Stat405
Polishing graphics for presentation
Hadley Wickham
Thursday, 21 October 2010

Mark Schoenhals
• Rice alum & former stat major
• Visiting on Monday. Two talks: 11am (Four
designs for ecommerce experiments),
4pm (Using data smarter faster: How one
small store sold $500 million of music
gear to over 1 million customers)
• Undergrads are invited to lunch with him.
Email me if you’re interested
Thursday, 21 October 2010

# Who is the most accurate shooter in the NBA?
library(plyr)
nba <- read.csv("nba-0809.csv.bz2")
shots <- subset(nba, etype == "shot")
success <- ddply(shots, c("team", "player"),
summarise,
total = length(player),
made = sum(result == "made"))
success$prop <- success$made / success$total
success <- arrange(success, desc(prop))
Thursday, 21 October 2010

team player total made prop
1 NYK Eddy Curry 1 1 1.0000000
2 OKC Steven Hill 1 1 1.0000000
3 SAS Marcus Williams 2 2 1.0000000
4 CHA Dwayne Jones 4 3 0.7500000
5 OKC Mouhamed Sene 7 5 0.7142857
6 SAS Pops Mensah-Bonsu 7 5 0.7142857
7 BOS J.R. Giddens 3 2 0.6666667
8 LAL Yue Sun 3 2 0.6666667
9 MIL Eddie Gill 9 6 0.6666667
10 DAL Erick Dampier 269 175 0.6505576
11 LAC DeAndre Jordan 132 85 0.6439394
12 ORL Adonal Foyle 11 7 0.6363636
13 BOS Bill Walker 58 36 0.6206897
14 POR Joel Przybilla 261 161 0.6168582
15 POR Shavlik Randolph 13 8 0.6153846
16 DET Amir Johnson 152 92 0.6052632
17 PHX Shaquille O'Neal 813 491 0.6039360
18 DEN Nene Hilario 708 427 0.6031073
19 ATL Solomon Jones 93 56 0.6021505
20 BOS Mikki Moore 85 51 0.6000000
Thursday, 21 October 2010

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Thursday, 21 October 2010

1. ggplot() practice
2. Communication graphics
3. Polishing a plot: scales and themes
Thursday, 21 October 2010

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−120 −110 −100 −90 −80 −70
Thursday, 21 October 2010

Your turn
Identify the data and layers in the flight
delays data, then write the ggplot2 code
to create it.
library(ggplot2)
library(maps)
usa <- map_data("state")
feb13 <- read.csv("delays-feb-13-2007.csv")
Thursday, 21 October 2010

ggplot(feb13, aes(long, lat)) +
geom_point(aes(size = 1), colour = "white") +
geom_polygon(aes(group = group), data = usa,
colour = "grey70", fill = NA) +
geom_point(aes(size = ncancelw / ntot),
colour = alpha("black", 1/2))
# Polishing: up next
last_plot() +
scale_area("% cancelled", to = c(1, 8),
breaks = seq(0, 1, by = 0.2), limits = c(0, 1))
scale_x_continuous("", limits = c(-125, -67)),
scale_y_continuous("", limits = c(24, 50))
Thursday, 21 October 2010

Communication graphics
When you need to communicate your
findings, you need to spend a lot of time
polishing your graphics to eliminate
distractions and focus on the story.
Now it’s time to pay attention to the small
stuff: labels, colour choices, tick marks...
Thursday, 21 October 2010

Context
Thursday, 21 October 2010

Consumption
Thursday, 21 October 2010

36
What’s
wrong
with this
34
plot?
bin
32 < 1000
< 1e4
lat
< 1e5
< 1e6
30
< 1e7
28
26
−106 −104 −102 −100 −98 −96 −94
long
Thursday, 21 October 2010

Some problems
Incorrect coordinate system
Bad colour scheme
Unnecessary axis labels
Legend needs improvement: better title
and better key labels
No title
Thursday, 21 October 2010

Thursday, 21 October 2010

1. Scales: used to override default
perceptual mappings, and tune
parameters of axes and legends.
2. Themes: control presentation of
non-data elements.
3. Saving your work: to include in
reports, presentations, etc.
Thursday, 21 October 2010

Scales
Thursday, 21 October 2010

Scales
Control how data is mapped to perceptual
properties, and produce guides (axes and
legends) which allow us to read the plot.
Important parameters: name, breaks &
labels, limits.
Naming scheme: scale_aesthetic_name.
All default scales have name continuous or
discrete.
Thursday, 21 October 2010

# Default scales
scale_x_continuous()
scale_y_discrete()
scale_colour_discrete()
# Custom scales
scale_colour_hue()
scale_x_log10()
scale_fill_brewer()
# Scales with parameters
scale_x_continuous("X Label", limits = c(1, 10))
scale_colour_gradient(low = "blue", high = "red")
Thursday, 21 October 2010

