure 4.3 Plot with Connected Points
Launch the Overlay Plot Platform
Launch Overlay Plot by selecting Graph > Overlay Plot.
Figure 4.4 The Overlay Plot Launch Window
In the Overlay Plot Launch window, you assign the following:
•
one X variable of any modeling type
•
as many numeric Y variables as you want
Table 4.1 Description of the Overlay Plot Launch Window
Cast Selected Columns Into Roles:
Chapter 4
Essential Graphing
Chapter 4
Essential Graphing
Overlay Plots
The Overlay Plot
79
Table 4.1 Description of the Overlay Plot Launch Window (Continued)
X, Y
You can graph many numeric Y variables against a single X
variable.
Left Scale/Right Scale
The columns assigned to the Y role have a left- or right-pointing
arrow to the left of the column name. This arrow designates on
which vertical axis (on the left or right of the plot) the variable
appears. Change the designation by highlighting the column in
the Y list and clicking the Left Scale/Right Scale button.
Grouping
This option produces a matrix of graphs for each Grouping
variable.
By
This option produces a separate graph for each level of the By
variable. If two By variables are assigned, a separate graph for
each possible combination of the levels of both By variables is
produced.
Options:
Sort X
This option causes the points to be connected in order of
ascending X values. Otherwise, the points are connected in row
order. This option is selected by default.
X Log Scale
This option applies a log scale to the x-axis.
Left Y Log Scale
This option applies a log scale to the left y-axis. It is available
only if one or more Y variables are left-scaled. (See Left Scale/
Right Scale in this table.)
Right Y Log Scale
This option applies a log scale to the right y-axis. It is available
only if one or more Y variables are right-scaled. (See Left Scale/
Right Scale in this table.)
For more information about the launch window, see Using JMP.
After you click OK, the Overlay plot appears. See “The Overlay Plot” on page 79.
The Overlay Plot
Follow the instructions in “Example of an Overlay Plot” on page 77 to produce the plot shown
in Figure 4.5.
Initially, this platform overlays all specified Y columns. The legend below the plot shows
individual markers and colors that identify each Y column. For information about additional
options for the plot, see “Overlay Plot Options” on page 80.
80
Overlay Plots
Overlay Plot Options
Chapter 4
Essential Graphing
Figure 4.5 The Overlay Plot
Overlay Plot Options
The basic Overlay Plot is shown in Figure 4.5.
The Overlay Plot platform has plotting options accessed from the red triangle menu on the
Overlay Plot title bar. When you select one of these options at the platform level, it affects all
plots in the report if no legend levels are highlighted. If one or more plot legend levels are
highlighted, the options affect only those plots. There is also a single-plot options menu for
each Y variable, which appears when you highlight a Y variable legend beneath the plot and
right-click.
The individual plot options are the same as those in the Y Options submenu at the platform
level. See “Y Options” on page 83.
General Platform Options
When you select one of these options at the platform level, it affects all plots in the report if no
legend levels are highlighted. If one or more plot legend levels are highlighted, the options
affect only those plots.
Chapter 4
Essential Graphing
Overlay Plots
Overlay Plot Options
81
Table 4.2 Descriptions of Overlay Plot Platform Options
Overlay Plots
Contains options for overlaying:
Overlay Y’s Overlays all variables assigned to the Y role on one
plot. This option is on by default and unavailable if only one Y
variable is assigned.
Overlays groups and produces a legend. This
option is off by default and unavailable if no grouping
variables are assigned. See “Overlay Groups” on page 82.
Overlay Groups
No Overlay Turns off overlaying for both Ys and groups. Creates
a separate plot for each Y and each group. This option is off by
default unless only one Y variable is assigned and no
grouping variables are assigned. In this case, no overlaying
options are available.
Separate Axes
Assigns each plot its own set of xy-axes. If Separate Axes is off,
the vertical axis is shared across the same row of plots and the
horizontal axis is shared on the same column of plots. The default
setting is on (except when multiple plots exist). See “Separate
Axes” on page 82.
Uniform Y Scale
Uses the same Y scale for all grouped plots. The default setting is
off.
Connect Thru Missing
Connects adjacent points in the plot, regardless of missing values.
The default setting is off.
Range Plot
Connects the lowest and highest points at each x value with a line
with bars at each end. The Needle and Range Plot options are
mutually exclusive.
Y Options
Contains options for the Y variables. See “Y Options” on page 83.
Ungroup Plots
Creates a separate chart for each level of a grouping variable.
Arrange Plots
Enables you to specify the number of plots in each row.
Script
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands
for the analysis to a window or a file. For more information, see
Using JMP.
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Overlay Plots
Overlay Plot Options
Chapter 4
Essential Graphing
Overlay Groups
Figure 4.6 shows the effect that the Overlay Groups option has on an overlay plot with one Y
variable, one X variable, and a grouping variable. The grouping variable has two levels. The
plot on the left has Overlay Groups turned off, so a separate graph is produced for the two
levels of the grouping variable. The plot on the right has Overlay Groups turned on, so there is
a single graph that uses colors and markers to show the two levels of the grouping variable. A
legend describing the levels is added under the graph.
Figure 4.6 Overlay Groups: Off (left) and On (right)
Separate Axes
Figure 4.7 shows the effect that the Separate Axes option has on an overlay plot with two Y
variables and one X variable. The Overlay Y’s option is turned off, so a separate plot is
produced for each Y variable. The plot on the left has Separate Axes turned off, so the two
graphs share a single x-axis. The plot on the right has Separate Axes turned on, so both graphs
have their own x-axis.
Chapter 4
Essential Graphing
Overlay Plots
Overlay Plot Options
83
Figure 4.7 Separate Axes: Off (left) and On (right)
Y Options
Each Y variable is labeled in a legend beneath the plot. The Y options are available from the Y
Options menu from the red triangle menu for Overlay Plot. You can also access the Y Options
menu by right-clicking on any Y variable in the legend.
Note: If no Y variables are selected, any Y options that you select affect all Y variables. If one
or more of the Y variables are selected, any Y options that you select affect only those you have
selected.
Selecting and Deselecting Y Variables in the Legend
•
Hold the SHIFT key and click to select multiple contiguous legend levels.
•
Hold the CONTROL key and click to select multiple discontiguous legend levels.
•
Hold the CONTROL key and click a selected legend level to deselect it.
Table 4.3 Descriptions of Y Options
Show Points
A toggle that either shows or hides points in the graph.
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Overlay Plots
Additional Examples of the Overlay Plot Platform
Chapter 4
Essential Graphing
Table 4.3 Descriptions of Y Options (Continued)
Connect Points
A toggle that either connects the points with lines or turns the
connecting lines off. You can use Connect Points without
showing points.
Needle
Draws a vertical line from each point to the x-axis.
Step
Draws a horizontal line from each point to the x value of the
following point, and then a vertical line to that point. You can use
Step without showing points.
Note: The Connect Points, Needle, and Step options are mutually exclusive.
Function Plot
Plots a formula (stored in the Y column) as a smooth curve. To
use this function, store a formula in a column that is a function of
a single X column. Assign the formula to the Y role. For an
example, see “Function Plots” on page 84.
Connect Color
Displays the JMP color palette for assigning colors to connecting
lines.
Overlay Marker
Displays the JMP marker palette for assigning markers to plotted
points.
Overlay Marker Color
Assigns a color to all points of the selected variable.
Line Style
Enables the choice of dashed, dotted, or other line styles.
Line Width
Enables the choice of line widths.
Additional Examples of the Overlay Plot Platform
The following sections show several examples of different overlay plots.
Function Plots
Overlay Plot normally assumes you want a function plot when the Y column contains a
formula. However, formulas that contain random number functions are more frequently used
with simulations, where function plotting is not often wanted. Therefore, the Function Plot
option is off by default when a random number function is present, but on for all other
functions.
To See an Example of a Function Plot
1. Select Help > Sample Data Library and open Density Compare.jmp.
Chapter 4
Essential Graphing
Overlay Plots
Additional Examples of the Overlay Plot Platform
85
2. From the Graph menu, select Overlay Plot.
3. Assign gamma1, gamma3, and gamma5 as the Y variables.
4. Assign Xgamma as the X variable.
5. Click OK.
6. Turn of the Show Points option by selecting Y Options > Show Points from the red triangle
menu.
Figure 4.8 Function Plot
Plotting Two or More Variables with a Second Y-axis
A second y-axis is useful for plotting data with different scales on the same plot, such as a
stock’s closing price and its daily volume, or temperature and pressure. For example, consider
plotting the selling price of an inexpensive stock against the Dow Jones Industrial Average.
1. Select Help > Sample Data Library and open Stock Prices.jmp.
2. From the Graph menu, select Overlay Plot.
3. Assign High, Low, Close, and Volume as Y variables.
4. Select Volume in the Y list and click Left Scale/Right Scale.
This action assigns Volume to the right axis, leaving the others on the left axis. The arrows
to the left of the Y variables show you which axis each variable is assigned.
5. Assign Date as the X variable.
6. Click OK.
7. From the red triangle menu for Overlay Plot, select Y Options > Connect Points.
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Overlay Plots
Additional Examples of the Overlay Plot Platform
Chapter 4
Essential Graphing
Figure 4.9 Dual Axis Overlay Plot
The variables High, Low, and Close are the stock prices of the same stock and thus are on the
same scale. Volume is a different scale entirely, representing the trading volume of the entire
Dow Jones Industrial Average.
To see why this matters, perform the same steps above without clicking the Left Scale/Right
Scale button for Volume. Compare the resulting graph in Figure 4.10 to Figure 4.9.
Figure 4.10 Single Axis Overlay Plot
Grouping Variables
The Overlay Plot platform allows the production of several plots in one window through the
use of grouping variables. With one grouping variable, a stacked vector of plots appears, with
one plot for each level of the grouping variable. Two grouping variables result in a matrix of
plots.
1. Select Help > Sample Data Library and open Students.jmp.
2. From the Graph menu, select Overlay Plot.
Chapter 4
Essential Graphing
Overlay Plots
Additional Examples of the Overlay Plot Platform
87
3. Assign weight as the Y variable and height as the X variable.
4. Assign age and sex as grouping variables.
5. Click OK.
A portion of the resulting plot is shown in Figure 4.11.
Figure 4.11 Grouped Plots Without Separate Axes
Select the Separate Axes option from the red triangle menu to produce plots that do not share
axes. Compare Figure 4.11 to Figure 4.12.
88
Overlay Plots
Additional Examples of the Overlay Plot Platform
Figure 4.12 Grouping Variables
Chapter 4
Essential Graphing
Chapter 5
Scatterplot 3D
Create a Rotating Three-Dimensional View of Data
The Scatterplot 3D platform shows the values of numeric columns in the associated data table
in a rotatable, three-dimensional view. Up to three columns that you select from the associated
data table are displayed at one time. See Figure 5.1.
To help visualize variation in higher dimensions, the 3D scatterplot can show a biplot
representation of the points and variables when you request principal components. The most
prominent directions of data are displayed on the 3D scatterplot report.
Figure 5.1 Example of a 3D Scatterplot
Contents
Example of a 3D Scatterplot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Launch the Scatterplot 3D Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
The Scatterplot 3D Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Spin the 3D Scatterplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Change Variables on the Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Adjust the Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Assign Colors and Markers to Data Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Assign Colors and Markers in the Data Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Scatterplot 3D Platform Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Normal Contour Ellipsoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Nonparametric Density Contours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Context Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Additional Examples of the Scatterplot 3D Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Example of an Ungrouped Normal Contour Ellipsoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Example of Grouped Normal Contour Ellipsoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Example of a Grouped Nonparametric Density Contour . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Chapter 5
Essential Graphing
Scatterplot 3D
Example of a 3D Scatterplot
Example of a 3D Scatterplot
This example uses the Iris.jmp sample data table, which includes measurements of sepal
length, sepal width, petal length, and petal width for three species of iris.
1. Open the Iris.jmp sample data table.
2. Select Graph > Scatterplot 3D.
3. Select Sepal length, Sepal width, and Petal length, and click Y, Columns.
4. Select Petal width and click Weight.
5. Click OK.
Figure 5.2 Example of an Initial 3D Scatterplot
Now you can spin the 3D scatterplot to see the relationships between the variables. In this
example, the data points are formatted in blue, red, and green. You might want to spin the
scatterplot to see more clearly the relationships between the red and green points.
91
92
Scatterplot 3D
Launch the Scatterplot 3D Platform
Chapter 5
Essential Graphing
Launch the Scatterplot 3D Platform
Launch the Scatterplot 3D platform by selecting Graph > Scatterplot 3D.
Figure 5.3 The Scatterplot 3D Launch Window
Table 5.1 Description of the Scatterplot 3D Launch Window
Y, Columns
Select the variables to plot on the 3D scatterplot. The order in
which you select the variables determines where the data points
appear on the axes:
•
The first variable appears on the x axis.
•
The second variable appears on the y axis.
•
The third variable appears on the z axis.
You can assign the remaining variables interactively through the
drop-down menus below the scatterplot.
Weight
Use the Weight variable to:
•
Assign a weight (importance or influence) to the data
•
Visualize a fourth variable that sizes the points
Note: Red triangle options account for the Weight variable. If you
do not want this variable accounted for in your analyses, remove it
from the launch window.
When you specify a Weight variable, JMP draws the points as balls.
The balls are scaled so that their volume represents the weight
value. You click and drag the Circle Size slider below the
scatterplot to resize the balls.
Freq
Identifies the data table column whose variables assign a
frequency to each row. This option is useful when a frequency is
assigned to each row in summarized data.
Chapter 5
Essential Graphing
Scatterplot 3D
The Scatterplot 3D Report
93
Table 5.1 Description of the Scatterplot 3D Launch Window (Continued)
By
Produces a separate 3D scatterplot for each By variable value.
When two By variables are assigned, a separate graph is produced
for each combination of both By variables.
For more information about the launch window, see Using JMP.
After you click OK, the Scatterplot 3D report window appears. See “The Scatterplot 3D
Report” on page 93.
The Scatterplot 3D Report
To produce the 3D scatterplot shown in Figure 5.4, follow the instructions in “Example of a 3D
Scatterplot” on page 91.
The Scatterplot 3D report shows a three-dimensional spinnable view of your data. See
Figure 5.4. In the launch window, you select the variables and then create the report. The
variables are displayed on the 3D scatterplots’ x, y, and z axes. Up to three variables can be
displayed at a time.
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The Scatterplot 3D Report
Chapter 5
Essential Graphing
Figure 5.4 Example of Information Displayed on the Scatterplot 3D Report
Click and drag an
empty area inside the
scatterplot to rotate it.
z axis
x axis
y axis
Plot Source
Circle Size
slider
x axis
y axis
z axis
axis controls
Note: Any rows that are excluded in the data table are also hidden in the 3D scatterplot.
Table 5.2 Description of the Scatterplot 3D Report
Plot source
The plot source box indicates the source of the data in the plot.
Circle Size slider
Note: The Circle Size slider appears only if you have specified a
Weight variable.
Click and drag the Circle Size slider to resize the balls while
maintaining their relative sizes.
Axis controls
Select which variable appears on each axis. Choose the Other option
to add a new variable.
Chapter 5
Essential Graphing
Scatterplot 3D
The Scatterplot 3D Report
95
Table 5.2 Description of the Scatterplot 3D Report (Continued)
Next Axis Set (not
shown)
Note: The Next Axis Set button appears only if your analysis
contains more than three variables.
Cycles through the axis controls for any hidden variables. See
“Change Variables on the Axes” on page 96.
After you create a 3D scatterplot, you can add features such as displaying ellipses around
specific data points, showing separate principal components, rotating components,
connecting points, and more. See “Scatterplot 3D Platform Options” on page 98 for details.
You can also assign colors and symbols (or markers) to data points either on the 3D scatterplot
itself or in the associated data table. See “Assign Colors and Markers to Data Points” on
page 97 and “Assign Colors and Markers in the Data Table” on page 98.
Spin the 3D Scatterplot
You spin the 3D scatterplot report in four ways:
•
Click and drag an empty area on the 3D scatterplot. The 3D scatterplot spins in the
direction you dragged the mouse.
Note: Click and drag on an empty area on the 3D scatterplot, not on an axis or data point.
Dragging the axis rescales the axis. Dragging a data point only selects the point.
•
Slide the mouse wheel. The 3D scatterplot spins up and down only.
•
Hold down an arrow key. (Before using an arrow on the number keypad, verify that NUM
LOCK is turned off.)
•
Hold down ESC. The 3D scatterplot spins left and right only.
In each case, the 3D scatterplot spins as long as you hold down the mouse button, arrow key,
or ESC key. The spinning also continues as you slide the mouse wheel.
You can also spin the 3D scatterplot continuously as follows:
•
Click and drag: Hold down SHIFT, click and drag an empty area on the plot, and release
SHIFT. The faster you drag the mouse, the faster the 3D scatterplot spins.
•
Mouse wheel: Hold down SHIFT, slide the wheel, and release the wheel. The 3D
scatterplot spins up and down only.
•
Arrow keys: Hold down SHIFT, press the arrow key, and release SHIFT.
•
ESC key: Hold down SHIFT and press ESC. The 3D scatterplot spins left and right only.
In addition to automatically spinning the plot, you can oscillate the plot. Hold down SHIFT
and CTRL and then click and drag the plot. The plot shakes up and down or left to right,
depending on the direction in which you dragged the plot.
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Chapter 5
Essential Graphing
To stop the spinning or oscillating, click on the plot or press ESC.
Change Variables on the Axes
The variables on each axis are determined by the order in which you select the variables in the
launch window. For example, the first variable that you select is displayed on the x axis. The
second variable is displayed on the y axis, and the third variable is displayed on the z axis.
After you create a 3D scatterplot, you can change the variable assigned to an axis, plot a
different set of variables, or sequence through all combinations of the variables.
1. To change the variable on a specific axis, select the axis control drop-down menu and
select a different variable.
2. To add a different variable, click an axis control drop-down menu, select Other, select the
variable, and then click OK.
3. To sequence through combinations of all variables, click the Next Axis Set button until the
variables that you want to plot are displayed.
Adjust the Axes
You can manually move or rescale the axis coordinates by clicking and dragging the axis. This
option shows a different set of coordinates on the 3D scatterplot. It also lets you change the
space displayed between the coordinates (or the coordinate scaling).
You can also specify axis properties by double-clicking the axis and modifying settings in the
specifications window.
To Move the Coordinates on the Axis
1. Place your cursor over the middle of the axis.
2. Click and drag the axis.
To Modify Coordinate Scaling
1. Place your cursor over the end of the axis.
2. Click and drag the axis.
To Rescale an Axis Precisely
1. Place your cursor over the middle of the axis (the axis, not the label).
2. Double-click the axis.
Chapter 5
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The Scatterplot 3D Report
97
Figure 5.5 The Axis Options Window
Table 5.3 Description of the Axis Options Window
Scale
Changes the scale of the axes. See the Using JMP book.
Format
Specifies how to display numbers on the 3D scatterplot. See the
Using JMP book.
Use thousands
separator
Displays a comma in numbers above 999. This option is available
depending on which option you select in the Format drop-down
menu.
Width
Controls the number of characters that appear.
Minimum
Specifies the minimum coordinate value.
Maximum
Specifies the maximum coordinate value.
Increment
Specifies the space between the coordinates.
Assign Colors and Markers to Data Points
Each point in the 3D scatterplot corresponds to a row in the associated data table. To highlight
points on the 3D scatterplot, you assign colors and markers to the points. The colors and
markers are then displayed on the 3D scatterplot and in the data table.
When you click a point, the following items are selected:
•
the point in the 3D scatterplot
•
the corresponding row in the associated data table
•
the point in any other opened graphs, if applicable
To select one point, click the point.
To select several points, double-click the 3D scatterplot and drag the cursor over the points. A
box is displayed to indicate which points are selected.
To deselect points, double-click the 3D scatterplot.
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To assign a color or marker to selected data points, proceed as follows:
1. To assign a color to the selected point, select Rows > Colors and then select the color.
2. To assign a marker to the selected point, select Rows > Markers and then select the marker.
Assign Colors and Markers in the Data Table
You can assign colors and markers to rows in the data table. The colors and markers appear
next to the row number in the data table and on the 3D scatterplot. This option distinguishes
points for each variable, and you can save the settings in the data table. Assigning colors and
markers to specific data points (as described in “Assign Colors and Markers to Data Points” on
page 97) only highlights them for the current open graphs.
See the Using JMP book for details about assigning colors and markers in the data table. For
details about changing the size, quality, or transparency of markers, see “Scatterplot 3D
Settings” on page 104.
Scatterplot 3D Platform Options
The red triangle menu next to Scatterplot 3D contains options to customize the display and to
compute, rotate, and save principal or rotated components.
Table 5.4 Descriptions of the Scatterplot 3D Options
Show Points
Shows or hides the data points on the graph.
Show Controls
Shows or hides the source and axis controls displayed beneath the
3D scatterplot. See Figure 5.4.
Drop Lines
Draws lines from each point to the plane created by the x and z
variables that you selected on the launch window.
Connect Points
Connects the points with a line. Points can be connected on the
data as a whole or in groups. You can also group data by a specific
variable.
Normal Contour
Ellipsoids
Draws one or more normal contour ellipsoids, that is,
three-dimensional ellipses that encompass a specified portion of
points. You specify whether you want an ellipsoid for all of the
data or for each group. You can also control the size and
transparency of the ellipsoids. For details, see “Normal Contour
Ellipsoids” on page 101.
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Table 5.4 Descriptions of the Scatterplot 3D Options (Continued)
Ellipsoid Coverage
Changes the size of normal contour ellipsoids. Type a value
between 0 and 1, where the greater the value creates a bigger the
ellipsoid. The actual values “0” and “1” produce no ellipsoid, so a
warning appears if you try to use those values.
This option only appears after you add a normal contour ellipsoid
to the 3D scatterplot.
Ellipsoid Transparency
Changes the surface of normal contour ellipsoids. The greater the
value, the more opaque the ellipsoid. This option only appears
after you add a normal contour ellipsoid to the 3D scatterplot.
Nonpar Density
Contour
Draws nonparametric density contours, which approximately
encompass a specified proportion of the points. You specify
whether you want a density contour for all of the data or for each
group. For details, see “Nonparametric Density Contours” on
page 101.
Drop Line Thickness
Changes the width of drop lines. This option only appears after
you add drop lines to the 3D scatterplot.
Principal Components
Calculates principal components on all variables. This changes the
axes of the plot to have principal component scores.
Biplot rays are displayed by default. You can remove them by
selecting Biplot Rays from the red triangle menu. For details about
principal components, see the Multivariate Methods book.
Std Prin Components
Calculates principal components (as with the Principal
Components option) but scales the principal component scores to
have unit variance. If this option is not selected, the scores have
variance equal to the corresponding eigenvalue.
With standardized principal components, the correlation between
the variables and the principal component scores is equal to the
values in the eigenvector. This helps you quickly assess the relative
importance of the variables.
For details, see the Multivariate Methods book.
Note: Select this option if you want GH' rather than JK' biplots.
GH' biplots try to preserve relationships between variables; JK'
biplots try to preserve relationships between observations. The
interpoint distance shown by GH' biplots is less meaningful, but
the angles of the GH' biplot rays measure correlations better.
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Table 5.4 Descriptions of the Scatterplot 3D Options (Continued)
Rotated Components
Specifies the number of factors that you want to rotate and the
rotation method. You rotate components to better align the
directions of the factors with the original variables so that the
factors might be more interpretable.
