## The Grammar of GraphicsPreface to First Edition Before writing the graphics for SYSTAT in the 1980’s, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that could draw every statistical graphic I had ever seen. The structure of the program was a collection of procedures named after the basic graph types they p- duced. The graphics code was roughly one and a half megabytes in size. In the early 1990’s, I redesigned the SYSTAT graphics package using - ject-based technology. I intended to produce a more comprehensive and - namic package. I accomplished this by embedding graphical elements in a tree structure. Rendering graphics was done by walking the tree and editing worked by adding and deleting nodes. The code size fell to under a megabyte. In the late 1990’s, I collaborated with Dan Rope at the Bureau of Labor Statistics and Dan Carr at George Mason University to produce a graphics p- duction library called GPL, this time in Java. Our goal was to develop graphics components. This book was nourished by that project. So far, the GPL code size is under half a megabyte. |

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### Contents

Introduction | 1 |

11 Graphics Versus Charts | 2 |

12 ObjectOriented Design | 3 |

13 An ObjectOriented Graphics System | 6 |

14 An Example | 8 |

15 What This Book Is Not | 13 |

16 Background | 18 |

Part 1 | 21 |

105 Aesthetic Attributes | 274 |

106 Examples | 293 |

107 Summary | 316 |

108 Sequel | 318 |

Facets | 319 |

112 Algebra of Facets | 320 |

113 Examples | 325 |

114 Sequel | 343 |

How To Make a Pie | 23 |

21 Definitions | 25 |

22 Recipe | 31 |

23 Notation | 38 |

24 Sequel | 40 |

Data | 41 |

31 Data Functions | 42 |

32 Empirical Data | 44 |

33 Abstract Data | 48 |

34 Metadata | 51 |

36 Sequel | 54 |

Variables | 55 |

41 Transforms | 56 |

42 Examples | 57 |

43 Sequel | 61 |

Algebra | 63 |

52 Examples | 73 |

53 Other Algebras | 80 |

54 Sequel | 83 |

Scales | 85 |

62 Scale Transformations | 93 |

63 Sequel | 109 |

Statistics | 111 |

71 Methods | 113 |

72 Examples | 123 |

73 Summary | 152 |

74 Sequel | 154 |

Geometry | 155 |

81 Examples | 158 |

82 Summary | 177 |

83 Sequel | 178 |

Coordinates | 179 |

91 Transformations of the Plane | 180 |

92 Projections onto the Plane | 227 |

93 3D Coordinate Systems | 244 |

94 HighDimensional Spaces | 248 |

95 Tools and Coordinates | 253 |

96 Sequel | 254 |

Aesthetics | 255 |

101 Continuous Scales | 256 |

102 Categorical Scales | 261 |

103 Dimensions | 265 |

104 Realism | 270 |

Guides | 345 |

122 Annotation Guides | 350 |

123 Sequel | 354 |

Semantics | 355 |

Space | 357 |

131 Mathematical Space | 361 |

132 Psychological Space | 376 |

133 Graphing Space | 379 |

134 Sequel | 403 |

Time | 405 |

142 Psychology of Time | 422 |

143 Graphing Time | 425 |

144 Sequel | 447 |

Uncertainty | 449 |

752 Psychology of Uncertainty | 464 |

153 Graphing Uncertainty | 466 |

154 Sequel | 486 |

Analysis | 487 |

161 Variance Analysis | 488 |

162 Shape Analysis | 494 |

163 Graph Drawing | 498 |

164 Sequence Analysis | 503 |

165 Pattern Analysis | 515 |

166 Sequel | 531 |

Control | 533 |

772 Exploring | 550 |

173 Sequel | 575 |

Automation | 577 |

182 Visualization Markup Language | 587 |

183 Summary | 606 |

184 Sequel | 607 |

Reader | 609 |

192 A Psychological Reader Model | 612 |

193 A Graphics Grammar Reader Model | 615 |

194 Research | 620 |

Coda | 621 |

202 Monarch Butterfly Migration | 627 |

203 Conclusion | 630 |

204 Sequel | 632 |

References | 633 |

671 | |

679 | |

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aesthetic algebra algorithm allows analysis appear applications approach assume attributes axis birth called categorical Chapter chart Cleveland cluster color column components construct contains continuous COORD coordinates correlation create crossing dataset defined dimension discuss display distance distribution domain edges ELEMENT error estimates example expression Figure frame function graph graphics GUIDE interval involves label language linear look matrix means measure methods metric nested nodes objects operations panel path pattern perception plot points polar position possible present probability problem produce projection range Rate region relation represent requires result rotation scale scatterplot separate sequence shape shows similar simple single smooth space specification statistical structure Table tion transformation tree usually values variable vertical visual weight York