how to speak ggplot2 like a native
DC R Meetup
Predictive Analytics World
October 19th, 2010
Harlan D. Harris, PhD
[email protected]
ggplot's philosophy
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Graphics are (should be!) created by combining
a specification with data. (Wilkinson, 2005)
The specification is not the name of the visual
form (bar graph, scatterplot, histogram).
The specification is a collection of rules that
together describe how to build a graph, a
Grammar of Graphics
October 19th, 2010
Harlan D. Harris, PhD
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graphics as grammar
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data
date ct sz z
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0
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me
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x=date
y=ct/sz
bars
group by z
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advantages
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Flexible
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Smart
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can define new graph types by changing
specifications
can combine many forms into single graphs
compact: rules have useful defaults
graphs always have meaning
Reusable
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can plug new data into old specification
can explore many types of plots from a set of data
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Harlan D. Harris, PhD
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ggplot2
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Hadley Wickham (Rice Univ.)
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Extends & implements
The Grammar of Graphics (Wilkinson, 1995, 2005)
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also: reshape2, plyr, etc.
Focus on layers; based on grid
Specification as R objects constructed by functions
Large library of components with good defaults
ggplot2: Elegant Graphics for Data Analysis
(Wickham, 2009)
October 19th, 2010
Harlan D. Harris, PhD
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my gripes
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Specification is hierarchical structure;
grammar is left-to-right R expression;
graph is spatial
Can't see the structure (usefully)
Abuses both notation and R semantics
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Deep Magic with lazy evaluation, proto objects
Existing tutorials lead to conceptual confusion,
requires relearning of fundamentals
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Start with the structure, not with the shortcuts
October 19th, 2010
Harlan D. Harris, PhD
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goal
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Harlan D. Harris, PhD
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data to plot
October 19th, 2010
Harlan D. Harris, PhD
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ggplot likes “long” data
October 19th, 2010
Harlan D. Harris, PhD
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will plot model vs. empirical
October 19th, 2010
Harlan D. Harris, PhD
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simplest plot
aes=”aesthetics”=”create mapping”
October 19th, 2010
Harlan D. Harris, PhD
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you don't need
to know this!
structure
ggplot(data=d.long.EI, mapping=aes(x=Parameter, y=Errors,
color=Condition)) +
layer(geom="line")
ggplot
data
(copy)
layers
Ø
mapping
scales
coords
facets
options
x=Param.
y=Errs
color=Cond.
layer[1]
data
mapping
geom
line
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stat
identity
geom_
params
stat_
params
structure(p), str(p)
October 19th, 2010
Harlan D. Harris, PhD
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add empirical data and chance
October 19th, 2010
Harlan D. Harris, PhD
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you don't need
to know this!
structure so far
ggplot
data
(copy)
layers
mapping
scales
x=Param.
y=Errs
color=Cond.
coords
facets
options
layer[1]
data
data
(U)
data
(K)
data
mapping
geom
layer[1]
line
mapping
mapping
yint=Errs
mapping
yint=[64]
October 19th, 2010
stat
identity
geom
layer[1]
stat
point
identity
hline
geom
hline
stat
geom
layer[1]
hline
Harlan D. Harris, PhD
stat
hline
geom_
params
geom_
params
size=3
geom_
params
size=2
geom_
stat_
params
stat_
params
stat_
params
stat_
color=”black”
params
params
linetype=2
size=.5
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scales
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Harlan D. Harris, PhD
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coordinates & scales
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coordinates affect display of axes
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scales affect data mapping
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cartesian, polar, map, etc.
colors, shapes, lines
source of confusion
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set axis ticks/breaks and labels with
scale_x_continuous() or scale_y_discrete(), but
restrict DATA range with scale_*(limits=c(1,10))
restrict AXIS (plotted) range with
coord_cartesian(xlim=c(1,10))
October 19th, 2010
Harlan D. Harris, PhD
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options
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Harlan D. Harris, PhD
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shortcuts
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All those layer() calls are tedious!
geom_*() creates a layer with a specific geom
(and various defaults, including a stat)
stat_*() creates a layer with a specific stat
(and various defaults, including a geom)
qplot() creates a ggplot and a layer
October 19th, 2010
Harlan D. Harris, PhD
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quick note on stats
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stat=”identity”
stat=”lm”
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stat=”smooth”
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fit y=f(x) with loess()
stat=”summary”
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fit y=f(x) with lm(), generate new data to be plotted
by geom_line(), CIs with geom_ribbon()
y=f(x) with arbitrary f()
stat=”bin”
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histograms
October 19th, 2010
Harlan D. Harris, PhD
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simplest faceted plot
October 19th, 2010
Harlan D. Harris, PhD
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everything else (+alpha)
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Harlan D. Harris, PhD
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other things I find useful
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scale_x_continuous(breaks=seq(1,9,2),
labels=c(“one”, “”, “five”, “”, “nine”))
geom_text(aes(x=.., y=.., label=..))
annotate(geom=”text”, x=14, y=19, “outlier!”)
geom_density()
stat_summary(fun.data=”mean_cl_boot”,
geom=”crossbar”)
geom_jitter(position=position_jitter(width=.5))
October 19th, 2010
Harlan D. Harris, PhD
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“fizzy bubbly” plot
rated.movies <- subset(movies,
mpaa!=“”)
rated.movies$mpaa <factor(rated.movies$mpaa)
p <- ggplot(rated.movies,
aes(mpaa, rating)) +
geom_jitter(alpha=.5) +
stat_summary(fun.data=
“mean_sdl”, geom=“crossbar”,
color=“red”, size=1)
ggsave(“movies.png”, p,
dpi=150)
October 19th, 2010
takehomes
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a ggplot graph is generated by a specification +
data
ggplot specifications are a core object plus
layers
mappings among data, x/y, scales, and other
attributes are fundamental
geom and stat shortcuts allow smart/compact
construction of graphs
ggplot encourages good graphs, with facets,
good use of color, minimal chartjunk
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Harlan D. Harris, PhD
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2010 case study competition winner
October 19th, 2010
resources
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Wickham, H. (2009) ggplot2: Elegant Graphics
for Data Analysis. Springer.
http://had.co.nz/ggplot2/
http://groups.google.com/group/ggplot2
http://stackoverflow.com/questions/tagged/r
http://github.com/hadley/ggplot2/wiki
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Harlan D. Harris, PhD
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thanks!
October 19th, 2010
Harlan D. Harris, PhD
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