For details, see the Multivariate Methods book.
Biplot Rays
Shows or hides biplot rays that correspond to the principal
components. You must have already selected Principal
Components, Std Prin Components, or Rotated Components for
this option to appear.
Show Ray Labels
Shows or hides labels for the biplot rays. You must have already
selected Biplot Rays for this option to appear.
Remove Prin Comp
Removes principal components, standardized principal
components, and rotated components from the scatterplot 3D
report. The 3D scatterplot reverts to its original display before
principal components were selected. This option, however, does
not remove any saved principal components from the data table.
This option only appears after you add principal, standard, or
rotated components to the 3D scatterplot.
Save Prin Components
Saves the specified number of current principal component scores
as new columns in the current data table. These columns also
include the formulas used for the principal components. For n
variables in the components list, n principal component columns
are created and named Prin1, Prin2, ... Prinn.
This option only appears after you add principal, standard, or
rotated components to the 3D scatterplot.
Save Rotated
Components
Saves all rotated component scores as columns in the current data
table. These columns also include the formulas that were used. If
you requested n rotated components, then n rotated component
columns are created and named Rot1, Rot2, ... Rotn.
This option only appears after you add rotated components to the
3D scatterplot.
Script
Contains options that are available to all platforms. They enable
you to redo or relaunch the analysis, turn off automatic
recalculation, copy the script, or save the JSL commands for the
analysis. For more information, see Using JMP.
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Normal Contour Ellipsoids
A normal contour ellipsoid is a 3-dimensional ellipse that encompasses a specified portion of
points. The ellipsoid is computed from a contour of the multivariate normal distribution fit to
the points. The ellipsoid is a function of the means, standard deviations, and correlations of
variables on the plot. See the Multivariate Methods book for details about multivariate normal
distributions.
When you add an ellipsoid, two formatting options are available:
•
Coverage changes the portion of data points covered by the ellipsoid. The larger the value,
the bigger the ellipsoid.
•
Transparency changes the surface of the ellipsoid from transparent to opaque. The larger
the value, the more opaque the ellipsoid.
The coverage and transparency options also appear in the red triangle menu after you add the
ellipsoid.
When you add normal contour ellipsoids to a 3D scatterplot, you specify whether you want an
ellipsoid for all of the data or for a specific group of data. The ellipsoid for each set of grouped
data is color-coded to differentiate one group from another.
You display and remove normal contour ellipsoids by selecting and deselecting Normal
Contour Ellipsoids from the red triangle menu.
The examples in this section use the Iris.jmp sample data table, which includes measurements
of sepal length, sepal width, petal length, and petal width for three species of iris.
Related Information
•
“Example of an Ungrouped Normal Contour Ellipsoid” on page 106
•
“Example of Grouped Normal Contour Ellipsoids” on page 107
Nonparametric Density Contours
The nonparametric density contour shows contours that approximately encompass a specified
proportion of the points. You add nonparametric density contours to see patterns in point
density when the scatterplot is darkened by thousands of points.
This feature is particularly valuable when you have many points on a 3D scatterplot; the
contours can be so dark that you cannot see the structure. In this situation, you remove the
points so that only the contours are displayed. See “Optimizing a Dense Nonparametric
Density Contour” on page 103 for details.
When you add nonparametric density contours to a 3D scatterplot, you specify whether you
want a contour for all of the data or for a specific group of data. The contour for each set of
grouped data is color-coded to differentiate one group from another.
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You display and remove nonparametric density contours by selecting and deselecting Nonpar
Density Contours from the red triangle menu.
Related Information
•
“Example of a Grouped Nonparametric Density Contour” on page 108
Density Contour Controls
The Density Contour Controls options are displayed below the 3D scatterplot. These options
let you select additional contours and change each contour’s formatting.
Figure 5.6 The Density Contour Controls Window
first contour level
second contour level
third contour level
Table 5.5 Description of the Density Contour Controls Window
Contour Quantile
Controls which contours are shown and lets you customize the
contour formatting.
•
Density level represents the volume and density of the points.
As the contours go from smaller to larger values, the contours
cover less volume but more dense areas. A 0.9 contour
represents the 10% densest part of the total, where the points
are closest together. Click and drag the slider below “Contour
Quantile,” or enter a value next to the slider.
•
Transparency changes the surface of density contours. The
greater the value, the more opaque the contour. Enter a value
in the box.
•
Color changes the color of the contour. Click the colored box
and select a different color. (This option only appears for
ungrouped density contours.)
Changes to these settings take effect immediately.
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Table 5.5 Description of the Density Contour Controls Window (Continued)
Resolution
Changes the resolution of the contours. A higher resolution results
in a less granular drawing of the contours but takes more time to
display.
Column Bandwidth
Changes the smoothness of the fitted density. A higher bandwidth
results in a smoother fitted density.
Type a new bandwidth for each variable, or click and drag the
sliders. Click Apply to display your changes.
Optimizing a Dense Nonparametric Density Contour
When you have many points on a 3D scatterplot, the contours can be so dark that you cannot
see the structure. In this situation, you remove the points so that only the contours are
displayed.
To remove points from a 3D scatterplot, select Show Points from the red triangle menu. You
can further optimize the contours by changing their size, color, and transparency. See Table 5.4
for details.
Figure 5.7 Example of Optimizing a Dense Nonparametric Density Contour
3D scatterplot with density
contour and points
3D scatterplot with density
contour and no points
Context Menu
Right-click the 3D Scatterplot to see the context menu.
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Table 5.6 Descriptions of the Context Menu Options
Show Legend
Shows and hides the color legend for the 3D scatterplot.
Note: This option requires the Coloring role.
Reset
Returns the orientation of the scatterplot to its original state.
Settings
Provides options to change the appearance of the 3D scatterplot.
See “Scatterplot 3D Settings” on page 104.
Hide Lights Border
Shows and hides a border that displays the lights. The lights
highlight different portions of the 3D scatterplot.
Right-click a light to turn it on or off and to change the color.
Wall Color
Changes the color of the 3D scatterplot.
Background Color
Changes the color surrounding the 3D scatterplot.
Rows
You can color, mark, exclude, hide, and label points that
correspond to rows in the associated data table. You must select
the points before selecting this option. See the Using JMP book.
Use Hardware
Acceleration
Turns hardware acceleration on or off for machines that support
acceleration. This option might display the scatterplot faster. If not,
try updating your graphics drivers.
Show ArcBall
Shows and hides a globe around the 3D scatterplot. This option
helps you visualize the rotation of the scatterplot. Select whether
you want the ArcBall to appear always, only when you drag the
scatterplot, or never.
Scatterplot 3D Settings
To customize properties such as the marker size, text size, and grid lines, right-click the 3D
scatterplot and select Settings. The Settings window appears. As you modify the settings, a
preview appears on the 3D scatterplot.
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Figure 5.8 The Scatterplot 3D Settings Window
Note the following:
•
Move the sliders left to decrease the selected property or to the right to increase the
selected property.
•
To move the Settings window around the scatterplot, click and drag the top portion of the
window.
Table 5.7 Descriptions of the Scatterplot 3D Settings Window Options
Reset
Resets the default settings.
Done
Closes the window.
Walls
Adds or removes the 3D scatterplot walls. Without walls, the
background color of the 3D scatterplot is displayed.
Grids
Shows or hides the coordinate lines.
Axes
Shows or hides the variable names that appear above each axis.
Box
Shows or hides the box. Without the box, the 3D scatterplot is
displayed as an open plot.
Zoom
Enlarges or shrinks the 3D scatterplot.
Orthographic
Changes the view of the scatterplot from 3-dimensional to an
orthographic projection. In the orthographic view, the walls of the
scatterplot do not converge to a vanishing point. This means that
you can compare near and far distances and see the structure
between data points.
Note: If you turn off orthographic view and completely decrease
the perspective, the walls of the scatterplot do not converge. This is
the same effect that you get when you turn on orthographic view.
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Table 5.7 Descriptions of the Scatterplot 3D Settings Window Options (Continued)
Perspective
Increases or decreases the perspective. Large values create a view
that is unnaturally large and visually disorienting. In this case, you
need to resize the scatterplot window to show the entire plot.
Marker Size
Increases or decreases the size of the data point markers.
Marker Quality
Increases and decreases the data marker quality. For example,
when you increase the marker quality, some markers have an
opaque center. Other symbol markers are formatted in bold.
Increase the zoom to see these changes in quality.
Marker Transparency
Increases or decreases the transparency of the data markers.
Text Size
Increases or decreases the text size.
Line Width
Changes the width of the coordinate and axes lines.
Additional Examples of the Scatterplot 3D Platform
This section contains additional examples using 3D scatterplots.
Example of an Ungrouped Normal Contour Ellipsoid
This example shows how to add a normal contour ellipsoid to more than 75% of the data
points. The ellipsoid is 25% transparent.
1. Select Help > Sample Data Library and open Iris.jmp.
2. Select Graph > Scatterplot 3D.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns.
4. Click OK.
5. From the red triangle menu, select Normal Contour Ellipsoids. Notice that Ungrouped is
already selected.
6. Type 0.75 next to Coverage.
7. Type 0.25 next to Transparency.
8. Click OK.
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Additional Examples of the Scatterplot 3D Platform
107
Figure 5.9 Example of an Ungrouped Normal Contour Ellipsoid
Example of Grouped Normal Contour Ellipsoids
This example shows how to group measurements by species and to format each group with a
normal contour ellipsoid. The ellipsoids cover 75% of the data points and are 50% transparent.
The contours are color-coded based on species.
1. Select Help > Sample Data Library and open Iris.jmp.
2. Select Graph > Scatterplot 3D.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns.
4. Click OK.
5. From the red triangle menu, select Normal Contour Ellipsoids.
6. Select Grouped by Column.
7. Select Species.
8. Type 0.75 next to Coverage.
9. Type 0.5 next to Transparency.
10. Click OK.
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Figure 5.10 Example of Grouped Normal Contour Ellipsoids
Example of a Grouped Nonparametric Density Contour
This example shows how to group data points and format each nonparametric density
contour.
1. Select Help > Sample Data Library and open Iris.jmp.
2. Select Graph > Scatterplot 3D.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns.
4. Click OK.
5. From the red triangle menu, select Nonpar Density Contour.
6. Select Grouped by Column.
7. Select Species and click OK. A different colored contour is displayed for each of the three
species.
8. Type 0.25 in the first Contour Quantile box. 25% of the data points appear outside the
contour surfaces, which results in smaller contours.
9. Type 0.15 in the first Transparency box. The contours are 15% opaque.
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Figure 5.11 Changing the Nonparametric Density Contour Transparency and Density
original contours
decreased density
decreased transparency
10. Select the second check box. The contour quantiles are the same (.25), so the new contours
overlap the first ones.
11. Type 0.5 in the second Contour Quantile box. 50% of the data points appear outside the
contour surfaces. A second set of contours appears within the first, to further illustrate the
density of the data points.
Figure 5.12 Adding a Second Nonparametric Density Contour
Darker contours indicate a
higher concentration of
data points.
one level of contours
two levels of contours
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You can now format the second levels of contours and turn on the third level of contours.
The options for formatting the grouped and ungrouped nonparametric density contours are
similar. The only difference is that you cannot change the color of each grouped nonparametric
density contour. See Table 5.4 for options.
Chapter 6
Contour Plots
View Multidimensional Relationships in Two Dimensions
The Contour Plot command in the Graph menu constructs contours of a response in a
rectangular coordinate system. A contour plot shows a three-dimensional surface in two
dimensions. Contours delineate changes in the third dimension.
Here are some of the options available with the Contour platform:
•
specify the number of contour levels
•
choose to plot contour lines or filled contours
•
show or hide data points
•
label contours with response values
•
define and use a custom coloring scheme
Figure 6.1 Examples of Contour Plots
Contents
Example of a Contour Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Launch the Contour Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
The Contour Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Contour Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Fill Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Contour Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Contour Plot Save Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Use Formulas for Specifying Contours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Additional Examples of Contour Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Example of Triangulation, Transform, and Alpha Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Chapter 6
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Example of a Contour Plot
113
Example of a Contour Plot
To create a contour plot, you need two variables for the x- and y-axes and at least one more
variable for contours. You can also use several y-variables. This example uses the Little
Pond.jmp sample data table. X and Y are coordinates of a pond. Z is the depth.
1. Select Help > Sample Data Library and open Little Pond.jmp.
2. Select Graph > Contour Plot.
3. Select the X and Y coordinates and click X.
4. Select the depth, Z, and click Y.
Note: In a contour plot, the X1 and X2 roles are used for the X and Y axes.
5. Click Specify.
6. In the Contour Specification window, select # of Contours as 7.
7. Select Minimum as -4.
8. Select Maximum as 8.
9. Click OK.
Figure 6.2 Example of a Contour Plot with Legend
The x- and y-axes are coordinates and the contour lines are defined by the depth variable. This
contour plot is essentially a map of a pond showing depth.
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Chapter 6
Essential Graphing
Launch the Contour Plot Platform
Launch the Contour Plot platform by selecting Graph > Contour Plot.
By default, the contour levels used in the plot are values computed from the data. You can
specify your own number of levels and level increments in the Launch window before you
create the plot. You can also do so in the red triangle menu for Contour Plot after you create
the plot. You can use a column formula to compute the contour variable values.
Figure 6.3 The Contour Plot Launch Window
Table 6.1 Description of the Contour Plot Launch Window
Y
Columns assigned to the Y role are used as variables to
determine the contours of the plot. You must specify at least
one, and you can specify more than one.
You can also assign a column with a formula to this role. If you
do so, the formula should be a function of exactly two variables.
Those variables should be the x variables entered in the Launch
window.
X
Columns assigned to the X role are used as the variables for the
x- and y-axes. You must specify exactly two columns for X.
By
This option produces a separate graph for each level of the By
variable. If two By variables are assigned, a separate graph for
each possible combination of the levels of both By variables is
produced.
Options:
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Table 6.1 Description of the Contour Plot Launch Window (Continued)
Contour Values
Specify your own number of levels and level increments.
See “Contour Specification” on page 118.
Fill Areas
Fill the areas between contour lines using the contour line
colors.
Use Table Data
Specify Grid
Most often, you construct a contour plot for a table of recorded
response values. In that case, Use Table Data is selected and the
Specify Grid button is unavailable.
However, if a column has a formula and you specify that
column as the response (Y), the Specify Grid button becomes
available. When you click Specify Grid, you can define the
contour grid in any way, regardless of the rows in the existing
data table. This feature is also available with table templates
that have one or more columns defined by formulas but no
rows.
See “Use Formulas for Specifying Contours” on page 121.
For more information about the launch window, see Using JMP.
After you click OK, the Contour plot appears. See “The Contour Plot” on page 115.
The Contour Plot
Follow the instructions in “Example of a Contour Plot” on page 113 to produce the plot shown
in Figure 6.4.
The legend for the plot shows individual markers and colors for the Y variable. Replace
variables in the plot by dragging and dropping a variable, in one of two ways: swap existing
variables by dragging and dropping a variable from one axis to the other axis; or, click on a
variable in the Columns panel of the associated data table and drag it onto an axis.
For information about additional options for the report, see “Contour Plot Platform Options”
on page 116.
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Figure 6.4 The Contour Plot Report
Contour Plot Platform Options
Using the options in the red triangle menu next to Contour Plot, you can tailor the appearance
of your contour plot and save information about its construction.
Table 6.2 Descriptions of Contour Plot Platform Options
Show Data Points
Shows or hides (x, y) points. The points are hidden by default.
Show Missing Data
Points
Shows or hides points with missing y values. Only available if
Show Data Points is selected.
Show Contours
Shows or hides the contour lines or fills. The contour lines are
shown by default.
Show Boundary
Shows or hides the boundary of the total contour area. The
boundary is shown by default.
Show Control Panel
Shows or hides the Alpha slider that allows you to change the
Alpha shapes filter.
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Table 6.2 Descriptions of Contour Plot Platform Options (Continued)
Transform
If the contour plot includes a Color role, the Transform option is
enabled. See “Example of Triangulation, Transform, and Alpha
Shapes” on page 122 for details.
None The triangulation is computed without any scaling to
coordinates using Delaunay triangulation. Delaunay triangles
are computed to maximize the minimum angle of the triangles
in the triangulation. This value is selected by default.
Range Normalized The X1/X2 values are both scaled to [0,1]
prior to computing the triangulation. If the X1/X2 limits are
different, then this is a non-uniform scale. This option may be
more desirable in cases where the X1/X2 units are very
different.
Fill Areas
Fills the areas between the contours with a solid color. It is the
same option that is available in the Launch window. If you leave
it deselected in the Launch window, you can see the line contours
before filling the areas.
See “Fill Areas” on page 117.
Label Contours
Shows or hides the label (z-value) of the contour lines.
Color Theme
Select another color theme for the contours.
Reverse Colors
Reverses the order of the colors assigned to the contour levels.
Change Contours
Set your own number of levels and level increments.
See “Contour Specification” on page 118.
Save
This menu has options to save information about contours,
triangulation, and grid coordinates to data tables.
See “Contour Plot Save Options” on page 120.
Script
This menu contains commands that are available to all platforms.
They enable you to redo the analysis or save the JSL commands
for the analysis to a window or a file. For more information, see
Using JMP.
Fill Areas
If you select Fill Areas, the areas between contour lines are filled with the contour line colors.
This option is available in the Launch window and in the red triangle menu for Contour Plot.
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Figure 6.5 shows a plot with contour lines on the left and a plot with the contour areas filled
on the right.
Figure 6.5 Comparison of Contour Lines and Area Fills
Areas are filled from low to high values. An additional color is added in the filled contour plot
for the level above the last, and highest, contour line.
Contour Specification
If you do not select options in the Launch window, the default plot spaces the contour levels
equally within the range of the Y variable. The default colors are assigned via the Continuous
Color Theme in Preferences. You can see the colors palette with the Colors command in the
Rows menu or by right-clicking on an item in the Contour Plot legend.
You can specify contour levels either in the Launch window (the Specify button) or in the
report window from the red triangle menu for Contour Plot (the Specify Contours option).
Chapter 6
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Contour Plot Platform Options
119
Figure 6.6 Example of Contour Specification: Launch Window (on the left) and Menu (on the
right)
Specify
This option is both in the Launch window and on the red triangle menu for Contour Plot (the
Specify Contours option).
Selecting this option displays the Contour Specification window. See Figure 6.7. Using this
window, you can do the following:
•
change the number of contours
•
specify minimum and maximum values to define the range of the response to be used in
the plot
•
change the increment between contour values
You supply any three of the four values, and the remaining value is computed for you. Click
on the check box to deselect one of the numbers and automatically select the remaining check
box.
Figure 6.7 The Contour Specification Window
Colors are automatically assigned and are determined by the number of levels in the plot.
After the plot appears, you can right-click (press CONTROL and click on the Macintosh) on
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any contour in the plot legend and choose from the JMP color palette to change that contour
color.
Retrieve
This option is both in the Launch window (the Retrieve button) and on the red triangle menu
for Contour Plot (the Retrieve Contours option).
Note: Neither the button nor the menu option are active unless there is an open data table in
addition to the table that has the contour plotting values. When you click Retrieve or select
Retrieve Contours, a window with a list of open data tables appears.
Using this option, you can retrieve the following from an open JMP data table:
•
the number of contours
•
an exact value for each level
•
a color for each level
From the list of open data tables, select the data table that contains the contour levels.
For level value specification, the Contour Plot platform looks for a numeric column with the
same name as the response column that you specified in the Launch window. The number of
rows in the data table defines the number of levels.
If there is a row state column with color information, those colors are used for the contour
levels. Otherwise, the default platform colors are used.
Revert Contours
This option appears only on the red triangle menu for Contour Plot.
If you have specified your own contours, selecting this option reverts your Contour Plot back
to the default contours.
Contour Plot Save Options
This menu has options to save information about contours, triangulation, and grid
coordinates to data tables.
Save Contours creates a new JMP data table with columns for the following:
•
the x- and y-coordinate values generated by the Contour platform for each contour
•
the response computed for each coordinate set
•
the curve number for each coordinate set
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The number of observations in this table depends on the number of contours you specified.
You can use the coordinates and response values to look at the data with other JMP platforms.
For example, you can use the Scatterplot 3D platform to get a three-dimensional view of the
pond.
Generate Grid displays a window that prompts you for the grid size that you want. When you
click OK, the Contour platform creates a new JMP data table with the following:
•
the number of grid coordinates you requested
•
the contour values for the grid points computed from a linear interpolation
Save Triangulation creates a new JMP data table that lists coordinates of each triangle used to
construct the contours. By default, JMP uses Delaunay triangulation to connect the nearest
data points to form triangles. The resulting set of triangles are calculated so that no other data
points are inside a triangle’s circumscribed circle, that is, the circle that passes through the
three vertices of the triangle. To change the triangulation to a normalized scale, select
Transform > Range Normalized.
Use Formulas for Specifying Contours
Most often you construct a contour plot for a table of recorded response values such as the
Little Pond data table. In that case, in the launch window, Use Table Data is checked and the
Specify Grid button is unavailable. However, if a column has a formula and you specify that
column as the response (Y), the Specify Grid button becomes active.
When you click Specify Grid, the window shown in Figure 6.8 appears.
Figure 6.8 Example of the Contour Specification for Formula Column
You can complete the Specify Grid window and define the contour grid in any way, regardless
of the rows in the existing data table. This feature is also available with table templates that
have one or more columns defined by formulas but no rows.
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Additional Examples of Contour Plots
Example of Triangulation, Transform, and Alpha Shapes
This example of a contour plot illustrates how to create a triangulation data table, to transform
the triangulation to use Delaunay triangles, and to filter Alpha shapes of the triangles.
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Contour Plot.
3. Select Ozone and click Y.
4. Select X and Y and click X.
5. Select to Fill Areas.
6. Click OK.
The Contour Plot for Ozone appears.
Note: By default, the contour plot uses Delaunay triangulation for generating the contour
plot. From the Contour Plot red triangle menu, select Transform > Range Normalized to
change the method for calculating the triangulations to a normalized scale ([0,1]) in both X
and Y.
7. From the Contour Plot red triangle menu, select Show Control Panel.
The Alpha slider appears.
Figure 6.9 Contour Plot for Ozone
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Additional Examples of Contour Plots
8. Click and move the Alpha slider to the right.
Figure 6.10 Alpha Shapes Filter
Using the Alpha slider filters out the larger Delaunay triangulation areas.
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Chapter 7
Bubble Plots
View Patterns in Multidimensional Data Using Bubble Plots
A bubble plot is a scatter plot that represents its points as circles, or bubbles. Bubble plots can
be dynamic (animated over time) or static (fixed bubbles that do not move). Use bubble plots
to:
•
dynamically animate bubbles using a time variable, to see patterns and movement across
time
•
use size and color to clearly distinguish between different variables
•
aggregate data (rows) into a single bubble, simplifying the bubble plot
Because you can see up to five dimensions at once (x position, y position, size, color, and
time), bubble plots can produce dramatic visualizations and readily show patterns and trends.
Note: Dynamic bubble plots were pioneered by Hans Rosling, Professor of International
Health, Karolinska Institutet, and the people involved in the Gapminder.org project.
