The document discusses the past, present, and future of the grammar of graphics. Specifically, it mentions how the grammar of graphics was developed by Leland Wilkinson in 1999 to provide an abstraction for creating statistical graphics. It then discusses how the ggplot2 package for R was inspired by the grammar of graphics and allows for high-level graphic creation. Finally, it outlines potential future directions for using the grammar to facilitate new types of displays and contribute new graphic components.

1. A grammar of graphics:
past, present, and future
Hadley Wickham
Rice University
http://had.co.nz/
Friday, 29 May 2009

2. Past
Friday, 29 May 2009

3. “If any number of
magnitudes are each
the same multiple of
the same number of
other magnitudes,
then the sum is that
multiple of the sum.”
Euclid, ~300 BC
Friday, 29 May 2009

4. “If any number of
magnitudes are each
the same multiple of
the same number of
other magnitudes,
then the sum is that
multiple of the sum.”
Euclid, ~300 BC
m(Σx) = Σ(mx)
Friday, 29 May 2009

5. The grammar of graphics
• An abstraction which makes thinking,
reasoning and communicating graphics
easier
• Developed by Leland Wilkinson, particularly
in “The Grammar of Graphics” 1999/2005
• One of the cornerstones of my research
(I’ll talk about the others later)
Friday, 29 May 2009

6. Present
Friday, 29 May 2009

7. ggplot2
• High-level R package for creating statistical graphics.
A rich set of components + user friendly wrappers
• Inspired by “The Grammar of Graphics”
Leland Wilkinson 1999
• John Chambers award in 2006
• Philosophy of ggplot
• Examples from a recent paper
• New methods facilitated by ggplot
Friday, 29 May 2009

8. Data Layer
Data
Trans Mapping
Geom
Element Stat
Position
Scale
Scale
Guide
Coord
+ defaults
Coord Facet – the grammar
Friday, 29 May 2009

9. Philosophy
• Make graphics easier
• Use the grammar to facilitate research into
new types of display
• Continuum of expertise:
• start simple by using the results of the theory
• grow in power by understanding the theory
• begin to contribute new components
• Orthogonal components and minimal special
cases should make learning easy(er?)
Friday, 29 May 2009

10. Examples
• J. Hobbs, H. Wickham, H. Hofmann, and D. Cook.
Glaciers melt as mountains warm: A graphical
case study. Computational Statistics. Special issue for
ASA Statistical Computing and Graphics Data Expo 2006.
• Exploratory graphics created with GGobi,
Mondrian, Manet, Gauguin and R, but needed
consistent high-quality graphics that worked in
black and white for publication
• So... used ggplot2 to recreate the graphics
Friday, 29 May 2009

11. qplot(long, lat, data = expo, geom = quot;tilequot;, fill = ozone,
facets = year ~ month) +
scale_fill_gradient(low = quot;whitequot;, high = quot;blackquot;) + map
Friday, 29 May 2009

12. 30
20
10
0
−10
−20
ggplot(df, aes(x = long + res * x, y = lat + res * y)) + map +
geom_polygon(aes(group = interaction(long, lat)), fill=NA, colour=quot;blackquot;)
−110 −85 −60
Friday, 29 May 2009

13. 30
20
10
0
−10
−20
ggplot(df, aes(x = long + res * x, y = lat + res * y)) + map +
geom_polygon(aes(group = interaction(long, lat)), fill=NA, colour=quot;blackquot;)
−110 −85 −60
Friday, 29 May 2009

14. 1.0
0.5 ●
0.0 ●
−0.5
−1.0
−1.0 −0.5 0.0 0.5 1.0
Friday, 29 May 2009

15. 1.0
1.0
0.8
0.5 ●
0.6
0.0 ●
●
0.4
−0.5
0.2
−1.0
0.0
−1.0 −0.5 0.0 0.5 1.0
0.0 0.2 0.4 0.6 0.8 1.0
Friday, 29 May 2009

