DevTut

# ggplot2

# Displaying multiple plots

Display multiple plots in one image with the different facet functions. An advantage of this method is that all axes share the same scale across charts, making it easy to compare them at a glance. We'll use the mpg dataset included in ggplot2.

Wrap charts line by line (attempts to create a square layout):

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  facet_wrap(~class)

(opens new window)

Display multiple charts on one row, multiple columns:

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  facet_grid(.~class)

(opens new window)

Display multiple charts on one column, multiple rows:

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  facet_grid(class~.)

(opens new window)

Display multiple charts in a grid by 2 variables:

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  facet_grid(trans~class) #"row" parameter, then "column" parameter

(opens new window)

# Prepare your data for plotting

ggplot2 works best with a long data frame. The following sample data which represents the prices for sweets on 20 different days, in a format described as wide, because each category has a column.

set.seed(47)
sweetsWide <- data.frame(date      = 1:20,
                         chocolate = runif(20, min = 2, max = 4),
                         iceCream  = runif(20, min = 0.5, max = 1),
                         candy     = runif(20, min = 1, max = 3))

head(sweetsWide)
##   date chocolate  iceCream    candy
## 1    1  3.953924 0.5890727 1.117311
## 2    2  2.747832 0.7783982 1.740851
## 3    3  3.523004 0.7578975 2.196754
## 4    4  3.644983 0.5667152 2.875028
## 5    5  3.147089 0.8446417 1.733543
## 6    6  3.382825 0.6900125 1.405674

To convert sweetsWide to long format for use with ggplot2, several useful functions from base R, and the packages reshape2, data.table and tidyr (in chronological order) can be used:

# reshape from base R
sweetsLong <- reshape(sweetsWide, idvar = 'date', direction = 'long', 
                      varying = list(2:4), new.row.names = NULL, times = names(sweetsWide)[-1])

# melt from 'reshape2'
library(reshape2)
sweetsLong <- melt(sweetsWide, id.vars = 'date')

# melt from 'data.table'
# which is an optimized & extended version of 'melt' from 'reshape2'
library(data.table)
sweetsLong <- melt(setDT(sweetsWide), id.vars = 'date')

# gather from 'tidyr'
library(tidyr)
sweetsLong <- gather(sweetsWide, sweet, price, chocolate:candy)

The all give a similar result:

head(sweetsLong)
##   date     sweet    price
## 1    1 chocolate 3.953924
## 2    2 chocolate 2.747832
## 3    3 chocolate 3.523004
## 4    4 chocolate 3.644983
## 5    5 chocolate 3.147089
## 6    6 chocolate 3.382825

See also Reshaping data between long and wide forms (opens new window) for details on converting data between long and wide format.

The resulting sweetsLong has one column of prices and one column describing the type of sweet. Now plotting is much simpler:

library(ggplot2)
ggplot(sweetsLong, aes(x = date, y = price, colour = sweet)) + geom_line()

line graph of sweets data (opens new window)

# Add horizontal and vertical lines to plot

# Add one common horizontal line for all categorical variables

# sample data
df <- data.frame(x=('A', 'B'), y = c(3, 4))

p1 <- ggplot(df, aes(x=x, y=y)) 
        + geom_bar(position = "dodge", stat = 'identity') 
        + theme_bw()

p1 + geom_hline(aes(yintercept=5), colour="#990000", linetype="dashed")

plot1 (opens new window)

# Add one horizontal line for each categorical variable

# sample data
df <- data.frame(x=('A', 'B'), y = c(3, 4))

# add horizontal levels for drawing lines
df$hval <- df$y + 2

p1 <- ggplot(df, aes(x=x, y=y)) 
        + geom_bar(position = "dodge", stat = 'identity') 
        + theme_bw()

p1 + geom_errorbar(aes(y=hval, ymax=hval, ymin=hval), colour="#990000", width=0.75)

plot2 (opens new window)

# Add horizontal line over grouped bars

# sample data
df <- data.frame(x = rep(c('A', 'B'), times=2), 
             group = rep(c('G1', 'G2'), each=2), 
             y = c(3, 4, 5, 6), 
             hval = c(5, 6, 7, 8))

p1 <- ggplot(df, aes(x=x, y=y, fill=group)) 
        + geom_bar(position="dodge", stat="identity")

p1 + geom_errorbar(aes(y=hval, ymax=hval, ymin=hval), 
               colour="#990000", 
               position = "dodge", 
               linetype = "dashed")

plot3 (opens new window)

# Add vertical line

# sample data
df <- data.frame(group=rep(c('A', 'B'), each=20), 
                 x = rnorm(40, 5, 2), 
                 y = rnorm(40, 10, 2))

p1 <-  ggplot(df, aes(x=x, y=y, colour=group)) + geom_point()

p1 + geom_vline(aes(xintercept=5), color="#990000", linetype="dashed")

enter image description here (opens new window)

# Scatter Plots

We plot a simple scatter plot using the builtin iris data set as follows:

library(ggplot2)
ggplot(iris, aes(x = Petal.Width, y = Petal.Length, color = Species)) + 
  geom_point()

This gives: Sample scatter plot using iris dataset (opens new window)

# Produce basic plots with qplot

qplot is intended to be similar to base r plot() function, trying to always plot out your data without requiring too much specifications.

basic qplot

qplot(x = disp, y = mpg, data = mtcars)

enter image description here (opens new window)

adding colors

qplot(x = disp, y = mpg, colour = cyl,data = mtcars)

enter image description here (opens new window)

adding a smoother

qplot(x = disp, y = mpg, geom = c("point", "smooth"), data = mtcars)

enter image description here (opens new window)

# Vertical and Horizontal Bar Chart

ggplot(data = diamonds, aes(x = cut, fill =color)) +
  geom_bar(stat = "count", position = "dodge")

enter image description here (opens new window)

it is possible to obtain an horizontal bar chart simply adding coord_flip() aesthetic to the ggplot object:


 ggplot(data = diamonds, aes(x = cut, fill =color)) +
  geom_bar(stat = "count", position = "dodge")+
  coord_flip()

enter image description here (opens new window)

# Violin plot

Violin plots are kernel density estimates mirrored in the vertical plane. They can be used to visualize several distributions side-by-side, with the mirroring helping to highlight any differences.

ggplot(diamonds, aes(cut, price)) +
  geom_violin()

basic violin plot (opens new window)

Violin plots are named for their resemblance to the musical instrument, this is particularly visible when they are coupled with an overlaid boxplot. This visualisation then describes the underlying distributions both in terms of Tukey's 5 number summary (as boxplots) and full continuous density estimates (violins).

ggplot(diamonds, aes(cut, price)) +
  geom_violin() +
  geom_boxplot(width = .1, fill = "black", outlier.shape = NA) +
  stat_summary(fun.y = "median", geom = "point", col = "white")

violin plot with boxplot (opens new window)

# Remarks

ggplot2 has its own perfect reference website http://ggplot2.tidyverse.org/ (opens new window).

Most of the time, it is more convenient to adapt the structure or content of the plotted data (e.g. a data.frame) than adjusting things within the plot afterwards.

RStudio publishes a very helpful "Data Visualization with ggplot2" cheatsheet that can be found here (opens new window).

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