Figure 7.1 Example of a Bubble Plot
Contents
Example of a Dynamic Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Launch the Bubble Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Specifying Two ID Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Specifying a Time Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Interact with the Bubble Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Control Animation for Dynamic Bubble Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Select Bubbles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Use the Brush Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Bubble Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Show Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Additional Examples of the Bubble Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Example of Specifying Only a Time Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Example of Specifying Only ID Variables and Splitting a Bubble . . . . . . . . . . . . . . . . . . . . 139
Example of a Static Bubble Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Example of a Bubble Plot with a Categorical Y Variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
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Example of a Dynamic Bubble Plot
127
Example of a Dynamic Bubble Plot
This example uses the PopAgeGroup.jmp sample data table, which contains population data
for countries and regions around the world. Examine the relationship between the proportion
of younger and older people in the sample populations.
1. Select Help > Sample Data Library and open PopAgeGroup.jmp.
2. Select Graph > Bubble Plot.
The launch window appears.
Figure 7.2 The Bubble Plot Launch Window
3. Select Portion60+ and click Y.
The portion of the population that are 60 years or older becomes the y coordinate.
4. Select Portion 0-19 and click X.
The portion of the population that are 0-19 years becomes the x coordinate.
5. Select Country and click ID.
All the rows for each country are aggregated into a single bubble.
6. Select Year and click Time.
The bubble plot shows a unique plot for each year’s data.
7. Select Pop and click Sizes.
The sizes of the bubbles reflect the overall population values.
8. Select Region and click Coloring.
The bubble for each region is colored differently.
9. Click OK.
The report window appears.
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Figure 7.3 The Bubble Plot Report Window
10. Click the play button to see the animated, dynamic report. Alternatively, you can click the
previous button to move forward by one year.
11. (Optional) To view a legend that identifies each color with its region, select Legend from
the red triangle menu.
As time progresses, you can see that the portion of the population that is 0-19 years decreases,
and the portion of the population that is 60 years or more increases.
Launch the Bubble Plot Platform
Launch the Bubble Plot platform by selecting Graph > Bubble Plot.
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Bubble Plots
Launch the Bubble Plot Platform
Figure 7.4 The Bubble Plot Launch Window
Table 7.1 Description of the Bubble Plot Launch Window
Y, X
The Y and X columns become the y and x coordinates of the
bubbles in the plot. These values can be continuous or
categorical (nominal or ordinal).
ID
(Optional) ID variables identify rows that should be
aggregated and shown as a single bubble. The default
coordinates of each bubble are the averaged x and y values,
and the default size of each bubble is the sum of the sizes of all
aggregated members. See “Specifying Two ID Variables” on
page 130.
Time
Maintains separate coordinates, sizes, and colors for each
unique time period. The bubble plot shows these values for a
single time period. For example, if the Time column contains
years, the bubble plot is updated to show data by each year.
See “Specifying a Time Variable” on page 130.
Sizes
Controls the size of the bubbles. The area of the bubbles is
proportional to the Size value. There is a minimum bubble
size, to keep bubbles visible, even if the size value is zero. If
Size is left blank, the default bubble size is proportional to the
number of rows in that combination of Time and ID.
Coloring
Colors the bubbles according to the selected variable. If the
selected variable is categorical (nominal or ordinal), each
category is colored distinctly. If the selected variable is
continuous, a gradient of colors is used.
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Table 7.1 Description of the Bubble Plot Launch Window (Continued)
By
Place a column here to produce a separate bubble plot for each
level of the variable.
For more information about the launch window, see Using JMP.
After you click OK, the Bubble Plot report window appears.
Specifying Two ID Variables
Specifying a second ID variable provides a hierarchy of categories, but the bubbles are not split
by the second category until they are selected and split interactively. In the launch window, if
you specify a second ID variable, Split and Combine buttons appear in the report window.
For example, you might specify a country as the first ID variable, resulting in a separate
aggregated bubble for each country. A second ID variable, perhaps designating regions within
each country, would further split each country when the interactive Split button under the
graph is pressed.
Specifying a Time Variable
Maintains separate coordinates, sizes, and colors for each unique time period. The bubble plot
shows these values for a single time period. For example, if the Time column contains years,
the bubble plot is updated to show data by each year.
To move the time label on the plot, click and drag the label.
If data is missing within a time period, the value is linearly interpolated. If data is missing for
the first or last time period, the value is not estimated, but left as missing.
Related Information
•
“Control Animation for Dynamic Bubble Plots” on page 131
•
“Example of Specifying Only a Time Variable” on page 137
Interact with the Bubble Plot
Note: Any rows that are excluded in the data table are also hidden in the bubble plot.
Use the Bubble Plot platform in one of two modes:
•
Static mode, where the bubbles are fixed and do not animate over time (no Time variable is
specified). See “Example of a Static Bubble Plot” on page 141.
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•
Bubble Plots
Interact with the Bubble Plot
131
Dynamic mode, where the bubbles are animated over time (a Time variable is specified). See
“Example of a Dynamic Bubble Plot” on page 127.
You interact with both static and dynamic bubble plots in different ways.
Control Animation for Dynamic Bubble Plots
Use sliders and buttons to control the animation of dynamic bubble plots.
Figure 7.5 Animation Controls
Table 7.2 Descriptions of the Animation Controls
Slider or Button
Description
Visibility
<Time variable>
Controls which time values appear in the
bubble plot. You manually drag the slider to
see a progression of time.
Only appears if you have
specified a variable for
Time.
Click and drag on the time variable in the
bubble plot to move its position.
Speed
Adjusts the speed of the animation.
Only appears if you have
specified a variable for
Time.
Bubble Size
Adjusts the size of the bubbles. The bubbles
maintain their relative size, but their
absolute size can be adjusted.
Appears on all bubble
plots.
Adjusts the time value by one unit and
shows the previous time value.
Only appears if you have
specified a variable for
Time.
Press play to animate the bubble plot. Moves
through all of the time values in order, and
loops back to the beginning when the last
time period is reached. Press pause to stop
the animation.
Only appears if you have
specified a variable for
Time.
Adjusts the time value by one unit and
shows the next time value.
Only appears if you have
specified a variable for
Time.
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Table 7.2 Descriptions of the Animation Controls (Continued)
Slider or Button
Description
Visibility
Split
Separates the bubble represented by the first,
larger ID variable into its smaller constituent
parts, which are defined by the second,
smaller ID column.
Only appears if you have
specified two ID
variables.
Select the bubble and click Split.
Combine
Reverses the action of the Split button by
recombining the smaller bubbles back into
their original bubble.
Only appears if you have
specified two ID
variables.
Select any of the smaller bubbles in the
group and click Combine.
Select Bubbles
Click on a bubble to select it. Note the following:
•
Visually, selected bubbles become darker or brighter, and non-selected bubbles are more
transparent.
•
If the bubble was not filled initially, selection fills it.
•
If no bubbles are selected, all of the bubbles are semi-transparent.
When you select a bubble, all of the rows in the data table that correspond to the selected
bubble are highlighted. Note the following:
•
If the bubble is an aggregate based on an ID column, all of the rows for that ID are
highlighted. Otherwise, the one row represented by that bubble is highlighted.
•
If you specify an ID and a Time variable, selecting a bubble highlights all of the rows for
that ID, across all of the Time levels.
If you select a row from the data table, it is selected in the associated bubble plot. Note the
following:
•
If you have not specified a Time variable, selecting one row from the data table highlights
the corresponding bubble in the plot.
•
If you have specified a Time variable, selecting one row from the data table highlights the
corresponding bubble for only that time period in the dynamic bubble plot.
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Use the Brush Tool
Use the brush tool
to temporarily select bubbles and obtain more information about the
selected bubbles. When you select bubbles with the brush tool, the corresponding rows are
highlighted in the associated data table.
Note: For a more granular examination of the highlighted rows, use the Tables > Subset
command or the Row Editor. See the Using JMP book.
Bubble Plot Platform Options
The following table describes the options in the Bubble Plot red triangle menu.
Draw
Set Shape
•
Select Filled to fill all of the bubbles.
•
Select Outlined to outline all of the bubbles.
•
Select Filled and Outlined to fill and outline all of the bubbles.
Change the shape of the bubble.
You can create a custom shape using JSL. The Custom option
opens the custom shape. If no custom shape has been created, the
Custom option uses the default circle shape. For more information
about creating custom shapes, see the Scripting Guide.
Orient Shapes
Orients the shapes as they move in particular directions over time,
following the shape of the data.
This option appears only if you have specified a variable for Time.
Trail Bubbles
Shows the past history of bubbles as a semi-transparent trail. See
“Example of Specifying Only a Time Variable” on page 137.
Note the following:
•
This option appears only if you have specified a variable for
Time.
•
If you do not want to see the bubble labels, select the Label >
None option.
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Trail Lines
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Shows the past history of bubbles as connected line segments. See
“Example of Specifying Only a Time Variable” on page 137.
Note the following:
•
•
This option appears only if you have specified a variable for
Time.
If you do not want to see the bubble labels, select the Label >
None option.
Label
•
Select None to label none of the bubbles in the plot.
•
Select All to label all of the bubbles in the plot.
•
Select Selected to label bubbles only when you select them.
Note: Click and drag on a label to move it.
Color Theme
Change the colors representing the high, middle, and low values
of the color variable.
This option appears only if you have specified a variable for
Coloring.
Revert Color Theme
Reverts back to the original color theme.
This option appears only if you have applied a color theme.
Legend
Shows a legend that describes the colors in the bubble plot.
This option appears only if you have specified a variable for
Coloring.
Selectable Across
Gaps
If a bubble is selected, this option keeps the bubble selected during
time periods where data is missing. Otherwise, the bubble is not
selected during time periods where data is missing.
Show Roles
Shows the variables that are used in the bubble plot. You can
change and delete the variables. See “Show Roles” on page 135.
Split All
Splits all bubbles into their constituent parts. Unlike the Split
button, the bubbles do not have to be selected.
This option appears only if you have specified two ID variables.
Combine All
Combines all constituent bubbles within a group into their larger
bubble. Unlike the Combine button, the bubbles do not have to be
selected.
This option appears only if you have specified two ID variables.
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Lock Scales
Prevents axis scales and gradient legend scales from automatically
adjusting in response to data or filtering changes.
Auto Stretching
Stretches the bubble plot to fill the window when the window is
re-sized.
Aggregation Options
Alters how the X, Y, and Sizes roles are computed. By default, the
values are calculated using means for X and Y, and sums for Sizes.
Save for Adobe Flash
platform (.SWF)
•
Selecting X as Sum or Y as Sum computes the X and Y values
using sums.
•
Deselecting Size as Sum computes Size values using means.
•
Selecting Color as Sum computes the sum of the data values
and maps to a color. This option appears only for continuous
variables.
Saves the bubble plot as .SWF files that are Adobe Flash player
compatible. You can use these files in presentations and in Web
pages. An HTML page is also saved that shows you the correct
code for using the resulting .SWF file.
For more information about this option, go to http://
www.jmp.com/support/swfhelp/en.
Script
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands for
the analysis to a window or a file. For more information, see Using
JMP.
Show Roles
Using the Show Roles option in the red triangle menu, you can make changes to your existing
variables without having to relaunch the platform and start your analysis over.
Follow the instructions in “Example of a Dynamic Bubble Plot” on page 127 to produce the
report window shown in Figure 7.6.
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Figure 7.6 Example of Bubble Plot with Show Roles Selected
o
Change the Variable Assigned to a Role
To change the variable assigned to a role, click on a blue underlined role name. For example,
in Figure 7.6, to change the Coloring variable from Region to Country, proceed as follows:
1. Click on the Coloring link.
The Select column for Coloring window appears.
2. Click on Country.
3. Click OK.
Country now replaces Region as the Coloring variable in the bubble plot.
Remove a Variable
To remove an existing variable from the bubble plot, make sure that nothing is selected in the
Select column for <Role> window, and click OK. For example, in Figure 7.6, to remove the
Sizes variable (Pop), proceed as follows:
1. Click on the Sizes link.
The Select column for Sizes window appears.
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Additional Examples of the Bubble Plot Platform
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2. Ensure that nothing is selected. If a variable is selected, deselect it by holding down the
CTRL key and clicking on the variable.
3. Click OK.
The Sizes role now appears with an empty box.
Note: The X and Y variables can be changed only and cannot be removed.
Add a Variable
Once you have removed an existing variable from the bubble plot, there are two ways to add a
new variable:
•
Click on the blue underlined role name. See “Change the Variable Assigned to a Role” on
page 136.
•
In the data table, click on the variable in the column panel, and drag it into the empty role
box.
Additional Examples of the Bubble Plot Platform
The following examples further illustrate the features of the Bubble Plot platform.
Example of Specifying Only a Time Variable
For dynamic bubble plots, you might specify only a Time variable and no ID variable. The
resulting bubble plot contains a single moving bubble that tracks the series as the Time value
changes.
1. Select Help > Sample Data Library and open PopAgeGroup.jmp.
2. Select Graph > Bubble Plot.
3. Select Portion60+ and click Y.
4. Select Portion 0-19 and click X.
5. Select Year and click Time.
6. Select Region and click Coloring.
7. Click OK.
The initial report window appears.
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Figure 7.7 The Initial Report Window with a Time Variable
8. Click on the bubble to select it.
All rows in the data table are also highlighted.
9. From the red triangle menu, select Trail Bubbles > All and Trail Lines > All.
10. Click the play button.
The bubble plot animates, showing a trail for the single bubble.
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Additional Examples of the Bubble Plot Platform
139
Figure 7.8 Animated Bubble Plot over Time
Example of Specifying Only ID Variables and Splitting a Bubble
For static bubble plots, you might specify one or two ID variables and no Time variable. The
resulting bubble plot contains a bubble at each ID value. Note that although this bubble plot is
static, you can perform splitting on bubbles.
1. Select Help > Sample Data Library and open PopAgeGroup.jmp.
2. Select Graph > Bubble Plot.
3. Select Portion60+ and click Y.
4. Select Portion 0-19 and click X.
5. Select Region and Country and click ID.
6. Select Region and click Coloring.
7. Click OK.
The initial report window appears.
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Figure 7.9 Initial Report Window with ID Variables
Split the bubble representing the region of North America into countries.
8. Click on the bubble representing North America (hover over a bubble to see its label, or
use the legend to find the color of North America.)
9. Click Split.
You see that the North America bubble has split into three bubbles, representing the
countries within the region of North America (the United States of America, Canada, and
Mexico).
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Figure 7.10 Splitting the North America Bubble
Example of a Static Bubble Plot
This example uses the SATByYear.jmp sample data table, which contains SAT verbal and math
test scores for a selection of the US population in 2004.
1. Select Help > Sample Data Library and open SATByYear.jmp.
2. Select Graph > Bubble Plot.
3. Select SAT Verbal and click Y.
4. Select SAT Math and click X.
5. Select State and click ID.
6. Select % Taking (2004) and click Sizes.
7. Click OK.
The report window appears.
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Figure 7.11 The Static Bubble Plot Report Window
From Figure 7.11, you draw the following conclusions:
•
Higher verbal scores appear to be associated with higher math scores, since the two track
very closely in the bubble plot. This signifies a correlation between verbal and math scores.
•
The larger bubbles represent the US states that have a high percentage of individuals
taking the SAT test in 2004. These larger bubbles are all grouped together in the lower left
of the graph. This shows that when a state has a high percentage of individuals taking the
test, both the math and verbal scores are low.
Instead of grouping the bubbles primarily by state, group the bubbles primarily by region as
follows:
1. From the red triangle menu, select Show Roles.
2. Click on the ID link.
3. Select Region, and click OK.
Region is now the primary ID variable.
4. Click on the ID2 link.
5. Select State, and click OK.
State is now the secondary ID variable.
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Figure 7.12 Example of Bubble Plot Grouped by Region and State
6. Click on the bubble that represents the Southwest region (hover over a bubble or click on it
to see its label).
7. Click Split.
Now the bubbles are split by the secondary ID variable, which is State. You now see each
state within the Southwest region.
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Figure 7.13 Example of Southwest Region Split by State
From Figure 7.13, you see that there is significant variation between the scores from the
Southwest states.
8. Click Combine to combine the southwest states again.
9. To do a comparison, click on the New England bubble (hover over a bubble or click on it to
see its label).
10. Click Split.
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Figure 7.14 Example of New England Region Split by State
You see that the New England states do not have as much variation as the Southwest states.
Example of a Bubble Plot with a Categorical Y Variable
All of the examples shown so far use continuous Y variables. If you use a categorical (nominal
or ordinal) Y variable, the bubble plot appears differently.
This example uses the blsPriceData.jmp sample data table, which shows the price of
commodities over several years. Because the value of the US dollar changes over time, a
column named Price/Price2000 shows the ratio of a commodity’s price at any given time to the
price in the year 2000.
1. Select Help > Sample Data Library and open blsPriceData.jmp.
2. Select Graph > Bubble Plot.
3. Select Series and click Y.
4. Select Price/Price2000 and click X.
5. Select date and click Time.
6. Click OK.
The report window appears.
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Additional Examples of the Bubble Plot Platform
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This produces a bubble plot that, when animated by clicking the play button, shows the
price bubbles moving side to side according to their price ratio.
Figure 7.15 Static Example of Animated Bubbles
For easier readability, add grid lines as follows:
7. Double-click on the categorical axis.
8. In the Y Axis Specification window, select the box under Gridline next to Major.
9. Click OK.
To animate the bubble plot, click the play button. The price bubbles move side to side,
according to their price ratio.
Chapter 8
Parallel Plots
View Patterns in Multidimensional Data by Plotting Parallel
Coordinates
Using parallel plots, you can visualize each cell in a data table. Parallel plots draw connected
line segments that represent each row in a data table. Parallel plots were initially developed by
Inselberg (1985) and later popularized by Wegman (1990).
Figure 8.1 Example of a Parallel Plot
Contents
Example of a Parallel Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Launch the Parallel Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
The Parallel Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Interpreting Parallel Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Parallel Plot Platform Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Additional Examples of the Parallel Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Examine Iris Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Examine Student Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
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Example of a Parallel Plot
149
Example of a Parallel Plot
This example uses the Dogs.jmp sample data table, which contains histamine level
measurements for 16 dogs that were given two different drugs. The histamine levels were
taken at zero, one, three, and five minutes. Examine the variation in the histamine levels for
each drug.
1. Select Help > Sample Data Library and open Dogs.jmp.
To see the differences by drug, color the parallel plot lines by drug:
2. Select Rows > Color or Mark by Column.
3. Select drug and click OK.
Morphine appears red and trimeth appears blue.
Create the parallel plot:
4. Select Graph > Parallel Plot.
5. Select hist0, hist1, hist3, and hist5 and click Y, Response.
6. Click OK.
The report window appears.
Figure 8.2 Parallel Plot of Histamine Variables
Each connected line segment represents a single observation. Click on a line segment to see
which observation (or row) it corresponds to in the data table.
For further exploration, isolate the trimeth values:
7. Select Rows > Data Filter.
8. Select drug and click Add.
9. Select trimeth.
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Only the trimeth values are highlighted in the parallel plot.
Figure 8.3 Trimeth Values Highlighted
From Figure 8.3, you observe the following about the histamine levels for dogs given trimeth:
•
For most of the dogs, the histamine levels had a sharp drop at one minute.
•
For four of the dogs, the histamine levels remained high, or rose higher. You might
investigate this finding further, to determine why the histamine levels were different for
these dogs.
Launch the Parallel Plot Platform
Launch the Parallel Plot platform by selecting Graph > Parallel Plot.
Figure 8.4 The Parallel Plot Launch Window
Chapter 8
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Parallel Plots
The Parallel Plot
151
Table 8.1 Description of the Parallel Plot Launch Window
Y, Response
Variables appear on the horizontal axis of the parallel plot. These
values are plotted and connected in the parallel plot.
X, Grouping
Produces a separate parallel plot for each level of the variable.
By
Identifies a column that creates a report consisting of separate
analyses for each level of the specified variable.
Scale Uniformly
Represents all variables on the same scale, adding a y-axis to the
plot. Without this option, each variable is on a different scale.
To allow for proper comparisons, select this option if your
variables are measured on the same scale.
Center at zero
Centers the parallel plot (not the variables) at zero.
For more information about the launch window, see Using JMP.
After you click OK, the Parallel plot appears. See “The Parallel Plot” on page 151.
The Parallel Plot
To produce the plot shown in Figure 8.5, follow the instructions in “Example of a Parallel
Plot” on page 149.
Figure 8.5 The Parallel Plot Report
A parallel plot is one of the few types of coordinate plots that show any number of variables in
one plot. However, the relationships between variables might be evident only in the following
circumstances:
•
when the variables are side by side
152
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The Parallel Plot
•
if you assign a color to a level of a variable to track groups
•
if you select lines to track groups
Chapter 8
Essential Graphing
Note: If the columns in a parallel plot use the Spec Limits column property, the specification
limits appear as red lines.
Interpreting Parallel Plots
To help you interpret parallel plots, compare the parallel plot with a scatterplot. In each of the
following figures, the parallel plot appears on the left, and the scatterplot appears on the right.
Strong Positive Correlation
The following relationship shows a strong positive correlation. Notice the coherence of the
lines in the parallel plot.
Figure 8.6 Strong Positive Correlation
Strong Negative Correlation
A strong negative correlation, by contrast, shows a narrow neck in the parallel plot.
Figure 8.7 Strong Negative Correlation
[
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Parallel Plots
Parallel Plot Platform Options
153
Collinear Groups
Now, consider a case that encompasses both situations: two groups, both strongly collinear.
One has a positive slope, the other has a negative slope. In Figure 8.8, the positively sloped
group is highlighted.
Figure 8.8 Collinear Groups: Parallel Plot and Scatterplot
Single Outlier
Finally, consider the case of a single outlier. The parallel plot shows a general coherence
among the lines, with a noticeable exception.
Figure 8.9 Single Outlier: Parallel Plot and Scatterplot
Related Information
•
“Additional Examples of the Parallel Plot Platform” on page 154
Parallel Plot Platform Options
The following table describes the options within the red triangle menu for Parallel Plot.
Show Reversing
Checkboxes
Reverses the scale for one or more variables.
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Additional Examples of the Parallel Plot Platform
Script
Chapter 8
Essential Graphing
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands for
the analysis to a window or a file. For more information, see Using
JMP.
For details about the context menu options that appear when you right-click on a parallel plot,
see Using JMP.
Additional Examples of the Parallel Plot Platform
The following examples further illustrate using the Parallel Plots platform.
Examine Iris Measurements
The following example uses the Fisher’s Iris data set (Mardia, Kent, and Bibby 1979). The
Iris.jmp sample data table contains measurements of the sepal length and width and petal
length and width in centimeters for three species of Iris flowers: setosa, versicolor, and
virginica. To find characteristics that differentiate the three species, examine these
measurements.
Examine Three Species in One Parallel Plot
1. Select Help > Sample Data Library and open Iris.jmp.
2. Select Graph > Parallel Plot.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Response.
4. Select the Scale Uniformly check box.
5. Click OK.
The report window appears.
Figure 8.10 Three Species in One Parallel Plot
Chapter 8
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Additional Examples of the Parallel Plot Platform
155
In this parallel plot, the three species are all represented in the same plot. The colors
correspond to the three species, as follows:
•
Blue corresponds to virginica.
•
Green corresponds to versicolor.
•
Red corresponds to setosa.
From Figure 8.10, you observe the following:
•
For sepal width, the setosa values appear to be higher than the virginica and versicolor
values.
•
For petal width, the setosa values appear to be lower than the virginica and versicolor
values.
Examine Three Species in Different Parallel Plots
1. From the Iris.jmp sample data table, select Graph > Parallel Plot.
2. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Response.
