readme.md

ggplot-cheatsheet

cheatsheet for ggplot2, compiled mostly from the book, ggplot2: Elegant Graphics for Data Analysis

Documentation for ggplot is available here.

Table of Contents

• Loading ggplot
• Basic use with qplot
  • Color, size, shape and other aesthetic attributes
  • Plot geoms
    • Adding a smoother
    • Boxplots and jittered points
    • Histogram and density plots
    • Bar charts
    • Time series
  • Faceting
  • Other options
• Build a plot layer by layer
  • Basic plot types
    • geom_point
    • geom_bar
    • geom_line
    • geom_area
    • geom_path
    • geom_text
    • geom_tile
    • geom_polygon
  • Displaying distributions
    • geom_histogram and geom_freqpoly
    • geom_boxplot
    • geom_jitter
    • geom_density
  • Deal with overplotting
  • Surface plots
  • Drawing maps
    • [Choropleth map](#choropleth-maphttpenwikipediaorgwikichoropleth_map)
  • Annotating a plot
• Faceting
  • Facet grid
    • Margins
  • Facet wrap

opts_chunk$set(warning=FALSE, message=FALSE, fig.width=8, fig.height=4)

Loading ggplot

library(ggplot2)

Basic use with qplot

Load the sample data

set.seed(1410)  # make the sample reproducible
head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

dsmall <- diamonds[sample(nrow(diamonds), 100), ]

qplot(carat, price, data=diamonds)

Color, size, shape and other aesthetic attributes

qplot(carat, price, data=dsmall, color=color, shape=cut, alpha=I(1/2))

Plot geoms

qplot(carat, price, data=dsmall, geom=c("point", "smooth"))

Adding a smoother

There are many different smoother that can be used with method argument.

qplot(carat, price, data=dsmall, geom=c("point", "smooth"), method="lm")

Boxplots and jittered points

qplot(color, price/carat, data=diamonds, geom="jitter")

qplot(color, price/carat, data=diamonds, geom="boxplot")

Histogram and density plots

qplot(carat, data=diamonds, geom="histogram", fill=color)

qplot(carat, data=diamonds, geom="density", color=color)

Change the amount of smoothing with binwidth argument.

qplot(carat, data=diamonds, geom="histogram", binwidth=1)

qplot(carat, data=diamonds, geom="histogram", binwidth=0.1)

qplot(carat, data=diamonds, geom="histogram", binwidth=0.01)

Bar charts

qplot(color, data=diamonds, geom="bar")

# bar plot of diamond color weighted by carat
qplot(color, data=diamonds, geom="bar", weight=carat) +
    scale_y_continuous("carat")

Time series

head(economics)

##         date   pce    pop psavert uempmed unemploy
## 1 1967-06-30 507.8 198712     9.8     4.5     2944
## 2 1967-07-31 510.9 198911     9.8     4.7     2945
## 3 1967-08-31 516.7 199113     9.0     4.6     2958
## 4 1967-09-30 513.3 199311     9.8     4.9     3143
## 5 1967-10-31 518.5 199498     9.7     4.7     3066
## 6 1967-11-30 526.2 199657     9.4     4.8     3018

qplot(date, unemploy/pop, data=economics, geom="line")

Faceting

qplot(carat, data=diamonds, facets=color~.,
      geom="histogram", binwidth=0.1, xlim=c(0,3))

Other options

• xlim and ylim: set limits for x- and y-axis (e.g. xlim=c(0,20))
• main: main title for the plot
• xlab and ylab: labels for x- and y-axis

qplot(carat, price, data=dsmall,
      xlab="Price ($)",
      ylab="Weight (carats)",
      main="Price-weight relationship")

Build a plot layer by layer

More complicated, multi-layer plots can be generated using ggplot().

Basic plot types

df <- data.frame(x=c(3, 1, 5), y=c(2, 4, 6), label=c("a", "b", "c"))
p <- ggplot(df, aes(x, y, label=label)) + xlab(NULL) + ylab(NULL)

geom_point

p + geom_point() + ggtitle("geom_point")

geom_bar

p + geom_bar(stat="identity") + ggtitle("geom_bar(stat=\"identity\")")

geom_line

p + geom_line() + ggtitle("geom_line")

geom_area

p + geom_area() + ggtitle("geom_area")

geom_path

p + geom_path() + ggtitle("geom_path")

geom_text

p + geom_text() + ggtitle("geom_text")

geom_tile

p + geom_tile() + ggtitle("geom_tile")

geom_polygon

p + geom_polygon() + ggtitle("geom_polygon")

Displaying distributions

For 1d data, the geom is the histogram.

