Introduction

ggplot2 is a powerful R package to produce elegant graphics. It’s implemented by Hadley Wickham. The gg in ggplot2 means Grammar of Graphics, a graphic concept which describes plots by using a “grammar”.

Two main functions are available in ggplot2 package : a qplot() and ggplot() functions.

• qplot() is a quick plot function which is easy to use for simple plots.
• The ggplot() function use the powerful grammar of graphics to build plot piece by piece.

According to ggplot2 concept, a plot can be divide in different fundamental parts : Plot <- data + Aesthetics + Geometry.

• data is a data frame
  
• Aesthetics is used to indicate x and y variables. It can also be used to control the color, the size or the shape of a point, the height of a bar, etc…..
  
• Geometry corresponds to the type of graphics (histogram, box plot, line plot, density plot, dot plot, ….)

This document describes how to create and customize different types of graphs using ggplot2. Many examples of code and graphics are provided.

Some examples of graphs, described in this document, are shown below:

Install and load ggplot2 package

# Installation
install.packages('ggplot2')
# Loading
library(ggplot2)

Data format

The data must be a data.frame (columns are variables and rows are observations).

The data set mtcars is used in the examples below:

data(mtcars)
df <- mtcars[, c("mpg", "cyl", "wt")]
head(df)

##                    mpg cyl    wt
## Mazda RX4         21.0   6 2.620
## Mazda RX4 Wag     21.0   6 2.875
## Datsun 710        22.8   4 2.320
## Hornet 4 Drive    21.4   6 3.215
## Hornet Sportabout 18.7   8 3.440
## Valiant           18.1   6 3.460

Quick plot : qplot()

The function qplot() is very similar to the basic plot() function from the R base package. It can be used to create and combine easily different types of plots. However, it remains less flexible than the function ggplot().

This chapter provides a brief introduction to qplot(). Concerning the function ggplot(), many articles are available at the end of this web page for creating and customizing different plots using ggplot().

qplot(x, y=NULL, data, geom="auto", 
      xlim = c(NA, NA), ylim =c(NA, NA))

# Use data from numeric vectors
x <- 1:10; y = x*x
# Basic plot
qplot(x,y)

# Add line
qplot(x, y, geom=c("point", "line"))

# Use data from a data frame
qplot(mpg, wt, data=mtcars)

# Smoothing
qplot(mpg, wt, data = mtcars, geom = c("point", "smooth"))

# Regression line
qplot(mpg, wt, data = mtcars, geom = c("point", "smooth"),
      method="lm")

# Linear fits by group
qplot(mpg, wt, data = mtcars, color = factor(cyl),
      geom=c("point", "smooth"),
      method="lm")

# Change the color by a continuous numeric variable
qplot(mpg, wt, data = mtcars, colour = cyl)

# Change the color by groups (factor)
df <- mtcars
df[,'cyl'] <- as.factor(df[,'cyl'])
qplot(mpg, wt, data = df, colour = cyl)

# Add lines
qplot(mpg, wt, data = df, colour = cyl,
      geom=c("point", "line"))

qplot(mpg, wt, data=df, colour= factor(cyl))

# Change the size of points according to 
  # the values of a continuous variable
qplot(mpg, wt, data = mtcars, size = mpg)

# Change point shapes by groups
qplot(mpg, wt, data = mtcars, shape = factor(cyl))

qplot(mpg, wt, data = mtcars, label = rownames(mtcars), 
      geom=c("point", "text"),
      hjust=0, vjust=0)

# y represents the count of cases
qplot(mpg, data = mtcars, geom = "bar")

# y represents values in the data
index <- 1:nrow(mtcars)
qplot(index, mpg, data = mtcars, 
      geom = "bar", stat = "identity")

# Order the data by cyl and then by mpg values
df <- mtcars[order(mtcars[, "cyl"], mtcars[, "mpg"]),]
df[,'cyl'] <- as.factor(df[,'cyl'])
index <- 1:nrow(df)

# Change fill color by group (cyl)
qplot(index, mpg, data = df, 
      geom = "bar", stat = "identity", fill = cyl)

head(PlantGrowth)

##   weight group
## 1   4.17  ctrl
## 2   5.58  ctrl
## 3   5.18  ctrl
## 4   6.11  ctrl
## 5   4.50  ctrl
## 6   4.61  ctrl

# Basic box plot from a numeric vector
x <- "1"
y <- rnorm(100)
qplot(x, y, geom="boxplot")

# Basic box plot from data frame
qplot(group, weight, data = PlantGrowth, 
      geom=c("boxplot"))

# Dot plot
qplot(group, weight, data = PlantGrowth, 
      geom=c("dotplot"), 
      stackdir = "center", binaxis = "y")

# Violin plot
qplot(group, weight, data = PlantGrowth, 
      geom=c("violin"), trim = FALSE)

# Box plot from a data frame
# Add jitter and change fill color by group
qplot(group, weight, data = PlantGrowth, 
      geom=c("boxplot", "jitter"), fill = group)

# Dot plot
qplot(group, weight, data = PlantGrowth, 
      geom = "dotplot", stackdir = "center", binaxis = "y",
      color = group, fill = group)

set.seed(1234)
mydata = data.frame(
        sex = factor(rep(c("F", "M"), each=200)),
        weight = c(rnorm(200, 55), rnorm(200, 58)))
head(mydata)

##   sex   weight
## 1   F 53.79293
## 2   F 55.27743
## 3   F 56.08444
## 4   F 52.65430
## 5   F 55.42912
## 6   F 55.50606

# Basic histogram
qplot(weight, data = mydata, geom = "histogram")

# Change histogram fill color by group (sex)
qplot(weight, data = mydata, geom = "histogram",
    fill = sex, position = "dodge")

# Basic density plot
qplot(weight, data = mydata, geom = "density")

# Change density plot line color by group (sex)
# change line type
qplot(weight, data = mydata, geom = "density",
    color = sex, linetype = sex)

qplot(weight, data = mydata, geom = "density",
      xlab = "Weight (kg)", ylab = "Density", 
      main = "Density plot of Weight")

Introduction to ggplot()

As mentioned above, there are two main functions in ggplot2 package for generating graphics:

• The quick and easy-to-use function: qplot()
• The more powerful and flexible function to build the plot piece by piece: ggplot

This section describes briefly how to use the function ggplot().

Recall that, the concept of ggplot divides a plot in different fundamental parts: plot = data + Aesthetics + geometry

• data: a data frame. Columns are variables
• Aesthetics is used to specify the x and y variables. It can also be used to control the color, the size or the shape of a point, the height of a bar, etc…..
  
• Geometry corresponds to the type of graphics (histogram, boxplot, line, density, dotplot, bar, …)

To demonstrate how the function ggplot() works, we’ll draw a scatter plot:

# Basic scatter plot
ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_point()

# Change the point size, and shape
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 2, shape = 23)

The function ggplot() is intensively used in the articles available at the end of this page.

Infos

This analysis was performed using R (ver. 3.1.2) and ggplot2 (ver 1.0.0).

• ggplot2 online documentation