3. Select Species and click X, Grouping.
4. Click OK.
The report window appears.
Figure 8.11 Three Species in Different Parallel Plots
Each species is represented in a separate parallel plot.
Examine Student Measurements
The following example uses the Big Class.jmp sample data table, which contains data on age,
sex, height, and weight for 40 students. Examine the relationships between different variables.
1. Select Help > Sample Data Library and open Big Class.jmp.
2. Select Graph > Parallel Plot.
3. Select height and weight and click Y, Response.
4. Select age and click X, Grouping.
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5. Select sex and click By.
6. Select the Scale Uniformly check box.
7. Click OK.
Figure 8.12 Height and Weight by Sex, Grouped by Age
From Figure 8.12, you observe the following:
•
Among the 13-year-old females, one female’s weight is lower than the other females in her
age group. If you click on the line representing the lower weight, the respective individual
(Susan) is highlighted in the data table.
•
Among the 14-year-old females, one female’s weight is higher than the other females in her
age group. If you click on the line representing the higher weight, the respective
individual (Leslie) is highlighted in the data table.
Chapter 9
Cell Plots
View Color-Intensity Plots of Variables
Using cell plots, you can visualize each cell in a data table. Cell plots are direct representations
of a data table, since they draw a rectangular array of cells where each cell corresponds to a
data table entry. Cell plots were popularized by genomics applications to browse large
numbers of values for gene expression levels.
Figure 9.1 Example of a Cell Plot
Contents
Example of a Cell Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Launch the Cell Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
The Cell Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Cell Plot Platform Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Context Menu for Cell Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Additional Example of the Cell Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Chapter 9
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Cell Plots
Example of a Cell Plot
159
Example of a Cell Plot
This example uses the Dogs.jmp sample data table, which contains histamine level
measurements for 16 dogs that were given two different drugs. The histamine levels were
taken at zero, one, three, and five minutes. Examine the variation in the histamine levels for
each drug.
1. Select Help > Sample Data Library and open Dogs.jmp.
2. Select the third row from the bottom (row 14).
3. Select Graph > Cell Plot.
4. Select drug, hist0, hist1, hist3, and hist5 and click Y, Response.
5. Click OK.
The report window appears.
Figure 9.2 Dogs.jmp cell plot
two colors
for drug
missing value
selected row
From Figure 9.2, notice the following:
•
There are two types of drugs, represented by two distinct colors.
•
Histamine levels are assigned colors from a gradient of blue to red.
•
Any missing values are delineated by an X.
•
The third row from the bottom is selected, and black lines appear next to the cells.
Launch the Cell Plot Platform
Launch the Cell Plot platform by selecting Graph > Cell Plot.
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The Cell Plot
Chapter 9
Essential Graphing
Figure 9.3 The Cell Plot Launch Window
Table 9.1 Description of the Cell Plot Launch Window
Y, Response
Variables appear on the horizontal axis of the cell plot. Each cell
represents a value.
X, Grouping
Produces a separate cell plot for each level of the variable.
Label
Labels each row by the specified variable. See “Additional
Example of the Cell Plot Platform” on page 163.
By
Identifies a column that creates a report consisting of separate
analyses for each level of the variable.
Scale Uniformly
Represents all variables on the same scale. Without this option,
each variable is on a different scale.
Center at zero
Centers the cell plot at zero.
For more information about the launch window, see Using JMP.
After you click OK, the Cell Plot window appears. See “The Cell Plot” on page 160.
The Cell Plot
To produce the plot shown in Figure 9.4, follow the instructions in “Example of a Cell Plot” on
page 159.
Chapter 9
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Cell Plots
Cell Plot Platform Options
161
Figure 9.4 The Cell Plot Report Window
Note: Any rows that are excluded in the data table are also hidden in the cell plot.
Cell plots are direct representations of a data table, drawn as a rectangular array of cells with
each cell corresponding to a data table entry. Colors are assigned to each cell based on the
range and type of values found in the column.
•
Nominal variables use a distinct color for each level. You can customize nominal and
ordinal colors using the Value Colors property of data columns, available through the
Column Info command.
•
Continuous variables are assigned a gradient of colors to show the smooth range of values
in the variable.
•
Ordinal variables are scaled like continuous variables in order.
•
When some outliers are present, the scale uses all but the extreme categories for the 90%
middle of the distribution, so that the outliers do not overly influence the scale.
The cell plot appears with a one-to-one correspondence of a colored cell representing each
data table entry.
Related Information
•
“Additional Example of the Cell Plot Platform” on page 163
Cell Plot Platform Options
The following table describes the options within the red triangle menu for Cell Plot.
Legend
Shows or hides a legend.
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Arrange Plots
Chapter 9
Essential Graphing
Specifies how many plots to put on the same row before starting
the next row of plots.
This option is available only if you specify an X, Grouping variable.
Script
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands for
the analysis to a window or a file. For more information, see Using
JMP.
Context Menu for Cell Plots
The following table describes the options that appear when you right-click on a cell plot.
Note: For details about the context options that appear when you right-click on labels, see the
Using JMP book.
Graph Type
Determines the appearance of the graph. See “Graph Type” on
page 163.
Color Theme
Shows a list of color themes that affect continuous variables in
color maps. The default color theme is Blue to Gray to Red
(corresponding to small values to middle values to large values).
Use White to Black to create a gray-scale plot.
Note: To see custom colors, you must first create them. Select
File > Preferences > Graphs. In the Color Themes area, click the
type of color theme that you want to create, click New, and then
change the colors. See Using JMP for details about creating custom
color themes.
Sort Ascending
Sorts the rows of the plot from lowest to highest by the values of a
column. To sort, right-click in the plot under a column and select
Sort Ascending. The entire plot is rearranged to accommodate the
sorting. See “Additional Example of the Cell Plot Platform” on
page 163.
Sort Descending
Sorts the rows of the plot from highest to lowest by the values of a
column. To sort, right-click in the plot under a column and select
Sort Descending. The entire plot is rearranged to accommodate the
sorting.
No Separator Lines
Draws or removes lines separating the columns.
Chapter 9
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Cell Plots
Additional Example of the Cell Plot Platform
163
Graph Type
Use the Graph Type option to change the appearance of the graph.
Figure 9.5 Graph Types
color map
dot plot
VLine plot
HLine plot
HBar plot
Additional Example of the Cell Plot Platform
This example uses the SAT.jmp sample data table, which contains SAT test scores (divided into
verbal and mathematics portions) for all 50 United States.
1. Select Help > Sample Data Library and open SAT.jmp.
2. Select Graph > Cell Plot.
3. Select all of the Verbal scores for all of the years, and click Y, Response.
4. Select all of the Math scores for all of the years, and click Y, Response.
5. Select State and click Label.
6. Click OK.
The report window appears.
7. Right-click on the plot under 2004 Verbal (the top left cell) and select Sort Ascending.
This sorts the cell plot by the verbal scores for 2004.
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Essential Graphing
Figure 9.6 Cell Plot for SAT Scores
From Figure 9.6, you notice the following:
•
Hawaii has the lowest verbal scores for 2004, and South Dakota has the highest verbal
scores for 2004.
•
There is a contrast between Hawaii’s math and verbal scores. Hawaii has average math
scores (represented by gray color values) but low verbal scores (represented by blue color
values). Hawaii appears to be an outlier, since it has a strikingly different pattern for its
math scores and its verbal scores.
•
There is very little contrast between North Dakota’s math and verbal scores. North
Dakota’s math and verbal scores are generally high (represented by red color values).
For a description of color themes, see “Context Menu for Cell Plots” on page 162.
Chapter 10
Treemaps
View Multi-Level Categorical Data in a Rectangular Layout
Treemaps are useful for observing patterns among groups that have many levels. Treemaps
are like bar charts that have been folded over in two dimensions so that there is no unused
space. Rather than drawing a single bar for each measurement, a treemap can show the
magnitude of a measurement by varying the size or color of a rectangular area. Use treemaps
when your data contains many categories, to visualize many groups.
Treemaps were named and popularized by Ben Schneiderman, who has an extensive website
about the idea (http://www.cs.umd.edu/hcil/treemap-history/index.shtml)
Figure 10.1 Example of a Treemap
Contents
Example of Treemaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Launch the Treemap Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Categories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
The Treemap Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Treemap Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Context Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Additional Examples of the Treemap Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Example Using a Sizes Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Example Using an Ordering Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Example Using Two Ordering Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Example of a Continuous Coloring Variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Example of a Categorical Coloring Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
Examine Pollution Levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Examine Causes of Failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Examine Patterns in Car Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
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Treemaps
Example of Treemaps
167
Example of Treemaps
Treemaps can be useful in cases where histograms or bar charts are ineffective. This example
uses the Cities.jmp sample data table, which contains meteorological and demographic
statistics for 52 cities. Compare the bar chart to the treemap.
Create a bar chart representing ozone levels in each of the 52 cities:
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Chart.
The launch window appears.
3. Select OZONE and click Statistics.
4. Select Mean.
5. Select city and click Categories, X, Levels.
6. Click OK.
The report window appears.
Figure 10.2 Ozone Levels in a Bar Chart
Although it is easy to see that there is a single large measurement, each bar looks similar.
Subtle distinctions are difficult to see.
Create a treemap representing ozone levels in each of the 52 cities:
1. Return to the Cities.jmp sample data table.
2. Select Graph > Treemap.
The launch window appears.
3. Select POP (population) and click Sizes.
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4. Select city and click Categories.
5. Select OZONE and click Coloring.
6. Click OK.
The report window appears.
Figure 10.3 Ozone Levels in a Treemap
Compare the bar chart to the treemap. Because the treemap folds the data over two
dimensions (size and color), each city’s data looks more distinctive than it did in the bar chart.
Note the following about this treemap:
•
The magnitude of the ozone level for each city is represented by color.
•
Each rectangle is colored based on a continuous color spectrum, with bright blue on the
lowest end and bright red on the highest end. In this treemap, Des Moines has the lowest
ozone levels, and Los Angeles has the highest ozone levels.
•
Ozone levels somewhere in the middle decrease the intensity of the color, so pale blue, and
pink indicate levels that are closer to the mean ozone level.
•
Cities colored black have missing ozone values.
Launch the Treemap Platform
Launch the Treemap platform by selecting Graph > Treemap.
Chapter 10
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Treemaps
Launch the Treemap Platform
169
Figure 10.4 The Treemap Launch Window
Table 10.1 Description of the Treemap Launch Window
Sizes
Determines the size of the rectangles based on the values of the
specified variable. See “Sizes” on page 170.
Categories
(Required) Specifies the category that comprises the treemap. See
“Categories” on page 170.
Ordering
Changes the ordering from alphabetical (where values progress
from the top left to the lower right) to order by the specified
variable. You can specify more than one ordering variable. See
“Ordering” on page 170.
Coloring
Colors the rectangles corresponding to the levels of the specified
variable.
•
If the variable is continuous, the colors are based on a
continuous color spectrum.
•
If the variable is categorical, the default colors are selected in
order from JMPs color theme.
See “Coloring” on page 170.
By
Identifies a column that creates a report consisting of separate
treemaps for each level of the variable.
Layout
Determines the layout of the rectangles. See “Layout” on page 173.
For more information about the launch window, see Using JMP.
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After you click OK, the Treemap window appears. See “The Treemap Window” on page 171.
Sizes
If you want the size of the rectangles to correspond to the levels of a variable, specify a Sizes
variable. The rectangle size is proportional to the sum of the Sizes variable across all of the
rows corresponding to a category. If you do not specify a Sizes variable, the rectangle size is
proportional to the number of rows for each category.
Categories
The only required variable role for the Treemap platform is Categories. If you specify only a
Categories variable and no other variables, the rectangles in the treemap have these attributes:
•
They are colored from a rotating color theme.
•
They are arranged alphabetically.
•
They are sized by the number of occurrences in each group.
If you specify two Categories variables, the treemap is grouped by the first variable, and sorts
within groups by the second variable. For example, using the Cities.jmp sample data table,
specify Region and city (in that order) as the Categories variables.
Ordering
By default, the rectangles in a treemap appear in alphabetical order. Values progress from the
top left to the lower right. To change this ordering, specify an Ordering variable. When an
Ordering variable is specified, the rectangles appear with the values progressing from the
bottom left to the upper right.
If you specify a single Ordering variable, the rectangles are clustered, with the high levels or
large values together, and the low levels or small values together.
If you specify two Ordering variables, the treemap arranges the rectangles horizontally by the
first ordering variable, and vertically by the second ordering variable. This approach can be
useful for geographic data.
Coloring
If you specify a Coloring variable, the colors of the rectangles correspond to the levels of the
variable.
•
If the variable is continuous, the colors are based on a continuous color theme setting. The
default color theme is Blue to Gray to Red. The color of each value is based on the average
value of all of the rows. Blue represents the lowest values, and red represents the highest
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The Treemap Window
171
values. The color is most intense at the extremes of the variable, and paler colors
correspond to levels that are close to the mean. For example, see “The Treemap Window”
on page 171.
•
If the variable is categorical, the colors are based on a categorical color theme. The default
color theme is JMP Default. For an example, see “Example of a Categorical Coloring
Variable” on page 178.
Note: If you have used the Value Colors column property to color a column, that property
determines the colors of the categories.
•
If you do not specify a Coloring variable, colors are chosen from a rotating color palette.
Related Information
•
“Example Using a Sizes Variable” on page 174
•
“Example Using an Ordering Variable” on page 175
•
“Example Using Two Ordering Variables” on page 176
•
“Example of a Continuous Coloring Variable” on page 177
•
“Example of a Categorical Coloring Variable” on page 178
•
“Treemap Platform Options” on page 173
The Treemap Window
To produce the plot shown in Figure 10.5, follow the instructions in “Example of Treemaps”
on page 167.
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Figure 10.5 The Treemap Window
Tip: To zoom in on the treemap, use the Magnifier tool or press the Z key.
Treemap rectangles can have the following attributes:
•
Categories add labels to the rectangles. You can specify one or two categories. You can
show or hide labels using the Show Labels option. See “Context Menu” on page 174.
•
Rectangle size is determined by one of the following:
‒ The Sizes variable, if you specify one.
‒ If you do not specify a Sizes variable, size is determined by the frequency of the
category.
•
Rectangle color is determined by one of the following:
‒ If the variable is continuous, the colors are based on a continuous color theme setting.
The default color theme is Blue to Gray to Red. The color of each value is based on the
average value of all of the rows. Blue represents the lowest values, and red represents
the highest values. The color is most intense at the extremes of the variable, and paler
colors correspond to levels that are close to the mean. For example, see “Example of a
Continuous Coloring Variable” on page 177.
‒ If the variable is categorical, the colors are based on a categorical color theme. The
default color theme is JMP Default. For example, see “Example of a Categorical
Coloring Variable” on page 178.
‒ If you do not specify a Coloring variable, colors are chosen from a rotating color theme.
‒ If you have used the Value Colors column property to color a column, that property
determines the colors of the categories.
•
The order of the rectangles is determined by one of the following:
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‒ The Ordering variable, if you specify one.
‒ If you do not specify an Ordering variable, the order is alphabetical by default. Values
progress from the top left to the lower right.
Related Information
•
“Additional Examples of the Treemap Platform” on page 174
Treemap Platform Options
The following table describes the options within the red triangle menu next to Treemap.
Change Color Column
Change the column that is currently used to color the rectangles.
Color Theme
Change the colors representing the high, middle, and low values
of the color column. This option is available only if you have
specified a Coloring variable.
Color Range
Specify the range that you want applied to the color gradient. The
default low value is the column minimum, and the default high
value is the column maximum. This option is available only if you
have specified a continuous column as the Coloring variable.
Legend
Shows or hides a legend that defines the coloring used on the
treemap. This option is available only if you have specified a
Coloring variable.
Layout
Arranges rectangles by order of the variable or by size of the
rectangle.
Script
•
Split preserves the order of the data. Split is the default setting.
•
Squarify sorts the data first. The largest value is in the top left
corner. The rectangle sizes decrease diagonally to the lower
right corner. The order of the variables is not preserved, but
visually comparing variables is easier.
•
Mixed preserves the order of the data for the main category
and then sorts within the subcategory. Applies only when you
select a category and subcategory.
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands for
the analysis to a window or a file. For more information, see Using
JMP.
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Context Menu
The following table describes the options available by right-clicking on the treemap.
Suppress Box Frames
Suppresses the black lines outlining each box.
Ignore Group
Hierarchy
Flattens the hierarchy and sorts by the Ordering columns without
using grouping, except to define cells.
Show Labels
Shows or hides the Categories labels. If you have specified two
Categories, the secondary labels are hidden or shown. If you hide
the Categories labels, place your mouse pointer over a rectangle to
show the primary or secondary category label.
Show Group Labels
Shows or hides the group labels. This option is available only if
more than one variable is assigned to the Categories role.
Group Label
Background
Adjust the transparency of the group label. This option is available
only if more than one variable is assigned to the Categories role.
Additional Examples of the Treemap Platform
The following examples further illustrate using the Treemap platform.
Example Using a Sizes Variable
Using the Cities.jmp sample data table, specify a Sizes variable to see a treemap of city sizes
based on population.
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select POP (population) and click Sizes.
4. Select city and click Categories.
5. Click OK.
The report window appears.
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Figure 10.6 POP as Sizes Variable
•
Cities with large populations are represented by large rectangles, such as New York and
Los Angeles.
•
Cities with smaller populations are represented by smaller rectangles, such as Cheyenne
and Dubuque.
Some rectangles are too small to show their labels (for example, Cheyenne and Dubuque).
Click on a small rectangle to select the corresponding row in the data table, where you can see
the label.
Example Using an Ordering Variable
For example, using the Cities.jmp sample data table, specify an Ordering variable as follows:
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select city and click Categories.
4. Select POP (population) and click Ordering.
5. Click OK.
The report window appears. Since you specified an Ordering variable, the cities in the
treemap are ordered from bottom left (small cities) to upper right (big cities).
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Figure 10.7 POP as Ordering Variable
large values
(big cities)
small values
(small cities)
Example Using Two Ordering Variables
For example, in the Cities.jmp sample data table, the X and Y columns correspond to the
geographic location of the cities. Specify the X and Y columns as Ordering variables:
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select city and click Categories.
4. Select X and click Ordering.
The X variable corresponds to the western and eastern US states.
5. Select Y and click Ordering.
The Y variable corresponds to the northern and southern US states.
6. Click OK.
The report window appears.
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177
Figure 10.8 Treemap with Two Ordering Variables
northern states
southern states
western states
eastern states
The western and eastern US states are arranged horizontally, and the northern and southern
US states are arranged vertically.
Example of a Continuous Coloring Variable
For example, using the Cities.jmp sample data table, specify a continuous Coloring variable as
follows:
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select city and click Categories.
4. Select OZONE and click Coloring.
5. Click OK.
The report window appears.
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Figure 10.9 City Colored by OZONE
Note that the size of the rectangles is still based on the number of occurrences of the
Categories variable, but the colors are mapped to ozone values. The high ozone value for Los
Angeles clearly stands out. Missing values appear as black rectangles.
Example of a Categorical Coloring Variable
For example, using the Cities.jmp sample data table, specify a categorical Coloring variable as
follows:
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select city and click Categories.
4. Select Region and click Coloring.
5. Click OK.
The report window appears.
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Figure 10.10 City Colored by Region
All of the cities belonging to the same region are colored the same color. The colors are chosen
from JMP’s color theme.
6. From the red triangle menu, select Layout > Squarify.
Figure 10.11 Squarify Treemap
The squares are now ordered according to size from the top left corner to the smallest
rectangle in the lower right corner. This makes the data easier to analyze.
To order the squares using a combination of Split and Squarify, select the Mixed layout option.
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Examine Pollution Levels
Using the Cities.jmp sample data table, examine the distribution of different pollution
measurements (ozone and lead) across selected cities in the United States.
First, examine ozone levels in the selected cities.
1. Select Help > Sample Data Library and open Cities.jmp.
2. Select Graph > Treemap.
3. Select POP (population) and click Sizes.
4. Select city and click Categories.
5. Select X and Y and click Ordering.
6. Select OZONE and click Coloring.
7. Click OK.
The report window appears.
Figure 10.12 OZONE Levels for Selected Cities
From Figure 10.12, you observe the following:
•
Los Angeles has a high level of ozone. It is also a western state, and that is reflected by its
positioning in the treemap.
•
Chicago and Houston have slightly elevated ozone levels.
•
New York and Washington have slightly lower-than-average ozone levels.
Next, examine lead levels in the selected cities. Perform the same steps as before, but
substitute Lead with OZONE for the Coloring variable.
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Figure 10.13 Lead Levels for Selected Cities
From Figure 10.13, you observe the following:
•
Cleveland and Seattle have high levels of lead. Cleveland is near the middle of the country,
and Seattle is in the northwest. These locations are reflected by their positioning in the
treemap.
•
Interestingly, most other cities have rather low lead levels.
•
Raleigh and Phoenix have missing values for lead measurements.
Examine Causes of Failure
This example uses the Failure3.jmp sample data table, which contains the common causes of
failure during the fabrication of integrated circuits. Examine the causes of failure and when it
occurs.
1. Select Help > Sample Data Library and open Quality Control/Failure3.jmp.
2. Select Graph > Treemap.
3. Select N and click Sizes.
4. Select failure and clean and click Categories.
5. Select clean and click Coloring.
6. Click OK.
The report window appears.
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Figure 10.14 Failure Modes
From Figure 10.14, you observe the following:
•
Contamination is the biggest cause of failure.
•
Contamination occurs more often before the circuits were cleaned, rather than after they
were cleaned.
Examine Patterns in Car Safety
This example uses the Cars.jmp sample data table, which contains impact measurements of
crash-test dummies in automobile safety tests. Compare these measurements for different
automobile makes and models during the years 1990 and 1991.
1. Select Help > Sample Data Library and open Cars.jmp.
Filter the data to show only the years 1990 and 1991 and create a subset of the Cars.jmp data
table.
2. Select Rows > Data Filter.
3. Select Year.
4. Click Add.
5. Select 90 and 91.
The rows corresponding to 1990 and 1991 are highlighted in the data table.
6. Select Tables > Subset.
7. Ensure that Selected Rows is selected and click OK.
A new data table (Subset of Cars) appears that contains only the data corresponding to the
years 1990 and 1991.
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Now, using the Subset of Cars data table, create the treemap.
1. Select Graph > Treemap.
2. Select Wt (weight) and click Sizes.
3. Select Make and Model and click Categories.
4. Select L Leg and click Coloring.
L Leg represents a measurement of injuries resulting from the deceleration speed of the left
leg, where more deceleration causes more injury.
5. Click OK.
The report window appears.
Figure 10.15 Left Leg Deceleration Injuries
From Figure 10.15, you can see that the Club Wagon and S10 Pickup 4x4 have the largest
number of left leg deceleration injuries.
You can examine other safety measurements without re-launching the Treemap platform, as
follows:
1. From the red triangle menu, select Change Color Column.
2. Select Head IC.
3. Click OK.
The treemap updates to reflect head injuries instead of left leg injuries.
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Figure 10.16 Head Injuries
From Figure 10.16, you notice the following:
•
Although the S10 Pickup 4x4 had a high number of left leg deceleration injuries, it has a
lower number of head injuries.
•
The Club Wagon still has a high number of head injuries, in addition to the high number of
left leg deceleration injuries.
•
The Trooper II 4x4 had a low number of left leg deceleration injuries, but it has a high
number of head injuries.