geom_histogram and geom_freqpoly

depth_dist <- ggplot(diamonds, aes(depth)) + xlim(58, 68)
depth_dist + geom_histogram()

To compare the distribution between groups, couple of options

depth_dist + geom_histogram(aes(y = ..density..), binwidth=0.1) +
    facet_grid(cut ~ .)

depth_dist + geom_histogram(aes(fill=cut), binwidth=0.1, position="fill")

depth_dist + geom_freqpoly(aes(y = ..density.., color=cut), binwidth=0.1)

geom_boxplot

qplot(cut, depth, data=diamonds, geom="boxplot")

library(plyr)
qplot(carat, depth, data=diamonds, geom="boxplot",
      group = round_any(carat, 0.1, floor), xlim=c(0, 3))

geom_jitter

qplot(class, cty, data=mpg, geom="jitter")

qplot(class, drv, data=mpg, geom="jitter")

geom_density

qplot(depth, data=diamonds, geom="density", xlim=c(54, 70))

qplot(depth, data=diamonds, geom="density", xlim=c(54, 70), fill=cut, alpha=I(0.2))

Deal with overplotting

• Make the points smaller

df <- data.frame(x=rnorm(2000), y=rnorm(2000))
norm <- ggplot(df, aes(x, y))
norm + geom_point()

norm + geom_point(shape=1)

norm + geom_point(shape = ".")          # pixel-sized

• Use alpha blending

library(scales)
norm + geom_point(color=alpha("black", 1/3))

norm + geom_point(color=alpha("black", 1/5))

norm + geom_point(color=alpha("black", 1/10))

• Randomly jitter if there is some discreteness

td <- ggplot(diamonds, aes(table, depth)) + xlim(50, 70) + ylim(50, 70)
td + geom_point()

td + geom_jitter()

jit <- position_jitter(width=0.5)
td + geom_jitter(position=jit)

td + geom_jitter(position=jit, color=alpha("black", 1/10))

td + geom_jitter(position=jit, color=alpha("black", 1/50))

td + geom_jitter(position=jit, color=alpha("black", 1/200))

Surface plots

Drawing maps

library(maps)
data(us.cities)
big_cities <- subset(us.cities, pop>500000)
qplot(long, lat, data=big_cities) + borders("state", size=0.5)

Choropleth map

states <- map_data("state")
arrests <- USArrests
names(arrests) <- tolower(names(arrests))
arrests$region <- tolower(rownames(USArrests))

choro <- merge(states, arrests, by="region")
# reorder the rows because order matters when drawing polygons and merge
# destroys the original ordering
choro <- choro[order(choro$order), ]
qplot(long, lat, data=choro, group=group, fill=assault, geom="polygon")

qplot(long, lat, data=choro, group=group, fill=assault/murder, geom="polygon")

Annotating a plot

Just extra data

• adding one at a time
• many at once

unemp <- qplot(date, unemploy, data=economics, geom="line",
               xlab="", ylab="No. unemployed (1000s)")

presidential <- presidential[-(1:3), ]

yrng <- range(economics$unemploy) 
xrng <- range(economics$date)
unemp + geom_vline(aes(xintercept=as.numeric(start)), data=presidential)

unemp + geom_rect(aes(NULL, NULL, xmin=start, xmax=end, fill=party),
                  ymin=yrng[1], ymax=yrng[2], data=presidential) +
    scale_fill_manual(values=alpha(c("blue", "red"), 0.2))

last_plot() + geom_text(aes(x=start, y=yrng[1], label=name),
                        data=presidential, size=3, hjust=0, vjust=0)

caption <- paste(strwrap("Unemployment rates in the US have varied
alot over the years", 40), collapse="\n")
unemp + geom_text(aes(x, y, label=caption),
                  data=data.frame(x=xrng[2], y=yrng[2]),
                  hjust=1, vjust=1, size=4)

highest <- subset(economics, unemploy==max(unemploy))
unemp + geom_point(data=highest, size=3, color=alpha("red", 0.3))

Faceting

Facet grid

qplot(cty, hwy, data=mpg) + facet_grid(. ~ cyl)

qplot(cty, data=mpg, geom="histogram", binwidth=2) + facet_grid(cyl ~ .)

qplot(cty, hwy, data=mpg) + facet_grid(drv ~ cyl)

Margins

p <- qplot(displ, hwy, data=mpg) + geom_smooth(method="lm", se=F)
p + facet_grid(cyl ~ drv)

p + facet_grid(cyl ~ drv, margins=T)

Facet wrap

library(plyr)
movies$decade <- round_any(movies$year, 10, floor)
qplot(rating, ..density.., data=subset(movies, decade > 1890),
      geom="histogram", binwidth=0.5) +
    facet_wrap(~ decade, ncol=6)