# First argument (name) controls axis label
scale_y_continuous("Latitude")
scale_x_continuous("")
# Breaks and labels control tick marks
scale_x_continuous(breaks = -c(106,100,94))
scale_fill_discrete(labels = c("< 1000" = "< 1000",
"< 1e4" = "< 10,000", "< 1e5" = "< 100,000",
"< 1e6" = "< 1,000,000", "< 1e7" = "1,000,000+"))
scale_y_continuous(breaks = NA)
# Limits control range of data
scale_y_continuous(limits = c(26, 32))
# same as:
p + ylim(26, 32)
Thursday, 21 October 2010

options(stringsAsFactors = FALSE)
pop <- read.csv("tx-pop.csv")
pop$bin <- cut(log10(pop$pop), breaks = 2:7,
labels = c("< 1000", "< 1e4", "< 1e5",
"< 1e6", "< 1e7"))
borders <- read.csv("tx-borders.csv")
choro <- join(borders, pop)
qplot(long, lat, data = choro, geom =
"polygon", group = group, fill = bin)
Thursday, 21 October 2010

Your turn
Fix the axis and legend related problems
that we have identified.
Thursday, 21 October 2010

qplot(long, lat, data = choro, geom = "polygon", group = group, fill = bin) +
scale_fill_discrete("Population", labels =
c("< 1000" = "< 1000" , "< 1e4" = "< 10,000", "< 1e5" = "< 100,000",
"< 1e6" = "< 1,000,000", "< 1e7" = "1,000,000+")) +
scale_x_continuous("") +
scale_y_continuous("") +
coord_map()
Thursday, 21 October 2010

Alternate scales
Can also override the default choice of
scales. You are most likely to want to
do this with colour, as it is the most
important aesthetic after position.
Need a little background to be able to
use colour effectively: colour spaces &
colour blindness.
Thursday, 21 October 2010

Colour spaces
Most familiar is rgb: defines colour as
mixture of red, green and blue. Matches
the physics of eye, but the brain does a
lot of post-processing, so it’s hard to
directly perceive these components.
A more useful colour space is hcl:
hue, chroma and luminance
Thursday, 21 October 2010

hue
luminance
chroma
Thursday, 21 October 2010

Default colour scales
Discrete: evenly spaced hues of equal
chroma and luminance. No colour
appears more important than any other.
Does not imply order.
Continuous: evenly spaced hues
between two colours.
Thursday, 21 October 2010

Colour blindness
7-10% of men are red-green colour
“blind”. (Many other rarer types of colour
blindness)
Solutions: avoid red-green contrasts; use
redundant mappings; test. I like color
oracle: http://colororacle.cartography.ch
Thursday, 21 October 2010

Alternatives
Discrete: brewer, grey
Continuous: gradient2, gradientn
Thursday, 21 October 2010

Your turn
Modify the fill scale to use a Brewer
colour palette of your choice. (Hint: you
will need to change the name of the scale)
Use RColorBrewer::display.brewer.all
to list all palettes.
Thursday, 21 October 2010

Themes
Thursday, 21 October 2010

Visual appearance
So far have only discussed how to get the
data displayed the way you want,
focussing on the essence of the plot.
Themes give you a huge amount of
control over the appearance of the plot,
the choice of background colours, fonts
and so on.
Thursday, 21 October 2010

# Two built in themes. The default:
qplot(carat, price, data = diamonds)
# And a theme with a white background:
qplot(carat, price, data = diamonds) + theme_bw()
# Use theme_set if you want it to apply to every
# future plot.
theme_set(theme_bw())
# This is the best way of seeing all the default
# options
theme_bw()
theme_grey()
Thursday, 21 October 2010

Plot title
The plot theme also controls the plot title.
You can change this for an individual plot
by adding
opts(title = "My title")
Thursday, 21 October 2010

Your turn
Add an informative title and see what the
plot looks like with a white background.
Thursday, 21 October 2010

Elements
You can also make your own theme, or
modify and existing.
Themes are made up of elements which
can be one of: theme_line, theme_segment,
theme_text, theme_rect, theme_blank
Gives you a lot of control over plot
appearance.
Thursday, 21 October 2010

Elements
Axis: axis.line, axis.text.x, axis.text.y,
axis.ticks, axis.title.x, axis.title.y
Legend: legend.background, legend.key,
legend.text, legend.title
Panel: panel.background, panel.border,
panel.grid.major, panel.grid.minor
Strip: strip.background, strip.text.x,
strip.text.y
Thursday, 21 October 2010

# To modify a plot
p + opts(plot.title =
theme_text(size = 12, face = "bold"))
p + opts(plot.title = theme_text(colour = "red"))
p + opts(plot.title = theme_text(angle = 45))
p + opts(plot.title = theme_text(hjust = 1))
Thursday, 21 October 2010

# If we want, we could also remove the axes:
last_plot() + opts(
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
axis.ticks.length = unit(0, "cm"),
axis.ticks.margin = unit(0, "cm"))
Thursday, 21 October 2010

Thursday, 21 October 2010