Chapter 11
Scatterplot Matrix
View Multiple Bivariate Relationships Simultaneously
Using the Scatterplot Matrix platform, you can assess the relationships between multiple
variables simultaneously. A scatterplot matrix is an ordered collection of bivariate graphs. For
further analysis, you can customize the scatterplots with density ellipses for all of your data,
or for only groups of your data.
Figure 11.1 Example of a Scatterplot Matrix
Contents
Example of a Scatterplot Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Launch the Scatterplot Matrix Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Change the Matrix Format. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
The Scatterplot Matrix Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Scatterplot Matrix Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Example Using a Grouping Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Create a Grouping Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
Chapter 11
Essential Graphing
Scatterplot Matrix
Example of a Scatterplot Matrix
Example of a Scatterplot Matrix
This example shows you how to create a scatterplot matrix.
1. Select Help > Sample Data Library and open Students.jmp.
2. Select Graph > Scatterplot Matrix.
3. Select age, sex, height, and weight and click Y, Columns.
4. Click OK.
Figure 11.2 Example of a Scatterplot Matrix
In this example, you can see that the graph for weight versus height is different from the
graph for sex versus age. If you turn off jitter by clicking on the red triangle menu and
selecting Points Jittered, the difference becomes even more pronounced.
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Launch the Scatterplot Matrix Platform
Chapter 11
Essential Graphing
Figure 11.3 Example of a Scatterplot Matrix with No Jitter
Categorical
Continuous
The weight versus height graph shows continuous data, and the sex versus age graph shows
categorical data.
Launch the Scatterplot Matrix Platform
Launch the Scatterplot Matrix platform by selecting Graph > Scatterplot Matrix.
Figure 11.4 The Scatterplot Matrix Launch Window
Chapter 11
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Scatterplot Matrix
Launch the Scatterplot Matrix Platform
189
Table 11.1 Description of the Scatterplot Matrix Launch Window
Y, Columns, X
•
If you assign variables to the Y, Columns role only, they appear
on both the horizontal and vertical axes.
•
If you assign variables to both the Y, Columns and X role, then
the Y, Columns variables appear on the vertical axis. The X
variables appear on the horizontal axis. This approach enables
you to produce rectangular matrices, or matrices that have
different, yet overlapping, sets of variables forming the axes of
the matrix.
Group
If you assign a variable to the Group role, you can add shaded
density ellipses for each level of the Group variable. See “Example
Using a Grouping Variable” on page 192.
By
This option produces a separate scatterplot matrix for each level of
the By variable. If two By variables are assigned, a separate graph
for each possible combination of the levels of both By variables is
produced.
Matrix Format
The Matrix Format can be one of three arrangements: Upper
Triangular, Lower Triangular, or Square. See “Change the Matrix
Format” on page 189.
For more information about the launch window, see Using JMP.
After you click OK, the Scatterplot Matrix window appears. See “The Scatterplot Matrix
Window” on page 190.
Change the Matrix Format
The Matrix Format can be one of three arrangements: Upper Triangular, Lower Triangular, or
Square.
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Chapter 11
Essential Graphing
Figure 11.5 Examples of Matrix Formats
Lower Triangular
Upper Triangular
Square
The Scatterplot Matrix Window
The Scatterplot Matrix window shows an ordered grouping of bivariate graphs. In each
graph, you can examine the relationships between each pair of variables.
Follow the instructions in “Example of a Scatterplot Matrix” on page 187 to produce the plot
shown in Figure 11.6.
Note: For information about additional options, see “Scatterplot Matrix Platform Options” on
page 191.
Chapter 11
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Scatterplot Matrix
Scatterplot Matrix Platform Options
191
Figure 11.6 Example of a Scatterplot Matrix Window
Replace variables in the plot by dragging and dropping a variable, in one of two ways: swap
existing variables by dragging and dropping a variable from one axis to the other axis; or, click
on a variable in the Columns panel of the associated data table and drag it onto an axis. This
feature is not available for matrices in the Square format.
Scatterplot Matrix Platform Options
The following table describes the options within the red triangle menu next to Scatterplot
Matrix.
Show Points
Shows or hides the points in the scatterplots.
Points Jittered
Turns the jittering of the points in the scatterplot on or off. This
option is available when at least one variable is either ordinal or
nominal.
Fit Line
Fits a simple regression line and its mean confidence interval to the
scatterplots.
Density Ellipses
Shows or hides the outline and area of the density ellipses. See
“Example Using a Grouping Variable” on page 192.
Shaded Ellipses
Colors the area within each ellipse. See “Example Using a
Grouping Variable” on page 192.
Ellipses Coverage
Enables you to select an α-level for the ellipses to cover.
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Essential Graphing
Ellipses Transparency
Enables you to select the transparency of the shaded ellipses,
where 0 is completely transparent and 1 is completely opaque.
Ellipses Color
Enables you to select a color for the outline and the area within an
ellipse.
Nonpar Density
Shows or hides the nonparametric density, which represents the
areas where the data points are the most dense. The nonparametric
density estimation is helpful when you have a lot of points and the
density of the points is difficult to see.
There are two quantile density contours. One contour includes
50% of the points, and the other contour includes 90% of the
points. The percentage is based on the smoothed density, which
may not agree with the actual point counts.
Group By
In the Group By window, you can perform the following actions:
•
If you did not select a Group variable in the launch window,
you can add one now.
•
If you did select a Group variable in the launch window:
‒ you can remove the existing Group variable.
‒ you can replace the Group variable.
See “Example Using a Grouping Variable” on page 192.
Lock Scales
Prevents axis scales and gradient legend scales from automatically
adjusting in response to data or filtering changes.
Script
This menu contains options that are available to all platforms.
They enable you to redo the analysis or save the JSL commands for
the analysis to a window or a file. For more information, see Using
JMP.
Example Using a Grouping Variable
This example shows you how to create a scatterplot matrix using a grouping variable.
1. Select Help > Sample Data Library and open Iris.jmp.
2. Select Graph > Scatterplot Matrix.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns.
4. Select Species and click Group.
5. Click OK.
Chapter 11
Essential Graphing
Scatterplot Matrix
Example Using a Grouping Variable
Figure 11.7 Initial Example Using a Grouping Variable
To make the groupings stand out, proceed as follows:
6. From the red triangle menu, select Density Ellipses.
7. From the red triangle menu, select Shaded Ellipses.
Figure 11.8 Example of a Scatterplot Matrix with Ellipses
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Create a Grouping Variable
If your data does not already have a grouping variable, you can create one using the Cluster
platform. Using the Iris.jmp data, assume that the Species column does not exist. You know
that the data comes from three species of Iris flowers, so you want to create three clusters
within a group.
Proceed as follows:
1. Using the Iris.jmp sample data table, select Analyze > Multivariate Methods > Cluster.
2. Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns.
3. Click OK.
4. From the red triangle menu, select Number of Clusters.
5. Type 3 to represent the three different Iris species.
6. Click OK.
7. From the red triangle menu, select Save Clusters.
8. Close the Hierarchical Cluster report window, and go back to the Iris.jmp data table.
You can see that a Cluster column has been added to the Iris.jmp data table.
9. Perform the Scatterplot Matrix analysis. Follow the instructions in the section “Example
Using a Grouping Variable” on page 192, but use Cluster as the grouping variable.
Figure 11.9 Example of a Scatterplot Matrix Using a Cluster Variable
Chapter 12
Ternary Plots
View Plots for Compositional or Mixture Data
The Ternary Plot command in the Graph menu produces a three-axis plot.
Ternary plots are a way of displaying the distribution and variability of three-part
compositional data. (For example, the proportion of sand, silt, and clay in soil or the
proportion of three chemical agents in a trial drug.) You can use data expressed in proportions
or use absolute measures.
The ternary display is a triangle with sides scaled from 0 to 1. Each side represents one of the
three components. A point is plotted so that a line drawn perpendicular from the point to each
leg of the triangle intersect at the component values of the point.
Figure 12.1 Examples of Ternary Plots
Contents
Example of a Ternary Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Launch the Ternary Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
The Ternary Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
Mixtures and Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Ternary Plot Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Additional Examples of the Ternary Plot Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Example Using Mixture Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Example Using a Contour Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Chapter 12
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Ternary Plots
Example of a Ternary Plot
197
Example of a Ternary Plot
This example uses the Pogo Jumps.jmp sample data table. The data, adapted from Aitchison
(1986), show measurements for pogo jumps of seven finalists in the 1985 Hong Kong
Pogo-Jump Championship. A single pogo jump is the total jump distance in three consecutive
bounces, referred to as yat, yee, and sam.
1. Select Help > Sample Data Library and open Pogo Jumps.jmp.
2. From the Graph menu, select Ternary Plot.
3. Select Yat, Yee, and Sam and click X, Plotting.
4. Click OK.
Figure 12.2 Example of a Ternary Plot
Use the crosshairs tool to determine exact coordinates of points within the plot.
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Essential Graphing
Figure 12.3 Using the Crosshairs Tool
To get a better idea of how the three bounces contribute to total distance, assign each
contestant’s points a different color and marker.
1. Right-click on the plot and select Row Legend.
2. Select Finalist in the column list box.
Colors should be automatically set to JMP Default.
3. Select Standard from the Markers menu.
4. Click OK.
5. To make the markers easier to see, right-click on the plot and select Marker Size > 3, Large.
Chapter 12
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Ternary Plots
Launch the Ternary Plot Platform
199
Figure 12.4 Pogo Data Colored by Finalist
Yat is 50% of total.
Yat is 30% of total.
Note that most of the finalists are consistent in the composition of total distance. However,
two finalists, Jao and Ko, both have one jump that is not consistent with their other jumps. For
example, for three of Jao’s jumps, the Yat composed about 50% of the total distance, but for the
other jump, the Yat composed only 30% of the total distance. That jump is not consistent with
the others. A similar observation can be made about Ko’s jumps.
Launch the Ternary Plot Platform
Launch Ternary Plot by selecting Graph > Ternary Plot.
Figure 12.5 The Ternary Plot Launch Window
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The Ternary Plot
Chapter 12
Essential Graphing
Table 12.1 Description of the Ternary Plot Launch Window
X, Plotting
Assign three columns to generate a ternary plot.
If you assign more than 3 variables to the X, Plotting role, a matrix of
ternary plots is created. A separate variable is assigned to the first two
axes of a plot, with the third axis being the sum of the other variables. If
necessary, the variables are scaled so they sum to 1.
Contour Formula
To plot contours of a response surface, assign a column containing a
formula to the Contour Formula role. If you have variables in a Contour
formula that are not listed as X, Plotting variables, JMP appends sliders
below the plot so that the values can be interactively adjusted. See
“Example Using a Contour Function” on page 203.
By
This option produces a separate graph for each level of the By variable.
For more information about the launch window, see Using JMP.
After you click OK, the Ternary Plot window appears. See “The Ternary Plot” on page 200.
The Ternary Plot
Follow the instructions in “Example of a Ternary Plot” on page 197 to produce the plot shown
in Figure 12.6.
Each of the three sides of a ternary plot represents a proportion of 0%, with the point of the
triangle opposite that base representing a proportion of 100%. As a proportion increases in
any one sample, the point representing that sample moves from the base to the opposite point
of the triangle.
Chapter 12
Essential Graphing
Ternary Plots
Ternary Plot Platform Options
201
Figure 12.6 The Ternary Plot
Mixtures and Constraints
Ternary Plot uses the Mixture column property to shade the portion of the graph that is out of
bounds. The only constraints that the Ternary plot recognizes are the mixture sum and the
mixture bounds. The Ternary plot does not recognize a general linear constraint like the
Mixture Profiler does. For information about setting the Mixture column property in the
Column Info window, see the Using JMP book.
Related Information
•
“Example Using Mixture Constraints” on page 202
Ternary Plot Platform Options
The red triangle menu next to Ternary Plot contains options to modify the plot.
Note: To view more detailed options, right-click on the plot.
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Additional Examples of the Ternary Plot Platform
Chapter 12
Essential Graphing
Table 12.2 Descriptions of Ternary Plot Platform Options
Show Constraints
Shows or hides the constraints on the plot. The default plot shows the
constraints.
Contour Fill
Allows filling of contours if a contour formula is specified in the plot.
You can select Lines Only, Fill Above, or Fill Below. The default
platform shows lines only.
Color Theme
Allows you to select a color theme. The default plot shows the Blue to
Gray to Red color theme.
Show Points
Shows or hides the plotted points. The default plot shows the points.
Script
This menu contains commands that are available to all platforms. They
enable you to redo the analysis or save the JSL commands for the
analysis to a window or a file. For more information, see Using JMP.
Additional Examples of the Ternary Plot Platform
This section contains additional examples illustrating ternary plots.
Example Using Mixture Constraints
1. Select Help > Sample Data Library and open Plasticizer.jmp.
The p1, p2, and p3 columns all have Mixture Column Properties defined.
2. From the Graph menu, select Ternary Plot.
3. Select p1, p2, and p3 and click X, Plotting.
4. Click OK.
Chapter 12
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Ternary Plots
Additional Examples of the Ternary Plot Platform
203
Figure 12.7 Mixture Constraints in a Ternary Plot
For more information about mixtures, see the section on the Mixture Profiler in the Profilers
chapter in the Profilers book.
Example Using a Contour Function
The data in Fish Patty.jmp is adapted from Cornell (1990) and comes from an experiment to
optimize the texture of fish patties. The columns Mullet, Sheepshead, and Croaker represent
what proportion of the patty came from those fish types. The column Temperature represents
the oven temperature used to bake the patties. The column Rating is the response and is a
measure of texture acceptability, where higher is better. A response surface model was fit to
the data and the prediction formula was stored in the column Predicted Rating. (For more
information, see the section on Mixture Profilers in the Profilers book.)
1. Select Help > Sample Data Library and open Fish Patty.jmp.
2. From the Graph menu, select Ternary Plot.
3. Select Mullet, Sheepshead, and Croaker and click X, Plotting.
4. Select Predicted Rating and click Contour Formula.
5. Click OK.
6. From the red triangle menu, select Contour Fill > Fill Above.
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Chapter 12
Essential Graphing
Figure 12.8 Ternary Plot with a Contour Formula
The manufacturer wants the rating to be at least 5. You can drag the slider for Temperature
and see the contours for the Predicted Rating change. Each point represents a mixture of the
three fish. Any given mixture of fish types receives different ratings according to the
temperature at which the patties are baked.
In this example, the red shaded area shows the mixture of fish that results in a rating of 5 to
5.5. Any purple areas show the mixture of fish that results in a rating of 5.5 and above. At 400
degrees, a mixture of mostly sheepshead and mullet with very little croaker results in a rating
of 5 and above.
Chapter 13
Summary Charts
Create Charts of Summary Statistics
The Chart platform on the Graph menu charts continuous variables versus categorical
variables. The continuous variables are summarized for each categorical level. Chart supports
several chart types, such as bar charts, pie charts, and line charts. Chart is similar to the Tables
> Summary command and is useful for making graphical representations of summary
statistics.
If you want to make a plot of individual data points (rather than summaries of data points),
we recommend using the Overlay Plot platform. See the “Overlay Plots” chapter on page 75
for details about overlay plots.
Figure 13.1 Examples of Charts
Stacked
Bar Chart
Pie Chart
Combined
Range and
Line Chart
Bar Chart
Contents
Example of the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Launch the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Plot Statistics for Y Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Use Categorical Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Use Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Adding Error Bars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
The Chart Report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Legends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Coloring Bars in a Chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
Chart Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
General Platform Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Y Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Additional Examples of the Chart Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Example Using Two Grouping Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Example Using Two Grouping Variables and Two Category Variables . . . . . . . . . . . . . . . 221
Plot a Single Statistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Plot Multiple Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Plot Counts of Variable Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
Plot Multiple Statistics with Two X Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Create a Stacked Bar Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Create a Pie Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Create a Range Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Create a Chart with Ranges and Lines for Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
Chapter 13
Essential Graphing
Summary Charts
Example of the Chart Platform
207
Example of the Chart Platform
Here is a simple example that shows how to plot the mean height of students based on their
age group.
1. Select Help > Sample Data Library and open Students.jmp.
2. Select Graph > Chart.
3. Select height and click Statistics.
4. Select Mean from the menu of statistics.
5. Select age and click Categories, X, Levels.
Figure 13.2 The Completed Chart Launch Window
6. Click OK.
Figure 13.3 Mean of height by age
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Chapter 13
Essential Graphing
This bar chart shows the following:
•
The mean height of the students in this class increases with age.
•
The largest increase occurs at the earliest ages.
•
The mean changes very little for the older students.
You might expect the change of the mean height to be different for males and females.
7. From the red triangle menu for Chart, select Script > Relaunch Analysis.
The Chart launch window appears, already filled out for you. If you clicked OK now, you
would see a duplicate of the chart you already have.
8. Open the Additional Roles outline.
9. Select sex and click Grouping.
10. Click OK.
Figure 13.4 Mean of height by age and Grouped by sex
These two bar charts confirm your assumption. The mean of height for girls in the class rises
early and then remains stable. The mean of height for boys rises more dramatically overall,
and also continues to increase at later ages.
Chapter 13
Essential Graphing
Summary Charts
Launch the Chart Platform
209
Launch the Chart Platform
Launch the Chart platform by selecting Graph > Chart.
Figure 13.5 The Chart Launch Window
In the Chart launch window, you can assign the following:
•
Up to two X variables, which appear on the x-axis in the same order that you assign them
in the launch window.
•
As many Y variables (statistics) as you want. If the data is already summarized, select Data
as the statistics option.
Table 13.1 Description of the Chart Launch Window
Cast Selected Columns Into Roles:
Statistics
Use this menu to select the statistic to chart for each Y variable.
See “Plot Statistics for Y Variables” on page 211.
Categories, X, Levels
Select up to two X variables whose levels are categories on the
x-axis. The Chart platform produces a bar for each level or
combination of levels of the X variables. If you do not specify an
X variable, the chart has a bar for each row in the data table.
See “Use Categorical Variables” on page 213.
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Chapter 13
Essential Graphing
Table 13.1 Description of the Chart Launch Window (Continued)
Additional Roles:
Grouping
For one or more Grouping variables, independent results are
produced for each level or combination of levels of the grouping
variables. These results appear in the same report window, but in
separate plots. See “Use Grouping Variables” on page 213.
Weight
Assigns a variable to give the observations different weights.
Freq
Assigns a frequency variable. This is useful if you have
summarized data.
By
By variables cause plots to be created in separate outline nodes.
Options:
Overlay
If this option is selected, all Y variables are plotted in one graph.
If this option is not selected, each Y variable is plotted in its own
graph. This option is selected by default where available.
This option is available for all chart types except Pie Chart.
Chart Orientation
Select Vertical for a vertical chart or Horizontal for a horizontal
chart. Vertical is the default option.
This option is available for all chart types except Pie Chart.
Chart Type
Select the type of chart that you want. Available chart types are
Bar Chart, Line Chart, Pie Chart, Needle Chart, and Point Chart.
Selecting a chart controls which of the other options are
available.
You can always change these options after the chart appears. See
“Y Options” on page 219.
Show Points
Shows the points in the plot. This option is selected by default
where available.
This option is available for all chart types except Bar Chart and
Pie Chart.
Connect Points
Connects the points in the plot. Show Points does not have to be
selected to connect points. This option is selected by default
where available.
This option is available only for Line Chart.
Chapter 13
Essential Graphing
Summary Charts
Launch the Chart Platform
211
Table 13.1 Description of the Chart Launch Window (Continued)
Add Error Bars to Mean
Adds error bars when the Mean statistic is selected for at least
one Y variable and at least one X variable is assigned. This option
is not selected by default.
This option is available for Line Chart and Bar Chart, and
additional options are added to the Chart launch window. See
“Adding Error Bars” on page 214.
Percent for quantiles
Sets the specific quantile when the Quantiles statistic is selected
for at least one Y variable. The default value is 25. Specify a
different quantile:
1. Type the value in the Percent for quantiles box.
2. Select a column.
3. Click Statistics.
4. Select Quantiles from the menu of statistics.
For more information about the launch window, see Using JMP.
After you click OK, the Chart report window appears. See “The Chart Report” on page 215.
Plot Statistics for Y Variables
You can plot the raw data for Y variables, or you can plot as many statistics as you want on the
y-axis. The Statistics menu in the Chart launch window lists the available statistics. To specify
the y-axis, highlight one or more numeric columns in the Select Columns list and select from
the list of statistics. If all the statistics requested are counting statistics (for example, N) for the
same column, that column is used as the category variable.
The available statistics in the Chart platform are described in the following table. They are the
same as those computed by statistical platforms in the Analyze menu and the Summary
command in the Tables menu.
Data
The value of each row in the data table when there is no categorical
variable. If there is a categorical variable, Data produces a point plot
within the variable’s levels.
N
The number of nonmissing values. Also used to compute statistics
when there is no column assigned as a weight variable. The Chart
platform shows N for each level of a categorical variable.
Mean
The arithmetic average of a column’s values. The mean is the sum of
nonmissing values divided by the number of nonmissing values.
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Essential Graphing
Std Dev
The sample standard deviation computed for each level of a categorical
variable. It is the square root of the variance of the level values.
Min
The least value, excluding missing values, in the level of a categorical
variable.
Max
The greatest value in the level of a categorical variable.
Range
The difference between the maximum and minimum values in each
level of a categorical variable.
% of Total
The percentage of the total number of rows represented by each level of
the Categories, X, Levels variable. If summary statistics are requested
on a continuous variable, then the % of Total equals the proportion of
the sum represented by each level of the Categories, X, Levels variable.
N Missing
The number of missing values in each level of a categorical variable.
N Categories
Also known as n-categories. The combinatorial or algebraic models of
directed spaces.
Sum
The sum of all values in each level of a categorical variable.
Sum Wgt
The sum of all values in a column assigned as Weight. Also used
instead of N to compute other statistics. Chart shows the sum of the
weight variable for each level of a categorical variable.
Variance
The sample variance computed for each level of a categorical variable.
Std Err
The standard error of the mean of each level of a categorical variable. It
is the standard deviation, Std Dev, divided by the square root of N for
each level. If a column is assigned a weight variable, then the
denominator is the square root of the sum of the weights.
CV
The coefficient of variation of a column’s values. The CV is computed
by dividing the column standard deviation by the column mean and
multiplying by 100.
Median
The middle value in each level of a categorical variable. Half of the
values in the level are greater than or equal to the median and half are
less than the median.
Interquartile
Range
The measure of statistical dispersion (difference between the upper and
lower quartiles) often used to find outliers in data. Also known as the
midspread or middle fifty.
Chapter 13
Essential Graphing
Summary Charts
Launch the Chart Platform
213
Divides a data set so that n% of the data is below the nth quantile. To
compute a specific quantile, enter the quantile value in the box located
in the lower left of the Chart launch window before requesting Quantile
from the menu.
Quantiles
Related Information
•
“Plot a Single Statistic” on page 222
•
“Plot Multiple Statistics” on page 223
Use Categorical Variables
You can assign zero, one, or two X variables whose levels are categories on the x-axis. The
Chart platform produces a bar (or a needle, or a pie slice, and so on) for each level or
combination of levels of the X variables. If you do not specify any X variable, the chart has a
bar for each row in the data table.
The following table shows what type of chart to expect based on the number of X and Y
variables.