16. with
eated r
lly cr n tou
Initia latio
30 corre
20
10
0
−10
−20
ggplot(rexpo, aes(x = long + res * rtime, y = lat + res * rpressure))
+ map + geom_line(aes(group = id))
−110 −85 −60
Friday, 29 May 2009

17. library(maps)
outlines <- as.data.frame(map(quot;worldquot;,xlim=-c(113.8, 56.2),ylim=c(-21.2, 36.2)))
map <- c(
geom_path(aes(x = x, y = y), data = outlines, colour = alpha(quot;grey20quot;, 0.2)),
scale_x_continuous(quot;quot;, limits = c(-113.8, -56.2), breaks = c(-110, -85, -60)),
scale_y_continuous(quot;quot;, limits = c(-21.2, 36.2))
)
Friday, 29 May 2009

18. 310
300
temperature
290
280
270
1995 1996 1997 1998 1999 2000
date
qplot(date, temperature, data=clustered, group=id, geom=quot;linequot;)
+ pacific + elnino(clustered$temperature)
Friday, 29 May 2009

19. pacific <- brush(cluster %in% c(5,6))
brush <- function(condition, background = quot;grey60quot;, brush = quot;redquot;) {
cond_string <- deparse(substitute(condition), width=500)
list(
aes_string(colour = cond_string, order = cond_string, size = cond_string),
scale_colour_manual(values = c(quot;FALSEquot; = background, quot;TRUEquot; = brush)),
opts(legend.position = quot;nonequot;)
)
}
Friday, 29 May 2009

20. Friday, 29 May 2009

21. ggplot(clustered, aes(x = long, y = lat))
+ geom_tile(aes(width = 2.5, height = 2.5,
fill = factor(cluster)))
+ facet_grid(cluster ~ .)
+ map
+ scale_fill_brewer(palette=quot;Spectralquot;)
qplot(date, value, data = clusterm, group = id,
geom = quot;linequot;, facets = cluster ~ variable,
colour = factor(cluster))
+ scale_y_continuous(quot;quot;, breaks=NA)
+ scale_colour_brewer(palette=quot;Spectralquot;)
Friday, 29 May 2009

22. New methods
• Supplemental statistical summaries
• Iterating between graphics and models
• Tables of plots
• Inspired by ideas of Tukey (and others)
• Exploratory graphics, not (as) pretty
Friday, 29 May 2009

23. Intro to data
• Response of trees to gypsy moth attack
• 5 varieties of tree: Dan-2, Sau-2, Sau-3,
Wau-1, Wau-2
• 2 treatments: NGM / GM
• 2 nutrient levels: low / high
• 5 reps
• Measured: weight, N, tannin, salicylates
Friday, 29 May 2009

24. qplot(genotype, weight, data=b) ●
70 ●
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Dan−2 Sau−2 Sau−3 Wau−1 Wau−2
genotype
Friday, 29 May 2009

25. qplot(genotype, weight, data=b, ●
70 colour=nutr) ●
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nutr
weight
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Dan−2 Sau−2 Sau−3 Wau−1 Wau−2
genotype
Friday, 29 May 2009

26. ●
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Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

27. Comparing means
• For inference, interested in comparing the
means of the groups
• But hard to do visually - eyes naturally
compare ranges
• What can we do? - Visual ANOVA
Friday, 29 May 2009

28. Supplemental summaries
Fro
m
• smry <- stat_summary( Hm
isc
fun=quot;mean_cl_bootquot;, conf.int=0.68,
geom=quot;crossbarquot;, width=0.3
)
• Adds another layer with summary statistics:
mean + bootstrap estimate of standard error
• Motivation: still exploratory, so minimise
distributional assumptions, will model explicitly
later
Friday, 29 May 2009

29. qplot(genotype, weight, data=b, ●
70 colour=nutr) ●
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nutr
weight
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Low
40 ●
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High
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Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

30. qplot(genotype, weight, data=b, ●
70 colour=nutr) + smry ●
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nutr
weight
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High
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Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

31. Iterating graphics
and modelling
• Clearly strong genotype effect. Is there a
nutr effect? Is there a nutr-genotype
interaction?
• Hard to see from this plot - what if we
remove the genotype main effect? What if
we remove the nutr main effect?
• How does this compare an ANOVA?
Friday, 29 May 2009

