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22 R: ggplot2

  • R package ggplot2 provides comprehensive options for plotting

  • The package is developed with the philosophy of grammar of graphics

  • Here, we provide some minimal examples of creating plots using ggplot2

22.1 ggplot2: Overview

  • Grammar of Graphics: ggplot2 by Hadley Wickham

  • Install ggplot2 in your current R environment: install.packages('ggplot2')

  • From RStudio: Tools > Install Packages and select ggplot2 in the package name text box

  • The initial gg in ggplot2 stands for grammar of graphics based on the concept the grammar of graphics by Leland Wilkinson.

  • Grammar of Graphics: The grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (colour, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinates system.

22.2 Steps of ggplot

22.3 Essential part

Arguments Explanation
data = The DATA that you want to plot
aes() AESTHETICS of the geometric and statistical objects, such as color, size, shape and position.
geom_ The GEOMETRIC shapes that will represent the data.

22.4 Advanced part

Arguments Explanation
stat_ STATISTICAL summaries of the data that can be plotted, such as quantiles, fitted curves (loess, linear models, etc.), sums and so o.
coord_ The transformation used for mapping data COORDINATES into the plane of the data rectangle.
facet_ The arrangement of the data into a grid of plots
theme_ The overall visual THEMES of a plot: background, grids, axe, default typeface, sizes, colors, etc.
scale_ MAP between the data and the aesthetic dimensions, such as data range to plot width or factor values to colors.

22.5 Geometric functions

Function Plot Graphical_parameters
geom_histogram Histogram colour, fill, alpha
geom_freqpoly Frequency polygon colour, fill, alpha
geom_density Density plot colour, fill, alpha, linetype
geom_rug Rug plot colour, side
geom_qq Quantile-Quantile plot colour, alpha, linetype, size
geom_boxplot Box plot colour, fill, alpha, notch, width
geom_violin Violin plot colour, fill, alpha, linetype, size
geom_point Scatter plot colour, alpha, shape, size
geom_jitter Jittered points colour, alpha, shape, size
geom_text Text colour, alpha, size, label, family, fontface
geom_bar Bar chart colour, fill, alpha
geom_line Line graph colour, alpha, linetype, size
geom_hline Horizontal line colour, alpha, linetype, size
geom_vline Vertical line colour, alpha, linetype, size
geom_smooth Fitted line method, formula, colour, fill, linetype, size

22.6 Read Data

Load ggplot2 library in the R environment

library(ggplot2)

Set the working directory to the data folder and read the iris dataset as an R object DF.

DF = read.csv('iris.csv')

22.7 Single variable

22.7.1 Histogram 

g = ggplot(data = DF, mapping = aes(SepalLength)) + geom_histogram()
g

22.7.2 Density plot 

g = ggplot(data = DF, mapping = aes(SepalLength)) + geom_histogram(aes(y = ..density..))
g = g + geom_density()
g

22.7.3 Boxplot 

DF[['All Species']] = ''
g = ggplot(data = DF, mapping = aes(x = 'All Species', y = SepalLength)) + geom_boxplot()
g

22.7.4 Bar plot 

g = ggplot(data = DF, mapping = aes(x = Species)) + geom_bar()
g

22.7.5 Pie chart 

g = ggplot(data = DF, mapping = aes(x = '1', fill = Species)) + geom_bar()
g <- g + coord_polar(theta = 'y')
g

22.8 Multple variables

22.8.1 Scatter plot 

g = ggplot(data = DF, mapping = aes(x = SepalLength, y = PetalLength)) + geom_point()
g

22.8.2 Scatter plot with group 

g = ggplot(data = DF, mapping = aes(x = SepalLength, y = PetalLength, color = Species)) + geom_point()
g

22.8.3 Scatter plot matrix 

require(GGally)

g = GGally::ggpairs(DF[, 1:4])
g

22.8.4 Boxplot 

g = ggplot(data = DF, mapping = aes(x = Species, y = SepalLength)) + geom_boxplot()
g
g = ggplot(data = DF, mapping = aes(x = Species, y = SepalLength, colour = Species))
g = g + geom_boxplot() + facet_wrap( ~ Species)
g