X
Y
Type of Chart
none
one or
more
If you do not specify a variable for categories, most statistics
produce a bar (or a needle, or a pie slice, and so on) for each
observation in the data table. This is useful when your data is
already summarized. In that case, you usually specify Data as the
statistic to plot. Each bar reflects the value of the Y variable. See
“Plot a Single Statistic” on page 222, for an example.
one or
two
none
Plots the counts for each level of the X variable. For two X
variables, the counts for each level of both X variables are
included (or overlaid) in a single chart. See “Plot Counts of
Variable Levels” on page 224, for an example.
one or
two
one or
more
Plots the selected statistics for each level of the X variable. For
two X variables, the selected statistics for each level of the X
variables are included (or overlaid) in a single chart. See “Plot
Multiple Statistics” on page 223, and “Plot Multiple Statistics
with Two X Variables” on page 225, for examples.
Use Grouping Variables
If you specify one grouping variable, the result is a separate chart for each level of the
grouping variable. All charts are under the same outline title. If you used the same variable as
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Launch the Chart Platform
Chapter 13
Essential Graphing
a By variable instead, the same separate charts are produced, but each chart is under its own
outline title.
If you specify two or more grouping variables, the result is a matrix of charts. Each chart
shows a combination of one level from each of the grouping variables.
If there are multiple statistics, the Overlay option is checked by default, and the Y variables
(statistics) are plotted on the same chart for each level of the grouping variable. However, the
levels of the grouping variable cannot be overlaid into the same plot frame. For example, if the
levels of your grouping variable are Male and Female, the Overlay option cannot be used to
combine the two Male and Female graphs into one graph. To see that type of result, use
Categories, X, Levels instead of Grouping variables.
Related Information
•
“Example Using Two Grouping Variables” on page 220
•
“Example Using Two Grouping Variables and Two Category Variables” on page 221
Adding Error Bars
Error bars are available when the Mean statistic is selected for at least one Y variable, and at
least one X variable is assigned. Error Bars are not available for pie charts. Selecting Add Error
Bars to Mean causes additional options to appear in the Chart launch window.
After the option is checked, select a type of error bar from the menu that appears. Some of the
types of error bar have an additional numeric field. The following table describes the different
types of error bars that are available.
Range
Creates error bars based on the range of the data.
Standard Error
Creates error bars based on the standard error of the mean.
You can specify the number of standard errors.
Standard Deviation
Creates error bars based on the standard deviation of the
data. You can specify the number of standard deviations.
Confidence Interval
Creates error bars based on a confidence interval of the
mean. The standard deviation used for the confidence
interval is separate for each bar. You can specify the level of
confidence.
Confidence Interval
(pooled)
Creates error bars based on a confidence interval of the
mean. The standard deviation used for the confidence
interval is based on the pooled standard deviation. This
option is not available if you have more than one category
variable. You can specify the level of confidence.
Chapter 13
Essential Graphing
Summary Charts
The Chart Report
215
The Chart Report
Follow the instructions in “Example of the Chart Platform” on page 207 to produce the report
shown in Figure 13.6.
Charts can be bar charts, pie charts, line charts, needle charts, point charts, and range charts.
Figure 13.6 shows a standard bar chart.
Figure 13.6 The Initial Chart Report Window
For information about additional options for the report, see “Chart Platform Options” on
page 217.
Legends
Legends are shown as needed. If your chart uses different colors or markers to show levels of
one or two X variables, a legend below the chart shows them. If your chart uses different
colors or markers to show more than one statistic, a legend to the right of the chart shows
them.
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Chapter 13
Essential Graphing
Figure 13.7 Examples of an X Legend (left) and Y Legend (right)
Ordering
By default, the Chart platform orders the bars using one of the common orders supported by
JMP (months, days of the week, and so on). However, if the grouping column has a Row Order
Levels column property, the levels are ordered in that order. If the grouping column has a
Value Ordering column property, it uses that order. If both Row Order Levels and Value
Ordering properties are defined, the Value Ordering property has precedence. With neither
property in effect, bars are drawn in alphanumeric order.
Coloring Bars in a Chart
There are a few ways to color bars after the chart has been created.
Manually Set the Color of All Bars
1. Ensure that no bars are selected.
2. From the red triangle menu for Chart, select Level Options > Colors.
3. Select a color from the color palette that appears.
Set the Color of a Single Bar
1. Select a bar in the chart.
2. From the red triangle menu for Chart, select Level Options > Colors.
3. Select a color from the color palette that appears.
Chapter 13
Essential Graphing
Summary Charts
Chart Platform Options
217
Note: If you assigned both a category variable and a grouping variable in your chart, all of the
bars are colored even if you selected only one bar.
Set the Color of a Single Bar Using the Legend
1. Select the legend bar color.
2. Right-click and select Colors.
3. Select a color from the color palette that appears.
Automatically Assign a Color to a Level
1. Select the column in the data table.
2. Select Cols > Column Info.
3. Assign colors using Value Colors in the Column Properties menu. For details about the
Value Colors property, see the Using JMP book.
Chart Platform Options
The basic Chart report is shown in Figure 13.6 on page 215.
The Chart platform has plotting options on the red triangle menu on the Chart title bar. When
you select one of these options at the platform level, it affects all plots in the report if no legend
levels are highlighted. If one or more plot legend levels are highlighted, the options affect only
those levels. There is also a single-plot options menu for each Y variable, which appears when
you highlight a Y variable legend beneath the plot and right-click.
The individual plot options are the same as those in the Y Options submenu at the platform
level. See “Y Options” on page 219.
General Platform Options
When you select one of these options at the platform level, it affects all plots in the report if no
legend levels are highlighted. If one or more plot legend levels are highlighted, the options
affect only those plots.
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Table 13.2 Descriptions of General Platform Options
Overlay
Displays a single overlaid chart when you have more than one
Y (statistics) variable. Each statistic can be assigned a different
type of chart (such as line and bar) and overlaid in a single plot.
Overlay is selected by default. The axis notation only shows for the
last chart displayed if the charts are not overlaid. When Overlay is
not selected, the platform shows duplicate axis notation for each
chart.
Vertical Chart,
Horizontal Chart
Changes horizontal charts to vertical charts (Vertical), or vertical
charts to horizontal charts (Horizontal). Affects all charts in the
report. Pie charts are converted to bar chats.
Pie Chart
Changes a horizontal or vertical chart type to a pie chart.
Range Chart
Displays a range chart. You can change any chart that includes at
least two statistics in a single plot into a range chart. See “Create a
Range Chart” on page 229, for an example of a range chart.
Add Error Bars to Mean
Adds error bars to charts based on means. A window opens,
prompting you to select the type of error bar. If error bars already
exist on a chart, you can change the error bar type. See “Adding
Error Bars” on page 214 for a description of error bar types.
Stack Bars
Stacks the bars from levels of a subgroup end-to-end. To use this
option, you need two Categories, X, Levels variables and a
statistic. See “Create a Stacked Bar Chart” on page 226, for an
example of stacking bars.
Y Options
Contains the options described in “Y Options” on page 219. To
apply these options to a single Y variable, highlight that variable
in the legend first.
Level Options
Selects colors and markers. If no levels (bars, points, or pie slices)
are selected, the color or marker that you select is applied to all
levels. If you select one or more levels, the color or marker that
you select is applied only to the selected levels. See “Coloring Bars
in a Chart” on page 216.
Label Options
Attaches labels to your plots. In the Label Options menu, the first
two options (Show Labels and Remove Labels) turn labels on and
off. The last three options (Label by Value, Label by Percent of
Total Values, Label By Row) specify what label should appear.
Only one label can be shown at a time. Label options are also
available by right-clicking in the chart.
Chapter 13
Essential Graphing
Summary Charts
Chart Platform Options
219
Table 13.2 Descriptions of General Platform Options (Continued)
Thick Connecting Line
Toggles the connecting line in a line chart to be thick or thin.
Show Y Legend
Shows the Y legend of the plot. This option is on by default for
overlaid charts.
Show Level Legend
Shows the level legend of the plot. This option is on by default
when the Show Separate Axes option is selected.
Show Separate Axes
Duplicates the axis notation for each chart when there are multiple
charts. By default, the axis notation only shows for the last chart
displayed if the charts are not overlaid. This option is not available
for grouped charts.
Ungroup Charts
Moves level identifiers from the right side of the charts to beneath
the charts for individual charts when a grouping variable is
specified.
Script
This menu contains commands that are available to all platforms.
They enable you to redo the analysis or save the JSL commands
for the analysis to a window or a file. For more information, see
Using JMP.
Y Options
The following section describes the Y Options submenu. These commands apply to all Y
variables, unless you have a legend level highlighted, then they apply to only the highlighted
Y variable.
Click on the legend within a plot to highlight a Y. If you right-click on a highlighted legend
level, the commands to modify that Y appear. The commands then affect only the highlighted
Y.
Table 13.3 Descriptions of Y Options
Bar Chart
Displays a bar for each level of the chart variables. The default chart is a
bar chart.
Line Chart
Replaces a bar chart with a line chart and connects each point with a
straight line. Select the Show Points option to show or hide the points.
Line Chart is also available as a platform option, which then applies to all
charts at once.
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Chapter 13
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Table 13.3 Descriptions of Y Options (Continued)
Needle Chart
Replaces each bar with a line drawn from the axis to the plotted value.
Needle Chart is also available as a platform option, which then applies to
all charts at once.
Point Chart
Shows only the plotted points, without connecting them.
Show Points
Toggles the point markers on a line or needle chart on or off.
Connect Points
Toggles the line connecting points on or off.
Show Error Bars
Toggles the error bars on plots of means. Note that this option is available
only for plots that involve means of variables.
Overlay Color
Assigns a color to statistics (y-axis) to identify them in overlaid charts.
Overlay Marker
Assigns a marker to statistics, to identify them in overlaid charts.
Pen Style
Selects a line style for connecting lines.
Label Format
Note: To see the impact of this change, you must turn on labels. From the
red triangle menu, select Label Options > Show Labels.
Specifies the format, field width, and number of decimals for labels.
Thousands separators can be turned on or off. Enter the values in the
window that appears.
Additional Examples of the Chart Platform
This section contains additional examples using the Chart platform.
Example Using Two Grouping Variables
1. Select Help > Sample Data Library and open Car Poll.jmp.
2. Select the Chart command from the Graph menu.
3. Select age and click Statistics.
4. Select Mean from the drop-down list of statistics.
5. Select sex and click Categories, X, Levels.
6. Open the Additional Roles outline.
7. Select marital status and country and click Grouping.
8. Click OK.
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Figure 13.8 Example of a Chart with Two Grouping Variables
Example Using Two Grouping Variables and Two Category Variables
If you use multiple grouping and category variables, the multiple group labels appear around
the borders of the charts, and the multiple X variables cause divisions within charts.
1. Select Help > Sample Data Library and open Car Poll.jmp.
2. Select the Chart command from the Graph menu.
3. Select age and click Statistics.
4. Select Mean from the drop-down list of statistics.
5. Select sex and type and click Categories, X, Levels.
6. Open the Additional Roles outline.
7. Select marital status and country and click Grouping.
8. Click OK.
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Figure 13.9 Example of a Chart with Two Grouping Variables and Two Category Variables
Plot a Single Statistic
If you do not specify a variable for categories, most statistics produce a bar for each
observation in the data table. This is useful when your data is already summarized. In that
case, you usually specify Data as the statistic to plot. Each bar reflects the value of the Y
variable.
1. Select Help > Sample Data Library and open Trial1.jmp.
This data table contains data from a popcorn experiment. Each row is an experiment, and
the yield column is the amount of popped corn that resulted.
2. Select Graph > Chart.
3. Select yield and click Statistics.
4. Select Data from the menu of statistics.
5. Click OK.
The bar chart in Figure 13.10 shows a bar for each experiment (each row) in the data table.
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Figure 13.10 Example of a Chart with One Statistic and No Categories
Plot Multiple Statistics
Chart more than one statistic in a single chart to compare them. If you do not assign an X
variable, the chart displays a bar or point for each row in the data table.
1. Select Help > Sample Data Library and open Financial.jmp.
2. Select Graph > Chart.
3. Select Type and click Categories, X, Levels.
4. Select Sales($M) and Assets($Mil.) and click Statistics.
5. Select Mean from the menu of statistics.
6. Click OK.
The bar chart in Figure 13.11 compares the mean of sales and assets for each type of company.
Figure 13.11 Example of a Chart with Two Statistics and One Category
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Plot Counts of Variable Levels
If you assign one or two X variables without specifying any Y variables, JMP produces bar
charts that show counts for each level of the X variables.
One X Variable
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Size Co and click Categories, X, Levels.
4. Click OK.
The bar chart in Figure 13.12 shows a bar for each level of the Size Co variable.
Figure 13.12 Example of a Chart with one Category and no Statistics
Two X Variables
If you specify two X variables (with no statistics variables), JMP divides the data into groups
based on the levels in the two X variables and plots the number of members in each group.
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Type and click Categories, X, Levels.
4. Select Size Co and click Categories, X, Levels.
The order that you assign these variables is important. The levels of the second variable are
nested within the levels of the first variable.
5. Click OK.
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The bar chart on the left in Figure 13.13 shows the levels for the size of computer companies
and of pharmaceutical companies. The bar chart on the right shows the results if you first
selected Size Co and then Type as category variables.
Figure 13.13 Examples of Charts with Two Categories and No Statistics
Plot Multiple Statistics with Two X Variables
When you assign two category variables, the result is a chart that shows the statistics for each
level of the second category variable. This variable is nested within each level of the first
category variable. The chart shows each Y side by side, using a common axis.
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Type and click Categories, X, Levels.
4. Select Size Co and click Categories, X, Levels.
5. Select Sales ($M) and Profits ($M) and click Statistics.
6. Select Mean from the menu of statistics.
7. Click OK.
The chart on the left in Figure 13.14 shows the result.
8. To see a separate chart for each statistic, click to deselect Overlay from the red triangle
menu for Chart.
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The chart on the right in Figure 13.14 shows the result.
Figure 13.14 Examples of Charts with Two Statistics and Two Categories
Create a Stacked Bar Chart
When you have two X levels and a single Y variable, stack the bars by selecting the Stack Bars
command from the platform menu.
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Type and click Categories, X, Levels.
4. Select Size Co and click Categories, X, Levels.
5. Select Sales ($M) and click Statistics.
6. Select Mean from the menu of statistics.
7. Click OK.
8. Select Stack Bars from the red triangle menu for Chart.
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Figure 13.15 Example of a Stacked Bar Chart
Create a Pie Chart
You can create a pie chart either in the Chart launch window or in the report window after
you create another type of chart.
Create a Pie Chart in the Chart Launch Window
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Size Co and click Categories, X, Levels.
4. Select Pie Chart from the menu of chart types.
5. Click OK.
Change a Bar Chart to a Pie Chart
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Size Co and click Categories, X, Levels.
4. Click OK.
5. Select Pie Chart from the red triangle menu for chart.
You can also right-click in the chart and select Chart Options > Pie Chart.
Both of the above steps produce the same pie chart shown in Figure 13.16.
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Figure 13.16 Example of a Pie Chart
The pie chart shows that many more companies are small then are big or medium.
Show the Values or Percentages for Each Category
1. Starting with the pie chart in Figure 13.16, select Label Options > Show Labels from the red
triangle menu for Chart.
‒ You can also right-click in the chart and select Label > Show Labels.
Label By Value is the default setting, so the pie chart now shows the number of companies
that are big, medium, and small. See the chart on the left in Figure 13.16.
2. To show percentages instead, select Label Options > Label by Percent of Total Values from
the red triangle menu for Chart.
‒ You can also right-click in the chart and select Label > Label by Percent of Total Values.
The chart on the right shows the percentage of each company size.
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Figure 13.17 Example of Pie Chart Labels: Values (left) and Percentages (right)
Create a Range Chart
A range chart shows the range between two values. In this example, use a range chart to
compare the profits and sales of differently sized companies.
1. Select Help > Sample Data Library and open Companies.jmp.
2. Select Graph > Chart.
3. Select Size Co and click Categories, X, Levels.
4. Select Profits ($M) and Sales ($M) and click Statistics.
5. Select Mean from the menu of statistics.
6. Click OK.
7. Select Range Chart from the red triangle menu for Chart.
Figure 13.18 Example of a Range Chart
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The chart shows a much larger difference between profits and sales than that for medium and
small companies.
Create a Chart with Ranges and Lines for Statistics
The Stock Prices.jmp sample data table contains data for the dates and values of a stock over
time. The variable YearWeek is a computed column representing the year and week in a single
variable. Use a range chart to show the high, low, and average close values for each stock. For
those weeks where data exists for multiple days, the average of the values is plotted.
1. Select Help > Sample Data Library and open Stock Prices.jmp.
2. Select Graph > Chart.
3. Select YearWeek and click Categories, X, Levels.
4. Select High and click Statistics.
5. Select Max from the menu of statistics.
6. Select Low and click Statistics.
7. Select Min from the menu of statistics.
8. Select Close and click Statistics.
9. Select Mean from the menu of statistics.
10. Click OK.
11. Select Range Chart from the red triangle menu for Chart.
12. In the legend, right-click Mean(Close) and select Connect Points.
Figure 13.19 Example of a Combined Range and Line Chart
The range for each date shows the highest and lowest values the stock reached during that
week. The line shows the stock’s average closing price for that week.
Chapter 14
Create Maps
Add Maps or Custom Shapes to Enhance Data Visualization
JMP transforms numbers and geographic data into compelling images, and turns simple
tables of numbers into captivating pictures that bring the story in your data to life. JMP can
help you display your data on geographical maps. Choose from built-in high-quality images.
Select Street Map Service or Web Map Service to get custom map images from the Internet.
JMP includes shape files for borders or many geographic regions and lets you add your own
custom shapes, such as for a manufacturing plant or campus.
Figure 14.1 Example of a Map
Contents
Overview of Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Example of Creating A Map in Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Graph Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Map Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
Custom Map Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Background Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Images in Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
Change Map Colors and Transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
Examples of Creating Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Louisiana Parishes Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Hurricane Tracking Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
Office Temperature Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
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Overview of Mapping
There are two types of map support in JMP: one where a map shows the data (Graph Builder)
and one where a map provides context for the data (Background Maps). You can also create
your own maps.
Graph Builder
You can interact with Graph Builder to create compelling visualizations of your data. JMP
includes graphical support to display analyses using background maps and shape files. You
can add color and geographical boundaries to maps through the following zones:
•
The Map Shape zone assigns geographical boundaries to a map based on variables in the
data table. The map shape value determines the x and y axes.
Boundaries such as U.S. state names, Canadian provinces, and Japanese prefectures are
installed with JMP. You can also create your own boundaries (geographical or otherwise)
and specify them as a Map Role column property in the data table.
•
The Color zone applies color based on a variable to geographical shapes.
•
The Size element scales map shapes according to the size variable, minimizing distortion.
Background Maps
You can add background maps to any JMP graph through the Set Background Map window.
You can use built-in background maps or connect to a Web Map Service (WMS) to display
specialty maps like satellite images, radar images, or roadways. Right-click in a graph and
select Graph > Background Map to choose from the following images and boundaries:
•
Simple Earth and Detailed Earth maps are installed with JMP.
•
NASA server provides maps using a WMS to show their most up-to-date maps.
•
Street Map Service provides street maps. The OpenStreetMap and Open Database License
links provide further information on the Street Map Service.
•
Web Map Service lets you enter the URL for a Web site that provides maps using the WMS
protocol. You can also specify the map layer.
•
Boundaries for various regions.
Example of Creating A Map in Graph Builder
This example uses the Crime.jmp sample data table, which contains data on crime rates for
each US state.
1. Select Help > Sample Data Library and open Crime.jmp.
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2. Select Graph > Graph Builder.
3. Drag and drop State into the Map Shape zone.
4. Drag and drop Burglary into the Color zone.
Figure 14.2 Example of Burglary by State
Note the following:
•
The latitude and longitude appear on the Y and X axes.
•
The legend shows the colors that correspond to the burglary rates. Since Burglary is a
continuous variable, the colors are on a gradient.
•
The map is projected so that relative areas are not distorted (the 49th parallel across the
top of the US is not a straight line).
Graph Builder
Open a data table that contains geographic data. Launch Graph Builder by selecting Graph >
Graph Builder. The primary element in the Graph Builder window is the graph area. The
graph area contains drop zones (Map Shape, Color and Size), and you can drag and drop
variables into the zones. From here you can map shapes for data tables that include place
names.
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Figure 14.3 The Graph Builder Window
Map Shape
When a column contains the names of geographical regions (such as countries, regions, states,
provinces, counties), you can assign the column to the Map Shape zone. When a variable is
dropped in Map Shape, Graph Builder looks for map shapes that correspond to the values of
the variable and draws the corresponding map. The variable can have a column property that
tells JMP where to find the map data. If not, JMP looks through all known map files. If you
have a variable in the Map Shape zone, the X and Y zones disappear. The Map Shape zone is
positional and influences the types of graph elements that are available.
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Figure 14.4 Example of Cities.jmp after Dragging State to Map Shape
For each map there are two .jmp files; one for the name data (one row per entity) and one for
coordinate data (many rows per entity). They are paired via a naming convention;
xxx-Name.jmp and xxx-XY.jmp, where "xxx" is some common prefix. Some examples of sample
files that are shipped with the product are:
•
World-Name.jmp
•
World-XY.jmp
•
US-State-Name.jmp
•
US-State-XY.jmp
Map Name Files
Each xxx-Name.jmp can contain any number of shape name columns, which are identified
with a column property. Multiple name columns support localizations and alternate names
styles (such as abbreviations), but a given graph usage uses only one column of names. The
first column of the Name file must contain unique Shape ID numbers in ascending order.
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Figure 14.5 Example of US-State-Name.jmp
Map XY Files
Each xxx-XY.jmp file has four columns. Each row is a coordinate in some shape. Each part is
made of one or more shapes. Each shape is a closed polygon. The first column is the same
Shape ID as in the xxx-Name file. The second column is the Part ID. The next two columns are X
and Y.
Figure 14.6 Example of US-State-XY.jmp
Color
The Graph Builder platform lets you adds color to create choropleth maps. A choropleth map
shows statistical differences in a geographic area while maintaining the proportion of the
statistical variable.
Drag a column containing geographic place-names, like countries, regions, states, or
provinces, into the Map Shape zone and create a map. Then drag a column to the Color zone
to color the map by that column. The categorical or continuous color theme selected in your
Preferences is applied to each shape.
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Figure 14.7 Example of SAT.jmp after Dragging 2004 Verbal to Color
Size
Use the Size element to scale map shapes according to the size variable, minimizing
distortion.
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Figure 14.8 Example of SAT.jmp after Dragging Population to Size
Customizing Graphs
To change colors and transparency for a map, right-click on the color bar in the legend. The
right-click options vary, depending on whether the color variable is continuous or categorical
(nominal or ordinal). However, for both types of variables, you can change the transparency.
To change the transparency of a graph:
1. Right-click on the color of the variable level on the color bar that you want to change and
select Transparency.
2. Specify the transparency between 0 (clear) and 1 (opaque).
3. Click OK.
You can also change the transparency of images (for example, Simple Earth and Detailed
Earth). To set the transparency, right-click over the graph and select Customize.... This brings
up the Customize Graph window, where you can select the Background Map and assign a
value for transparency. A valid value for transparency goes from 0.0 (completely transparent)
to 1.0 (completely opaque). Within Graph Builder, you can also right-click over the graph and
select Graph > Transparency.
Categorical (nominal or ordinal) variables use a singular coloring system, where each level of
the variable is colored differently.