32. qplot(genotype, weight, data=b, ●
70 colour=nutr) + smry ●
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nutr
weight
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Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

33. ●
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nutr
weight2
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b$weight2 <- resid(lm(weight ~ genotype, data=b))
qplot(genotype, weight2, data=b, colour=nutr) + smry
●
Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

34. ●
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nutr
weight3
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−20 b$weight3 <- resid(lm(weight ~ genotype + nutr, data=b))
qplot(genotype, weight3, data=b, colour=nutr) + smry
●
Sau−3 Dan−2 Sau−2 Wau−2 Wau−1
genotype
Friday, 29 May 2009

35. Df Sum Sq Mean Sq F value Pr(>F)
genotype 4 13331 3333 36.22 8.4e-13 ***
nutr 1 1053 1053 11.44 0.0016 **
genotype:nutr 4 144 36 0.39 0.8141
Residuals 40 3681 92
anova(lm(weight ~ genotype * nutr, data=b))
Friday, 29 May 2009

36. Graphics ➙ Model
• In the previous example, we used graphics
to iteratively build up a model - a la
stepwise regression!
• But: here interested in gestalt, not accurate
prediction, and must remember that this is
just one possible model
• What about model ➙ graphics?
Friday, 29 May 2009

37. Model ➙ Graphics
• If we model first, we need graphical tools to
summarise model results, e.g. post-hoc
comparison of levels
• We can do better than SAS! But it’s hard
work: effects, multComp and multCompView
• Rich research area
Friday, 29 May 2009

38. ●
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0 a a b bc c
Sau3 Dan2 Sau2 Wau2 Wau1
genotype
Friday, 29 May 2009

39. ●
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ggplot(b, aes(x=genotype, y=weight))
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+ geom_hline(intercept = mean(b$weight))
20
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+ geom_crossbar(aes(y=fit, min=lower,max=upper),
●
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data=geffect) ●
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+ 0geom_point(aes(colour = nutr))
a a b bc c
+ geom_text(aes(label Sau2group), data=geffect)
Sau3 Dan2 = Wau2 Wau1
genotype
Friday, 29 May 2009

40. Tables of plots
• Often interested in marginal, as well as
conditional, relationships
• Or comparing one subset to the whole,
rather than to other subsets
• Like in contingency table, we often want to
see margins as well
Friday, 29 May 2009

41. Dan−2 Sau−2 Sau−3 Wau−1 Wau−2
●
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High
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2.0 trt
NGM
n
4.5 GM
4.0
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Low
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GM NGM GM NGM GM NGM GM NGM GM NGM
trt
Friday, 29 May 2009

42. Dan−2 Sau−2 Sau−3 Wau−1 Wau−2 (all)
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n
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GM
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(all)
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GM NGM GM NGM GM NGM GM NGM GM NGM GM NGM
trt
Friday, 29 May 2009

43. Arranging plots
• Facilitate comparisons of interest
• Small differences need to be closer
together (big differences can be far apart)
• Connections to model?
Friday, 29 May 2009

44. Summary
• Need to move beyond canned statistical
graphics to experimenting with new
graphical methods
• Strong links between graphics and models,
how can we best use them?
• Static graphics often aren’t enough
Friday, 29 May 2009

45. Future
Friday, 29 May 2009

46. GGobi ggplot2 reshape
plyr
rggobi data expo 09
data sets
classifly meifly
clusterfly
Friday, 29 May 2009

47. GGobi ggplot2 reshape
plyr
rggobi data expo 09
data sets
classifly meifly
clusterfly
Foundations of
New methods Data analysis
statistical graphics
Friday, 29 May 2009

48. GGobi ggplot2 reshape2
??
plyr
A grammar of
?? rggobi data expo 09
interactive
graphics data sets
classifly meifly
clusterfly Product/area graphics
Tourr
Foundations of
New methods Data analysis
statistical graphics
Friday, 29 May 2009

49. Questions?
Friday, 29 May 2009