A Backstage Tour of ggplot2 with Hadley Wickham

ggplot2:
A backstage tour

Hadley Wickham
Assistant Professor /
Dobelman Family Junior Chair
Department of Statistics
Rice University

February 2012
Wednesday, February 8, 12

1. Why ggplot2?
2. Sneak peek and new features
3. Best practices
4. Questions


Poll: What graphics
system are you
currently using?


Why ggplot2?


2004
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“Nothing is as practical as a good theory”
—Kurt Lewin

“[A good model] will bring together in a
coherent way things that previously
appeared unrelated and which also will
provide a basis for dealing systematically
with new situations”
—David Cox


A plot is made up of multiple layers.
A layer consists of data, a set of
mappings between variables and
aesthetics, a geometric object and a
statistical transformation
Scales control the details of the mapping.
All components are independent and
reusable.


Interesting ggplot example
Layered grammar + ggplot2

James Cheshire, http://bit.ly/xqHhAs

Charlotte Wickham, http://cwick.co.nz/

David B Sparks, http://bit.ly/hn54NW

Claudia Beleites, http://bit.ly/yNqlpz

Poll: What resources are
most helpful to you when
improving your R skills?


Learning ggplot2
ggplot2 mailing list
http://groups.google.com/group/ggplot2
stackoverﬂow
http://stackoverﬂow.com/tags/ggplot2
Lattice to ggplot2 conversion
http://learnr.wordpress.com/?s=lattice
Cookbook for common graphics
http://wiki.stdout.org/rcookbook/Graphs/
ggplot2 book
http://amzn.com/0387981403


Sneak
peek

Poll: Why do you use
visualisation?


# Getting started

# To get the CRAN version
install.packages("ggplot2")

# To get the development version
install.packages("devtools")
library(devtools)
dev_mode() # don't overwrite your existing install
install_github("ggplot2")


Development version

CRAN version


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qplot(x, y, data = df, colour = colour, alpha = I(1/4))

qplot(x, y, data = df, colour = colour, alpha = I(1/4)) +
guides(colour = guide_legend(
override.aes = list(alpha = 1, size = 2)))

# Better layout

df <- data.frame(x = 1:10, y = 10:1, colour = 1:2)
qplot(x, y, data = df) + coord_fixed()
qplot(x, y, data = df) + facet_wrap(~ colour)

# Internally, there has been a big rewrite of
# the facetting data processing and rendering
# systems. This lays the foundation for new
# features, and fixes some annoying long-standing
# bugs.


# Speed improvements

system.time(
print(qplot(carat, price, data = diamonds))
)

# Includes new tools for figuring out what's
# taking all the time
benchplot(qplot(carat, price, data = diamonds))

# See also geom_raster and geom_map

# Still a lot of work to do. The emphasis in
# ggplot2 is reducing the amount of thinking
# time by making it easier to go from the plot in
# your brain to the plot on the page.


30s with geom_tile, 8s with annotation_raster

library(ggplot2)
library(reshape2)
library(RgoogleMaps)
library(ggmap)

theft <- subset(crime, offense == "theft" & lat > 29 & lat < 30.2 &
lon > -95.8)

lonr <- range(theft$lon)
latr <- range(theft$lat)

h_map <- GetMap.bbox(lonr, latr, size = c(1024, 1024))
h_raster <- as.raster(h_map$myTile)

benchplot(ggplot(theft, aes(lon, lat)) +
annotation_raster(h_raster, lonr[1], lonr[2], latr[1], latr[2]) +
geom_density2d(colour = "black"))

h_data <- melt(as.matrix(h_raster))
h_data$lat <- seq(latr[2], latr[1], length = 640)[h_data$Var1]
h_data$lon <- seq(lonr[1], lonr[2], length = 640)[h_data$Var2]

benchplot(ggplot(theft, aes(lon, lat)) +
geom_tile(aes(fill = value), data = h_data) +
scale_fill_identity() +
geom_density2d(colour = "black"))


ggplot2 0.9 scheduled for
release on March 1


Poll: How big
is your data?


# Future work: big visualisation
# (Sponsored by Revolution Analytics)

# How can you make a plot of 100 million
# observations?

# In less that one minute.


~100,000 points
0.06 s to bin
0.20 s to convert
6.0 s to plot

~1.2 million
~100,000 points
10 s toto bin
0.06 s bin
0.20 s to convert
6.0 s to plot

Best
practices

Poll: How do you learn
about new packages?


Package best
practices
• Namespace
• Documentation
• Unit tests
• Read the source!

• (ggplot2 not always the best example: it was
was my second R package - I have now written
around 30. I now know a lot more!)


# Namespaces

library(ggplot2)
ddply

# Note that plyr, reshape etc aren't automatically
# loaded. This is good development practice -
# it's better to be explicit than implicit.

# Look at the NAMESPACE file.


export("%+%")
export(aes_all)
export(aes_auto)
export(aes_string)
export(aes)
export(annotate)
export(annotation_custom)
export(annotation_map)
export(annotation_raster)
export(autoplot)
export(benchplot)
export(borders)
export(continuous_scale)
export(coord_cartesian)
export(coord_equal)
export(coord_fixed)
export(coord_flip)
export(coord_map)
export(coord_polar)
...


# Unit tests

# Look in tests/ or inst/tests/

library(testthat)
test_package("ggplot2")


# Documentation

# Function level in man/
?geom_point
?facet_wrap
package?ggplot2

# Vignettes in inst/doc
# (ggplot2 doesn't have any)

# Publications
citation("ggplot2")


Questions


A Backstage Tour of ggplot2 with Hadley Wickham

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A Backstage Tour of ggplot2 with Hadley Wickham