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To change the color of one of the variable levels:
1. Right-click on the color of the variable level that you want to change and select Fill Color.
2. Select the new color.
Continuous variables use a color gradient.
To change the color theme:
1. Right-click on the color bar and select Gradient.
2. In the Gradient Settings window, select a different Color Theme.
For more details about this window, see “Change Map Colors and Transparency” on page 254.
Graphs consist of markers, lines, text, and other graphical elements that you can customize. If
you right-click an image, there are several options for working with the graph. The options
differ based on what you clicked. For more information, see the “Graph Builder” chapter on
page 31 and Using JMP. Below are a few options.
Figure 14.9 Right-click Menu for Graphics
•
Map Shapes:
‒ Change To: Caption Box - a summary statistic value for the data
‒ Summary Statistics - provides options for changing the statistic being plotted
‒ Show Missing Shapes - Shows or hides missing data from a map (turned off by
default). Missing Shape means that there are some shape names that exist in the map
file but not in the data table for analysis.
‒ Remove
•
Customize - You can change the properties of the graph such as contents, grid lines, or
reference lines. The graphical elements that you can customize differ for each graph. Select
Background Map to change the transparency of a background map or Map Shape to
change the line color, line style and width, fill color, missing shape fill or missing value fill.
Click Help in the Customize Graph window for a more detailed explanation of the
customize options.
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Custom Map Files
You can create your own map files by following the same pattern as the built-in files. To add
your own map files, you need two things: a series of XY coordinates for the vertices of the
polygons that describe the shape, and a set of names for each polygon. Data and shape
attributes are required to map custom shapes so you can add your own shapes to JMP. There
are two common sources for data like this: Esri shapefiles and SAS/GRAPH map data sets.
In order for JMP to automatically find your files, place them in the following directory:
•
On Windows: C:\Users\<user name>\AppData\Local\SAS\JMP\Maps
•
On Mac: /Users/<user name>/Library/Application Support/JMP/Maps
Note: On Windows, in JMP Pro, the “JMP” folder is named “JMPPro”. In JMP Shrinkwrap, the
“JMP” folder is named “JMPSW”.
Or, you can link the map files to your data files explicitly with the Map Role column property.
Note the following when creating map files:
•
Each set of map files that you create must contain a -Name file and a -XY file.
•
The first column in both files must be the ascending, numeric Shape ID variable. The
-Name file can contain any other columns. The shapes are built by rows. The XY
coordinates have to go around the shape rather than just define the convex hull of the
shape.
•
For the Map Role column property, columns that are marked with the Shape Name
Definition are searched for shape identification and must contain unique values.
•
If you import an Esri SHP file, it is opened in the correct format. -Name files commonly
have a .dbf extension. For more information, see “Esri® Shapefiles” on page 244.
•
SAS/GRAPH software includes a number of map data sets that can be used with JMP. For
more information, see “SAS/GRAPH® Map Data Sets” on page 245.
You might want to create choropleth maps of other non-geographic regions (for example, a
floor of an office building). Simply, add the two shape files for your non-geographic space. If
you do not have XY coordinates, but you do have a graphic image of the space, you can use
the Custom Map Creator add-in for JMP. With this add-in, you can trace the outlines of the
space and JMP creates the -XY and -Name files for you. You can download this add-in from the
JMP File Exchange page.
Map Role
You can specify the attributes and properties of a column in a data table within the Column
Info window in Column Properties. The Map Role property is set for a column like other
column properties in the Column Info window.
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If you have created your own data table that contains boundary data (such as countries,
regions, states, provinces, or counties) and you want to see a corresponding map in Graph
Builder, use the Map Role property within Column Properties. Each pair of map files that you
create must contain a -Name file and a -XY file.
Note the following:
•
If the custom boundary files reside in the default custom maps directory, then you need to
specify only the Map Role property in the -Name file.
•
If the custom boundary files reside in an alternate location, specify the Map Role property
in the -Name file and in the data table that you are analyzing.
•
The columns that contain the Map Role property must contain the same boundary names,
but the column names can be different.
To add the Map Role property into the -Name data table:
1. Right-click on the column containing the boundaries and select Column Properties > Map
Role.
2. Select Shape Name Definition below Map Role.
3. Click OK.
4. Save the data table.
Figure 14.10 Shape Name Definition Example
To add the Map Role property into the data table that you are analyzing:
Note: Perform these steps only if your custom boundary files do not reside in the default
custom maps directory.
1. Right-click on the column containing the boundaries and select Column Properties > Map
Role.
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2. Select Shape Name Use below Map Role.
3. Next to Map name data table, enter the relative, or absolute path to the -Name map data
table.
If the map data table is in the same folder, enter only the filename. Quotes are not required
when the path contains spaces.
4. From the Shape definition column list, select the column in the map data table whose
values match those in the selected column.
Figure 14.11 shows an example of the room/office column in the S4 Temps.jmp sample data
table.
Figure 14.11 Shape Definition Column Example
5. Click OK.
6. Save the data table.
When you generate a graph in Graph Builder and assign the modified column to the Map
Shape zone, your boundaries appear on the graph.
For numeric columns, the Format Menu appears in the Column Info window. Specify the
format to tell JMP how to display numbers in the column. Latitude and Longitude for
geographic maps are located under Format > Geographic when customizing axes and axes
labels.
Shows latitude and longitude number formatting for geographic maps. Latitude
and longitude options include the following:
Geographic
‒ DDD (degrees)
‒ DMM (degrees and minutes)
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‒ DMS (degrees, minutes, and seconds)
In each format, the last field can have a fraction part. You can specify the direction with
either a signed degree field or a direction suffix. To show a signed degree field, such as
-59°00'00", deselect Direction Indicator. To show the direction suffix, such as 59°00'00" S,
select Direction Indicator.
To use spaces as field separators, deselect Field Punctuation. To use degrees, minutes, and
seconds symbols, select Field Punctuation.
Esri® Shapefiles
The Esri shapefile is a vector data format that contains data about geographic features such as
terrain and oceans. It is developed and regulated by Esri as a specification for geographic
mapping software.
Each shapefile is a set of files with the same name and different extensions.
main file (.shp)
The .shp file contains sequences of points that make up polygons. When opened with JMP, a
.shp file is imported as a JMP table.
•
The Shape column is added during import to uniquely identify each geographic region.
Each coordinate point is in a separate row.
•
The Part column to indicate discontiguous regions, and the XY coordinates (in latitude and
longitude degrees).
JMP supports two-dimensional .shp files (no elevation information).
dBase table (.dbf)
You add a Shape ID column to the .dbf table, which maps to the Shape column in the .shp file.
Add any number of columns that provide common names or values to refer to specific
regions.
To convert an Esri shapefile to a JMP map file:
1. Open the .shp file in JMP.
2. Make sure that the Shape column is the first column in the .shp file. Add formatting and
axis settings for the X and Y columns (optional). Graph Builder uses those settings for the
X and Y axes.
3. Save the .shp file as a JMP data table to the Maps folder with a name that ends in -XY.jmp.
4. Open the .dbf file.
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5. Add a Shape ID column as the first column in the table. This column should be the row
numbers from 1 to n, the number of rows in the data table (Note - You can use Cols >New
Column > Initialize Data > Sequence Data).
6. Assign the Map Role column property to any column that you use for place names in the
Shape role of Graph Builder. To do this, right-click at the top of the column and select
Column Properties > Map Role.
7. Select Shape Name Definition from the drop-down box in the property definition.
8. Save the table as a JMP data table with a name that matches the earlier table and that ends
in -Name.jmp.
JMP looks for these files in two locations. One location is shared by all users on a machine.
This location is:
•
Windows: C:\Program Files\SAS\JMP\<Version Number>\Maps
•
Mac: /Library/Application Support/JMP/<Version Number>/Maps
The other location is specific for an individual user:
•
On Windows: C:\Users\<user name>\AppData\Local\SAS\JMP\Maps
•
On Mac: /Users/<user name>/Library/Application Support/JMP/Maps
Note: On Windows, in JMP Pro, the “JMP” folder is named “JMPPro”. In JMP Shrinkwrap, the
“JMP” folder is named “JMPSW”.
SAS/GRAPH® Map Data Sets
SAS/GRAPH software includes a number of map data sets that can be converted for use with
JMP. The data sets are in the Maps library. The traditional map data sets contain the XY
coordinate data and the feature table contains the common place names. You need to convert
both of these files to JMP data tables for use with JMP.
Most of the traditional map data sets have unprojected latitude and longitude variables in
radians. The data sets can be used with JMP once they have been converted to degrees and the
longitude variable has been adjusted for projection. The following is a DATA step that shows
the conversion process for the Belize data set.
data WORK.BELIZE;
keep id segment x y;
rename segment=Part;
set maps.belize;
if x NE .;
if y NE .;
y=lat*(180/constant('pi'));
x=-long*(180/constant('pi'));
run;
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You can now import the converted file and save it as Belize-XY.jmp.
The next step is to import the matching feature data set (in this case: MAPS.BELIZE2). After
importing the feature data set, move the ID column to the first position in the data table. Then
assign the Map Role column property to the columns that you use for place names in the
Shape role of Graph Builder. To do this, right-click the top of the column and select Column
Properties > Map Role. Then select Shape Name Definition from the drop-down box in the
property definition. For MAPS.BELIZE2, use the IDNAME column. Save the feature data table
as Belize-Name.jmp.
To convert SAS maps, download the SAS to JMP Map Converter add-in from the JMP File
Exchange page. For each map, the add-in reads the data from the two SAS map tables,
rearranges and formats the data and then places it into the two JMP map tables.
Background Maps
Adding map images and boundaries to graphs provides visual context to geospatial data.
Affixing a background map generates an appealing map, providing your data a geographic
context and giving you a whole new way to view your data. For example, you can add a map
to a graph that displays an image of the U.S. Another option is displaying the boundaries for
each state (when data includes the latitudes and longitudes for the U.S.). There are different
types of background maps. Some maps are built into JMP and are delivered as part of the JMP
install. Other maps are retrieved from an Internet source, and still other maps are
user-defined.
The data should have latitudinal and longitudinal coordinates. Otherwise, the map has no
meaning in the context of the data. The X and Y axes also have range requirements based on
the type of map. These requirements are described in the following sections. Simply plot
longitude and latitude on the X and Y axes, and then right-click within the graph and select
Graph > Background Map.
The Background Map window shows two columns of choices: Images and Boundaries. On the
left of the window you can select from two built-in map images, or you can connect to a Web
Map Service to retrieve a background image. On the right side of the window, you can select
political boundaries for a number of regions.
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Figure 14.12 Example of Background Map Options
maps installed
with JMP
boundaries
installed with JMP
Table 14.1 Descriptions of Background Map Options
Images
None
Removes the background map that you selected in the Images
column.
Simple Earth
Shows a map of basic terrain.
Detailed Earth
Shows a high-resolution map with detailed terrain.
NASA Server
Shows a map from the NASA server. Requires an Internet
connection.
Street Map Service
Shows a map with an appropriate amount of detail based on the
display’s zoom level. This allows you to zoom down to the street
level.
Web Map Service
Shows a map from the Uniform Resource Locator (URL) and the
layer that you specify. Requires an Internet connection.
Boundaries
None
Removes the boundaries that you selected in the Boundaries
column.
Boundaries for
various regions
Shows borders for the map regions, such as Canadian provinces,
U.S. counties, U.S. States, and world countries. The list varies
based on your location. The maps that you created from Esri
shapefiles are also listed here.
Two tools are especially helpful when you are viewing a map:
•
The grabber tool (
•
The magnifier tool (
) lets you scroll horizontally and vertically through a map.
) lets you zoom in and out.
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Images in Maps
Every flat map misrepresents the surface of the Earth in some way. Maps cannot match a globe
in truly representing the surface of the entire Earth. A map projection is used to portray all or
part of the round Earth on a flat surface. This cannot be done without some distortion. Every
projection has its own set of advantages and disadvantages. A map can show one or more, but
not all, of the following: true direction, distance, area, or shape. JMP employs a couple of
projections (Albers Equal Area Conic and Kavrayskiy VII) for its maps. Within Images, you
can select from two built-in map images, or you can connect to a Web Map Service to retrieve
a background image.
Earth Images Installed with JMP
JMP provides two levels of earth imagery; simple and detailed. Both maps show features such
as bodies of water and terrain. However, detailed maps show more precise terrain. And with
detailed maps, you can zoom in farther, and the map features remain clear. Image maps are
raster images. The maps wrap horizontally, so you continue to see map details as you scroll
from left to right. The maps do not wrap vertically. Beyond the -90 and 90 y-axis range, a plain
background appears instead of the map.
Figure 14.13 Examples of Simple and Detailed Maps
Simple Earth Image
Detailed Earth Image
As its name suggests, Simple Earth is a relatively unadorned image of the earth’s geography. It
does not show clouds or arctic ice, and it uses a green and brown color scheme for the land
and a constant deep blue for water. Detailed Earth has a softer color scheme than Simple
Earth, lighter greens and browns for the land, as well as variation in the blue for the water.
Detailed Earth also has a slightly higher resolution than Simple Earth. The higher resolution
lets you zoom into a graph further with Detailed Earth than with Simple Earth before the
quality of the background image begins to blur.
Another feature of Simple Earth and Detailed Earth is the ability to wrap. The Earth is round,
and when you cross 180° longitude, the Earth does not end. The longitudinal value continues
from -180° and increases. The map wraps continuously in the horizontal direction, much as
the Earth does. The background map does not wrap in the vertical direction.
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Simple Earth and Detailed Earth both support a geodesic scaling. In Figure 14.13 on page 248,
the Earth appears as a rectangle, where the width is twice as wide as the height. If we were to
take this rectangle and roll it up, we would have a cylinder. In reality, we know that the Earth
does not form a cylinder, but rather a sphere. You can use a geodesic scaling, which
transforms the map to a more realistic representation of the Earth. To use the geodesic scaling,
change the type of scale on the axes.
To change the axes scale:
1. Right-click the X or Y axis and then select Axis Settings.
2. Change the Scale Type to Geodesic or Geodesic US.
Figure 14.14 Y Axis Setting Window
Both choices transform the map to a geodesic scaling. Use Geodesic US if you are viewing a
map of the continental US and you want Alaska and Hawaii to be included in the map. It is
important to note that you must set the scale to geodesic for both axes to get the
transformation. You will not see a change in the map after setting only one of the axes. In the
following figure, Simple Earth is used as the background map with the axes set to use a
geodesic scale. The axes lines are turned on as well. Notice the longitudinal lines are now
curved, instead of straight.
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Figure 14.15 Simple Earth Background Map - Axes set to Geodesic Scale
Since Detailed and Simple Earth are built into JMP, these options work any time, without a
network connection. However, these images might not be all that you want, or they might not
be detailed at the resolution that you need. If this is the case, and if you have an Internet
connection, you can connect to a Web Map Service to retrieve a map image that meets your
needs.
Maps from the Internet
The National Aeronautics and Space Administration (NASA) and other organizations provide
map image data using a protocol called Web Map Service (WMS). These maps have the
advantage of showing the most up-to-date geographical information. However, the display of
the maps can be slow depending on the response time of the server, and the sites can change
or disappear at any time. An Internet connection is required to access the information.
Figure 14.16 Examples of NASA and WMS maps
NASA image
WMS image from the MetaCarta™ Server
The NASA server provides maps for the entire Earth. The following figure displays the Earth
using the NASA server as its source for the background map. It also has a boundary map
turned on, to show the outlines of the countries.
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Figure 14.17 NASA Server Map Example
Not only does this server cover the entire Earth, but you can also zoom in on a much smaller
area of the Earth and still get a reasonable map. The following figure displays the Colorado
River running through the Grand Canyon in Arizona. The Grand Canyon Village is visible in
the bottom of the map.
Figure 14.18 NASA Server Map Example - Zoom in on Colorado
If you look at the axes values, you can see that the area is less than 1/10° by 1/10°. The Simple
Earth and Detailed Earth background maps do not display that type of resolution. The NASA
server provides a fairly detailed view of any land mass on Earth. Water, however, is simply
filled in as black. The NASA server is free to access, but it is also limited in availability. If the
server is temporarily unavailable or becomes overloaded with requests, it delivers an error
message instead of the requested map.
Another Internet-based option for background maps is a Web Map Service (WMS). The WMS
option enables you to specify any server that supports the WMS interface. The NASA server is
an example of a WMS server, but we have provided the URL and a layer name for you. With
the WMS option, you must know the URL to the WMS server and a layer name supported by
the server. Most WMS servers support multiple layers. For example, one layer can show
terrain, another layer can show roads, and still another layer can include water, such as rivers
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and lakes. By specifying the URL for the server and the layer, JMP can make a request to the
server and then display the map that is returned.
Unlike with simple and detailed maps, WMS maps do not wrap. You can scroll horizontally
and vertically. However, beyond the -180 to 180 (x axis) and -90 to 90 (y axis) ranges, a plain
background appears instead of the map. The limits of the axes are used to define the limits of
the map that is displayed.
In order to use the WMS option for a background map, you need to decide which WMS server
to use. There are many WMS servers freely available from the Internet. Most of them provide
maps only for a particular area of the world, and each of them supports their own layers. So
you have to search for the appropriate WMS server for your particular situation.
You can search for WMS servers on the Internet using your favorite search engine. Once you
find one, you need to discover the layers that it supports. For this, you can use the WMS
Explorer add-in. The WMS Explorer add-in generates a list of all the layers available on a
server. You can select a layer from the list to see what it looks like. You can download the
WMS Explorer add-in from the JMP File Exchange page.
Note: To use the WMS Explorer add-in and the WMS background map capabilities of JMP,
your computer must be connected to the Internet.
To locate a server, launch the add-in through the menu items Add-Ins > Map Images > WMS
Explorer. The add-in presents a text box for entering the url of a known WMS server.
Alternatively, you can make a selection from a drop-down list of pre-discovered WMS servers
(the list can be out of date). After specifying a WMS server, select Get Layers. Using Get Layers
is not necessary if selecting from the drop-down list or if clicking Enter after entering a URL.
This sends a request to the WMS server for a list of layers that the server supports. The
returned list appears in the list box on the left, labeled Layers. A map of the world appears as
an outline in the graph to the right. Selecting a layer makes a request to the WMS server to
return a map, using the specified layer, that represents the entire earth. Selecting a different
layer generates a different map.
It is important to note that not all maps are generated to cover the entire earth (for example,
some WMS servers might provide mapping data for a particular county, within a state). In that
case, it is likely that selecting a layer will not generate any visible map. You might have to
zoom in on the appropriate area before any image map is visible. The standard JMP toolbar is
available in the add-in window and the zoom tool works just like it does in any JMP window.
The graph is a typical graph in JMP, which means that all the regular JMP controls are
available to you. You can adjust the axes or use the zoom tool (found on the hidden menu bar)
just as you would in JMP. You can also right-mouse-click to select Size/Scale > Size to Isometric
to get your graph back into a proper aspect ratio. You can also select Background Map, where
you can adjust the boundary map.
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Once a desirable map is determined, note the URL in the text box at the top and the selected
layer in the Layers list. This is the information that you need to enter in the background map
window when WMS is selected as the type of image background map.
Because requests are being made to a server across the Internet, there are a number of
conditions that can generate an error. WMS servers often have limited availability and
sometimes are not available at all. Occasionally a WMS server might return a name of a layer
that it no longer supports. In these types of cases (and others), a server usually returns an error
message in lieu of a map. If that happens, the error message is displayed below the Layers list
in an area labeled Errors.
Boundaries
JMP can display boundaries (such as U.S. states or French region boundaries). These
boundaries draw an outline around a defined area and can be displayed alone on a graph or
combined with image data. Several boundaries are installed with JMP. Alternatively, you can
create your own boundaries from Esri shapefiles or from scratch. Because of this, the list of
Boundaries that you see in the Set Background Map window can be different.
When you add shape files to the built-in locations in JMP, they are available not only for the
Graph Builder platform, but also for the Boundaries option in the Background Map window.
In this way, you can add more political boundaries for use with background maps.
Boundary-style maps are vector-based shapes.
Figure 14.19 Example of U.S. State Boundaries
Add a Background Map and Boundaries
To add a background map and boundaries:
1. Right-click a blank area on the graph and select Background Map (or select Graph >
Background Map in Graph Builder).
The Set Background Map window appears (Figure 14.12).
2. To display a background, do one of the following:
‒ Select Simple Earth, Detailed Earth, NASA server, or Street Map Service in the Images
column.
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‒ Select Web Map Service and paste a WMS URL next to URL. Type the layer identifier
next to Layer.
3. To display geographic borders on the map, select an option in the Boundaries column (If
you installed your own boundary shapefiles, they are also listed in this column).
4. Click OK.
If the NASA map, Street Map, or WMS map does not appear after you add it, the map
server might not be available. View the error log to verify the problem.
Change Map Colors and Transparency
To change colors and transparency for a map, right-click on the color bar in the legend. The
right-click options vary, depending on whether the Color variable is continuous or categorical
(nominal or ordinal). However, for both types of variables, you can change the transparency.
Continuous variables use a color gradient. To modify the gradient, right-click the color bar
and select Gradient.
Figure 14.20 Gradient Settings Window
From this window, you can do the following:
•
Change the Color Theme or define a custom color theme.
•
Specify the Number of labels that you want to include. The default value of zero means
that all labels are shown.
•
Choose a Scale Type:
‒ Linear (default) is a piecewise linear between the minimum, center, and maximum
values.
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‒ Quantile is a piecewise linear in the quantile space.
‒ Standard Deviation is a piecewise linear between the minimum and maximum and the
selected number of standard deviation offsets from the mean.
‒ Log is linear in the log space. The center value is ignored.
•
Choose a Range Type:
‒ Default is similar to an axis, containing round numbers, usually beyond the data range.
‒ Exact Data Range enables you to specify Minimum and Maximum values in the range.
‒ Middle 90% scales from the 5th to the 95th quantiles, with adjustments for degenerate
ranges.
Note: If you specify values in the edit boxes, these can override the Range Type values.
•
Specify the Minimum, Center, and Maximum values in your gradient.
•
Select Horizontal to change the orientation of the gradient colors in the legend.
•
Select Reverse Colors to flip the color theme.
•
Select Reverse Scale to flip the values of your gradient.
•
Select Discrete Colors to produce a stepped gradient.
Categorical (nominal or ordinal) variables use a singular coloring system, where each level of
the variable is colored differently.
To change the color of one of the variable levels, proceed as follows:
1. Right-click on the color of the variable level that you want to change and select Fill Color.
2. Select the new color.
Note: For more details about color options, see the Using JMP book.
Examples of Creating Maps
The following are examples of creating and using maps to find and display patterns in your
geographical data.
Louisiana Parishes Example
In this example you work with custom map files and then create custom maps in two different
ways:
•
Set up custom map files initially and save them in the predetermined location. JMP finds
and uses them in the future with any appropriate data.
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Point to specific predefined map files directly from your data. This step might be required
each time you want to specify custom maps.
Set Up Automatic Custom Maps
Suppose that you have downloaded Esri shapefiles from the Internet and you want to use
them as your map files in JMP. The shapefiles are named Parishes.shp and Parishes.dbf. These
files contain coordinates and information about the parishes (or counties) of Louisiana.
Note: Path names in this section refer to the “JMP” folder. On Windows, in JMP Pro, the
“JMP” folder is named “JMPPro”. In JMP Shrinkwrap, the “JMP” folder is named “JMPSW”.
Save the .shp File
Save the .shp file with the appropriate name and in the correct directory, as follows:
1. In JMP, open the Parishes.shp file from the following default location:
‒ On Windows: C:\Program Files\SAS\JMP\<Version Number>\Samples\Import Data
‒ On Mac: /Library/Application Support/JMP/<Version Number>/Samples/Import Data
Note: If you cannot see the file, you might need to change the file type to All Files.
JMP opens the file as Parishes. The .shp file contains the x and y coordinates.
2. Save the Parishes file with the following name and extension: Parishes-XY.jmp. Save the file
here:
‒ On Windows: C:\Users\<user name>\AppData\Local\SAS\JMP\Maps
‒ On Mac: /Users/<user name>/Library/Application Support/JMP/Maps
3. Close the Parishes-XY.jmp file.
Save the .dbf File
Perform the initial setup and save the .dbf file, as follows:
1. Open the Parishes.dbf file from the following default location:
‒ On Windows: C:\Program Files\SAS\JMP\<Version Number>\Samples\Import Data
‒ On Mac: /Library/Application Support/JMP/<Version Number>/Samples/Import Data
Note: If you cannot see the file, you might need to change the file type to All Files.
JMP opens the file as Parishes. The .dbf file contains identifying information.
2. In the Parishes file, add a new column. Name it Shape ID. Drag and drop it to be the first
column.
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3. In the first three rows of the Shape ID column, type 1, 2, and 3 (Note - You can also use Cols
> New Column > Initialize Data > Sequence Data).
4. Select all three cells, right-click, and select Fill > Continue sequence to end of table.
Figure 14.21 Shape ID Column in Parishes File
5. Right-click on the PARISH column and select Column Info.
6. Select Column Properties > Map Role.
7. Select Shape Name Definition.
8. Click OK.
9. Save the Parishes file with the following name and extension: Parishes-Name.jmp. Save the
file here:
‒ On Windows: C:\Users\<user name>\AppData\Local\SAS\JMP\Maps
‒ On Mac: /Users/<user name>/Library/Application Support/JMP/Maps
10. Close the Parishes-Name.jmp file.
Create the Map in Graph Builder
Once the map files have been set up, you can use them. The Katrina.jmp data table contains
data on Hurricane Katrina’s impact by parish. You want to visually see how the population of
the parishes changed after Hurricane Katrina. Proceed as follows:
1. Select Help > Sample Data Library and open Katrina.jmp.
2. Right-click the Parish column and select Column Properties > Map Role.
3. Select Shape Name Use.
4. Click the Map name data table button
you previously created.
and browse to select Parishes-Name.jmp, which
This tells JMP where the data tables containing the map information reside.
5. Select PARISH from the Shape definition column list.
In Parishes-Name.jmp, the PARISH column has the Shape Name Definition Map Role
property assigned. The column consists of map shape data for each parish.
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6. Select Graph > Graph Builder.
7. Drag and drop Parish into the Map Shape zone.
The map appears automatically, since you defined the Parish column using the custom
map files.
8. Drag and drop Population into the Color zone.
9. Drag and drop Date into the Group X zone.
Figure 14.22 Population of Parishes Before and After Katrina
10. Select the Magnifier tool to zoom in on the Orleans parish in both maps (Figure 14.23)
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Figure 14.23 Orleans Parish
You can clearly see the drop in population as a result of Hurricane Katrina. The population of
the Orleans parish went from 437,186 in July 2005 to 158,353 in January 2006.
Point to Existing Map Files Directly from Your Data
Suppose that you already have your custom map files and they are named appropriately. Your
map files are US-MSA-Name.jmp and US-MSA-XY.jmp. They are saved in the sample data
folder.
The PopulationByMSA.jmp data table contains population data from the years 2000 and 2010
for the metropolitan statistical areas (MSAs) of the United States. This example shows how the
data table has been set up to create a map.
Add the Map Role Column Property
1. Select Help > Sample Data Library and open PopulationByMSA.jmp.
2. Right-click the Metropolitan Statistical Area column and select Column Info.
3. Select Column Properties > Map Role.
4. Select Shape Name Use.
5. Next to the Map name data table, type $SAMPLE_DATA/US-MSA-Name.jmp.
This tells JMP where the data tables containing the map information reside.
6. Select MSA_Name from the Shape definition column list.
MSA_Name is the specific column within the US-MSA-Name.jmp data table that contains
the unique names for each metropolitan statistical area. Notice that the MSA_Name
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column has the Shape Name Definition Map Role property assigned, as part of correctly
defining the map files.
Note: Remember, the Shape ID column in the -Name data table maps to the Shape ID
column in the -XY data table. This means that indicating where the -Name data table
resides links it to the -XY data table, so that JMP has everything that it needs to create the
map.
Figure 14.24 Map Role Column Property
7. Click OK.
Create the Map in Graph Builder
Once the Map Role column property has been set up, you can perform your analysis. You
want to visually see how the population has changed in the metropolitan statistical areas of
the United States between the years 2000 and 2010.
1. Select Graph > Graph Builder.
2. Drag and drop Metropolitan Statistical Area into the Map Shape zone.
Since you have defined the Map Role column property on this column, the map appears.
3. Drag and drop Change in Population to the Color zone.
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Figure 14.25 Change in Population for Metropolitan Statistical Areas
4. Select the Magnifier tool to zoom in on the state of Florida.
5. Select the Arrow tool and click on the red area.
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Figure 14.26 Population Change of Palm Coast, Florida
6. Select the Magnifier tool and hold down the ALT key while clicking on the map to zoom
out.
7. Select the Magnifier tool and zoom in on the state of Utah.
8. Select the Arrow tool and click on the area that is slightly red.
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Figure 14.27 Population Change of St. George, Utah
You can see that the areas of Palm Coast, Florida, and St. George, Utah had the most
population change between 2000 and 2010. The Palm Coast area saw a population change of
92%, and the St. George area saw a population change of about 53%.
Hurricane Tracking Examples
This example uses the Hurricanes.jmp sample data table, which contains data on hurricanes
that have affected the east coast of the United States. Adding a background map helps you see
the areas the hurricanes affected. A script has been developed for this example and is part of
the data table.
1. Select Help > Sample Data Library and open Hurricanes.jmp.
2. Run the Bubble Plot script (Bubble Plot > Run Script).
3. Drag the Date slider to the right as shown in Figure 14.28.
4. Click the red dot to display the name of the hurricane. The date appears in the upper left
corner of the window. The red dot shows the location of Hurricane Paloma on November
14, 2008.
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Figure 14.28 Bubble Plot of Hurricanes.jmp
Note that even though the location of the hurricane is plotted, it does not really tell us
where it is. The axes information is there (27° North latitude and 86° West longitude), but
we need a little more context. It is most likely over the middle of the Atlantic, but is it over
a small island? This could make a big difference, especially for the inhabitants of the small
island. Obviously, a map in the background of our graph would add a good deal of
information.
5. Right-click the graph and select Background Map. The Set Background Map window
appears (Figure 14.12).
6. Select Simple Earth and click OK.
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Figure 14.29 Bubble Plot of Hurricanes.jmp with Background Map
Now the coordinates make geographic sense. Click Run to view the animation of the hurricane
data moving over the background map. Experiment with different options and view the
displays. Adjust the axes or use the zoom tool to change what part of the world you are
viewing. The map adjusts as the view does. You can also right-click the graph and select Size/
Scale->Size to Isometric to get the aspect ratio of your graph to be proportional.
The next example uses the Katrina Data.jmp sample data table, which contains data on
hurricane Katrina such as latitude, longitude, date, wind speed, pressure, and status. Adding
a background map helps you see the path the hurricane took and impact that it had on land
based on size and strength. A script has been developed for this example and is part of the
data table.
1. Select Help > Sample Data Library and open Katrina Data.jmp.
2. Select Graph > Bubble Plot.
3. Select LAT and click Y.
4. Select LON and click X.
5. Select Date and click Time.
6. Select WIND and click Sizes.
7. Select Stat and click Coloring.
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Figure 14.30 Bubble Plot Setup of Katrina Data.jmp
8. Click OK.
The following image appears. The yellow dot shows the location of Tropical Depression
Katrina on August 23, 2005.
Figure 14.31 Bubble Plot of Katrina Data.jmp
Note that even though the location of the storm is plotted, it does not really tell us where it
is. To add more context, add a map in the background.
9. Right-click the graph and select Background Map. The Set Background Map window
appears.
10. Select Detailed Earth and click OK.
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Figure 14.32 Bubble Plot of Katrina Data.jmp with Background Map
Now the coordinates make geographic sense. You can edit the axes and the size/scale to
change the way the graph appears.
11. Right-click the X axis (LON) and select Axis Settings. The X Axis Specification window
appears.
12. Select Scale > Geodesic US.
13. Select Format > Geographic > Longitude DMM.
14. Click OK.
15. Repeat the same for the Y axis (LAT) except select Format > Geographic > Latitude DMM.
16. Right-click the map and select Size/Scale > Size to Isometric.
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Figure 14.33 Bubble Plot of Katrina Data.jmp with Background Map
Click Run to view the animation of the hurricane data moving over the background map. You
can manipulate the speed and the bubble size. Experiment with different options and view the
displays. Adjust the axes or use the zoom tool to change what part of the area you are
viewing. Add boundaries to the states. The map adjusts as the view does.
Office Temperature Study
This example demonstrates the creation of a custom background map for an office
temperature study and how JMP was used to visualize the results. Data was collected
concerning office temperatures for a floor within a building. A map was created for the floor
using the Custom Map Creator add-in from the JMP File Exchange (https://
community.jmp.com/docs/DOC-6218). Using Graph Builder, the office temperature results
were then analyzed visually.
The map shown below is the floor, grouped by time of day, with color reflecting the Fahrenheit
value. Exploring data visually in this way can give hints as to what factors are affecting office
temperature. Looking at this map, it appears the offices on the east side of the building are
warmer in the mornings than they are in the afternoons. On the western side of the building,
the opposite appears to be true. From this visualization, we might expect that both of these
variables are affecting office temperatures, or perhaps that the interaction between these terms
is significant. Such visuals help guide decision-making during the analysis.
Chapter 14
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Create Maps
Examples of Creating Maps
Figure 14.34 Room/Office Colored by Fahrenheit and Grouped by Time of Day
First, data was collected and input into a data table (S4 Temps.jmp). Note the Room/Office
column. It contains the unique names for each office and was assigned the Map Role to
correctly define the map files.
Figure 14.35 S4 Temps.jmp Data Table
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Then, a map of the floor was created using the Custom Map Creator add-in, which you can
download from the JMP File Exchange at https://community.jmp.com/docs/DOC-6218. The
add-in creates two tables to define the shapes; an XY table and a Name table. The instructions
below describe how it was built.
Create a Map of the Floor
1. Launch the add-in through the menu items Add-Ins > Map Shapes > Custom Map Creator.
Two tables open in the background followed by the Custom Map Creator Window.
2. Drag a background image into the graph frame. An image of the floor plan was available.
3. Perform any resizing on the background image and graph the frame.
4. Name the table (for example, S4).
5. Click Next.
6. Name the shape that you are about to define. For this example, each office was
individually named for the map (for example, S4001).
7. Within the graph frame, use your mouse to click all of the boundaries of the shape that you
want to define. A line appears that connects all of the boundary points.
8. As soon as you finish defining the boundaries of the shape, click Next Shape. Continue
adding shapes until you have completed the floor plan. Note that you do not need to
connect the final boundary point; the add-in automatically does that for you when you
click Next Shape.
9. The line size and color can be changed. In addition, checking Fill Shapes fills each shape
with a random color.
10. Click Finish.
The custom map files were created and named appropriately. The map files are S4-Name.jmp
and S4-XY.jmp and have been saved in the JMP Samples\Data folder.
Add the Map Role Column Property
Note: Path names in this section refer to the “JMP” folder. On Windows, in JMP Pro, the
“JMP” folder is named “JMPPro”. In JMP Shrinkwrap, the “JMP” folder is named “JMPSW”.
The S4 Temps.jmp data table contains office data over a three-day period. Set up the Map Role
column property in the data table, as follows:
1. Select Help > Sample Data Library and open S4 Temps.jmp.
2. Right-click on the Room/Office column and select Column Info.
3. Select Column Properties > Map Role.
4. Select Shape Name Use.
5. Next to Map name data table, type $SAMPLE_DATA\S4-Name.jmp.
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271
This tells JMP where the data tables containing the map information reside.
6. Select room from the Shape definition column list.
Room is the specific column within the S4-Name.jmp data table that contains the unique
names for each office. Notice that the room column has the Shape Name Definition Map
Role property assigned, as part of correctly defining the map files.
Note: Remember, the Shape ID column in the -Name data table maps to the Shape ID
column in the -XY data table. This means that indicating where the -Name data table
resides links it to the -XY data table, so that JMP has everything that it needs to create the
map.
Figure 14.36 Map Role Column Property
7. Click OK.
Once the Map Role column property has been set up, you can perform your analysis. You
want to visually see the differences in office temperatures throughout the floor.
1. Select Graph > Graph Builder.
2. Drag and drop room/office into the Map Shape zone.
Since you have defined the Map Role column property on this column, the map appears.
3. Drag and drop Fahrenheit to the Color zone.
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Figure 14.37 Room/Office Colored by Fahrenheit
4. Drag and drop Time of Day onto Group X zone.
Figure 14.38 Room/Office Colored by Fahrenheit and Grouped by Time of Day
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Examples of Creating Maps
273
Note that only the offices that were part of the study and were created using the Custom Map
Creator add-in are displayed. To add the entire floor plan image, the original floor plan
graphic was dragged and dropped onto the Graph Builder window to create Figure 14.39.
To view Figure 14.39, select Help > Sample Data Library and open S4 Temps.jmp and run the
by Time of Day script.
Figure 14.39 Room/Office Map with Original Floor Plan
There are several scripts provided with the data table that you can run to view the various
analysis and modeling that can be performed and visually displayed.
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References
Box, G.E.P., Hunter,W.G., and Hunter, J.S. (1978), Statistics for Experimenters, New York: John
Wiley and Sons, Inc.
Cornell, J.A. (1990), Experiments with Mixtures, Second Edition, New York: John Wiley and
Sons.
Inselberg, A. (1985) “The Plane with Parallel Coordinates.” Visual Computing 1. pp 69–91.
Mardia, K.V., Kent, J.T., and Bibby J.M. (1979). Multivariate Analysis, New York: Academic
Press.
Wegman, E. J. (1990) “Hyperdimensional Data Analysis using Parallel Coordinates.” Journal of
the American Statistical Association 85, pp. 664–675.
276
Appendix 15
Essential Graphing
Index
Essential Graphing
Numerics
3D scatterplots, see Scatterplot 3D platform
A
Add Error Bars to Mean option 211, 214, 218
Add option 45
Aggregation options 135
Area element 42
Area Style option 45
Arrange Plots option 81, 162
Axes option 105
Axis Options window 97
B
Background Color option 104
Background Maps 246
Bar Chart option 219
Bar element 42
Bar Style option 45
Biplot Rays option 100
Box option 105
Box Plot element 43
Box Style option 45
brush tool 133
Bubble Plot platform 125
animating dynamic 131
brush tool 133
By variable 130
categorical Y variable 145–146
dynamic 125, 127–128, 131
example 127–128, 137–146
launching 128–130
options 133–137
selecting bubbles 132
splitting a bubble 139–141
static 125, 130, 141–145
Bubble Size slider 131
C
Caption Box element 43
Categorical Color Theme option 44
Categories variable 169–170
Categories, X, Levels button 209, 213
Cell Plot platform 157
examples 159, 163–164
launching 159
options 161–163
report window 160–161
Center at zero option 151, 160
Change Contours options 117–120
Change to option 45
Chart Orientation options 210
Chart platform 205
coloring bars 216–217
examples 207–208, 220–230
launching 209–214
legends 215
options 217–220
Ordering 216
report window 215–217
Chart Type options 210
Color option 48
Color Theme option 117, 134, 162
Color zone 41
Coloring variable 129, 169–179
Column Bandwidth option 103
Combine All option 134
Combine button 132
Confidence of Fit option 45
Confidence of Prediction option 45
Connect Color option 84
Connect Points option 84, 98, 210, 220
Connect Thru Missing option 81
278
Continuous Color Theme option 44
Contour element 42
Contour Formula button 200
Contour Plot
Alpha slider 116
Specify Grid option 121
Use Table Data option 121
Contour Plot platform 111
example 113
launching 114–115
options 116–121
report window 115
using formulas 121
Contour Quantile option 102
Contour Values buttons 115
Coverage option 45
D
Degree option 45
Delaunay triangulation 117, 121
Density Contour Controls options 102–103
Density Ellipse option 191
detailed maps 248
Draw option 133
Drop Line options 98–99
dynamic bubble plots 125, 131
E
Ellipse element 42
Ellipses Coverage option 191
Ellipses Transparency option 192
Ellipsoid Coverage option 99
Ellipsoid Transparency option 99
Equation option 45
Error Bars option 45
F
Fill Areas 117
Fill Areas option 115, 117–118
Fit Line command 191
Formula element 43
Freq zone 41
Function Plot option 84
Index
Essential Graphing
G
Generate Grid 121
Graph Builder 31, 33
adding multiple variables 50
adding variables 49
buttons 43
changing the legend 52
elements 42
examples 33–74
launching 39
maps 53, 234, 255
moving grouping labels 49
moving variables 50
options 44
right-click menus 44
zones 40–43
Graph Builder Customize 47
Graph Type option 162
Grids option 105
Group button 189, 192
Group By option 192
Group X zone 41
Group Y zone 41
Grouping button 79, 210, 213
H
Heatmap element 43
Hide Lights Border option 104
Histogram element 43
Horizontal option 218
I
ID variable 129, 139–141
Images 248
Include Missing Categories option 44
Interquartile Range 212
J
jitter 46
JMP Starter 27
L
Label Contours option 117
279
Index
Essential Graphing
Label Format option 220
Label option 46, 134
Label Options for charts 218
Launch Analysis option 44
Left Scale/Right Scale button 79
Left Y Log Scale option 79
Legend 41, 134, 161
Legend Position option 44
Level Options 218
Level Orientation option 48
Levels in View option 48
Levels per Row option 48
Line Chart option 219
Line element 42
Line of Fit element 42
Line Style option 84
Line Width option 84
Line Width slider 106
Lock Scales option 44, 135
M
Make into Data Table option 44
Map Role 241
Map Shape zone 41, 53, 235
Map Shapes element 43
Maps 231
maps
create custom 241
simple and detailed 248
WMS 250
Marker Quality slider 106
Marker Size slider 106
Marker Transparency slider 106
Matrix Format options 189–190
menu tips 26
middle fifty 212
midspread 212
Mixture column property 201
Mosaic element 43
Move Backward option 46
Move Forward option 46
N
N Categories 212
Needle Chart option 220
Needle option 84
next button 131
No Overlay option 81
No Separator Lines option 162
Nonpar Density command 192
Nonpar Density Contour option 99
Nonparametric Density Contours
option 101–103
Normal Contour Ellipsoids option 98, 101–108
Number of Levels option 46, 48–49
O
Ordering variable 169–177
Orient Shapes option 133
Orthographic option 105
Outliers option 46
Overlay Color option 220
Overlay Groups option 81–82
Overlay Marker Color option 84
Overlay Marker option 84, 220
Overlay option in Chart 210, 218
Overlay Plot platform 75
example 77–78, 84–88
launching 78–79
options 80–84
report window 79
Overlay Y’s option 81
Overlay zone 41
P
Page zone 41
Parallel Plot platform 147
examples 149–150, 154–156
launching 150
options 153
report window 151–153
Pen Style option 220
Percent for quantiles option 211
Perspective slider 106
Pie Chart option 218
Pie element 43
Pie Style option 46
play/pause button 131
Point Chart option 220
Points element 42
280
Points Jittered option 187, 191
previous button 131
Principal Components option 99
R
Range Chart option 218
Range Plot option 81
Ref Labels option 202
Remove command 46, 48
Remove Prin Comp option 100
Reset option 104–105
Resolution option 103
Response Axis option 46
Retrieve Contours 120
Reverse Colors option 117
Revert Color Theme option 134
Right Y Log Scale option 79
Root Mean Square Error option 46
Rotated Components option 100
Row order option 46
Rows option 104
S
Sampling option 44
Save Contours 120
Save for Adobe Flash platform (.SWF)
option 135
Save options
in Contour Plot 117, 120
Save Prin Components option 100
Save Rotated Components option 100
Save Triangulation 121
Scale Uniformly option 151, 160
Scatterplot 3D platform 89
adjusting axes 96
assigning markers 97
changing variables 96
coloring points 97
example 91
launching 92
options 98, 103–106
report window 93–98
Settings window 104
spinning the plot 95
Scatterplot Matrix platform 185
Index
Essential Graphing
examples 187, 192–194
launching 188
options 191
report window 190
Script options
in Contour Plot 117
Selectable Across Gaps option 134
selecting
bubbles 132
Separate Axes option 81–82
Set Shape option 133
Shaded Ellipses option 191
Show ArcBall option 104
Show Boundary option 116
Show Contours option 116
Show Control Panel
Alpha shapes 116
Show Control Panel option 44
Show Controls option 98, 116
Show Data Points option 116
Show Error Bars option 220
Show Footer option 44
Show Legend option 44
Show Level Legend option 219
Show Missing Data Points option 116
Show Missing Shapes option 46
Show Points option
for Chart 210, 220
for Overlay Plot 83
for Scatterplot 3D 98
for Scatterplot Matrix 191
for Ternary Plot 202
Show Reversing Checkboxes option 153
Show Roles option 134–137
Show Separate Axes option 219
Show Title option 48
Show Y Legend option 219
simple maps 248
Size zone 41
Sizes variable 129, 169–170
Smoother element 42
Sort Ascending option 162
Sort Descending option 162
Sort X option 79
Specify Contours 119
Specify Grid button 115, 121
281
Index
Essential Graphing
Speed slider 131
Split All option 134
Split button 132, 140
Stack Bars option 218
static bubble plots 125, 130, 141–145
Statistics button 209, 211–213
Std Prin Components option 99
Step option 84
Summary Statistic option (in Graph
Builder) 46
Swap command 48
T
Ternary Plot platform 195
examples 197–199, 203
launching 199
options 201
report window 200–203
Text Size slider 106
Thick Connecting Line option 219
Time variable 129, 131, 137–139
Title Orientation option 48
tooltips 26
Trail Bubbles option 133
Trail Lines option 134
Transform
None option 117
Range Normalized option 117
Treemap element 43
Treemap platform 165
By variable 169
Categories variable 169–170
Coloring variable 169–179
examples 167–168, 174–184
launching 168–179
Layout 169
options 173–174
Ordering variable 169–177
report window 171–173
Sizes variable 169–170
tutorials 25
U
Ungroup Charts option 219
Ungroup Plots option 81
Uniform Y Scale option 81
Use Hardware Acceleration option 104
Use Table Data option 115
V
Vertical option 46, 218
W-Z
Wall Color option 104
Walls option 105
Wrap zone 41
X Group Edge option 48
X Log Scale option 79
X option in Graph Builder 47
X Position option 47
X zone 41
X, Plotting button 200
Y Group Edge option 48
Y option in Graph Builder 47
Y Options
in Chart 218–219
in Overlay Plot 83–84
Y Position option 47
Y zone 41
Zoom slider 105
282
Index
Essential Graphing
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