Data Visualization using GGPlot2

What is ggplot2

GGPlot2 is a powerful and a flexible R package, implemented by Hadley Wickham, for producing elegant graphics piece by piece (Wickham et al. 2017).

The gg in ggplot2 means Grammar of Graphics, a graphic concept which describes plots by using a “grammar”. According to the ggplot2 concept, a plot can be divided into different fundamental parts: Plot = data + Aesthetics + Geometry

• data: a data frame
• aesthetics: used to indicate the x and y variables. It can be also used to control the color, the size and the shape of points, etc…..
• geometry: corresponds to the type of graphics (histogram, box plot, line plot, ….)

Key functions

After installing and loading the ggplot2 package, you can use the following key functions:

Example of plots

The main function in the ggplot2 package is ggplot(), which can be used to initialize the plotting system with data and x/y variables.

For example, the following R code takes the iris data set to initialize the ggplot and then a layer (geom_point()) is added onto the ggplot to create a scatter plot of x = Sepal.Length by y = Sepal.Width:

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

# Change point size, color and shape
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(size = 1.2, color = "steelblue", shape = 21)

Note that, in the code above, the shape of points is specified as number. The different point shape available in R, include:

It’s also possible to control points shape and color by a grouping variable (here, Species). For example, in the code below, we map points color and shape to the Species grouping variable.

Note that, a ggplot can be holded in a variable, say p, to be printed later

# Control points color by groups
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species, shape = Species))

# Change the default color manually.
# Use the scale_color_manual() function
p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species, shape = Species))+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
print(p)

Legend position

The default legend position is “right”. Use the function theme() with the argument legend.position to specify the legend position.

Allowed values for the legend position include: “left”, “top”, “right”, “bottom”, “none”.

Examples:

# Change legend position to the top
p + theme(legend.position = "top")

Titles and axis labels

The function labs() can be used to change easily the main title, the subtitle, the axis labels and captions.

p + labs(
  title = "Edgar Anderson's Iris Data",
  subtitle = "iris is a data frame with 150 cases (rows) and 5 variables",
  x = "Sepal Length (cm)", y = "Sepal Width (cm)"
  )

Facet: Plot with multiple pnels

You can also split the plot into multiple panels according to a grouping variable. R function: facet_wrap(). Another interesting feature of ggplot2, is the possibility to combine multiple layers on the same plot. For example, with the following R code, we’ll:

• Add points with geom_point(), colored by groups.
• Add the fitted smoothed regression line using geom_smooth(). By default the function geom_smooth() add the regression line and the confidence area. You can control the line color and confidence area fill color by groups.
• Facet the plot into multiple panels by groups
• Change color and fill manually using the function scale_color_manual() and scale_fill_manual()

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species))+               
  geom_smooth(aes(color = Species, fill = Species))+
  facet_wrap(~Species, ncol = 3, nrow = 1)+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

GGPlot theme

Note that, the default theme of ggplots is theme_gray() (or theme_grey()), which is theme with grey background and white grid lines. More themes are available for professional presentations or publications. These include: theme_bw(), theme_classic() and theme_minimal().

To change the theme of a given ggplot (p), use this: p + theme_classic(). To change the default theme to theme_classic() for all the future ggplots during your entire R session, type the following R code:

theme_set(
  theme_classic()
)

Now you can create ggplots with theme_classic() as default theme:

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point()

Further customizations of a ggplot

You can read more in our online ggplot cheatsheet, GGPlot Cheat Sheet for Great Customization, which describes how to:

• Add title, subtitle, caption and change axis labels
• Change the appearance - color, size and face - of titles
• Set the axis limits
• Set a logarithmic axis scale
• Rotate axis text labels
• Change the legend title and position, as well, as the color and the size
• Change a ggplot theme and modify the background color
• Add a background image to a ggplot
• Use different color palettes: custom color palettes, color-blind friendly palettes, RColorBrewer palettes, viridis color palettes and scientific journal color palettes.
• Change point shapes (plotting symbols) and line types
• Rotate a ggplot
• Annotate a ggplot by adding straight lines, arrows, rectangles and text.

Save ggplots

You can export a ggplot to many file formats, including: PDF, SVG vector files, PNG, TIFF, JPEG, etc.

The standard procedure to save any graphics from R is as follow:

1. Open a graphic device using one of the following functions:

• pdf(“r-graphics.pdf”),
• svg(“r-graphics.svg”),
• png(“r-graphics.png”),
• tiff(“r-graphics.tiff”),
• jpeg(“r-graphics.jpg”),
• and so on.

Additional arguments indicating the width and the height (in inches) of the graphics region can be also specified in the mentioned function.

2. Create and print a plot
3. Close the graphic device using the function dev.off()

For example, to export ggplot2 graphs to a pdf file, the R code looks like this:

# Create some plots
library(ggplot2)
myplot1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
  geom_point()
myplot2 <- ggplot(iris, aes(Species, Sepal.Length)) + 
  geom_boxplot()

# Print plots to a pdf file
pdf("ggplot.pdf")
print(myplot1)     # Plot 1 --> in the first page of PDF
print(myplot2)     # Plot 2 ---> in the second page of the PDF
dev.off() 

For printing to a png file, use:

png("myplot.png")
print(myplot)
dev.off()

It’s also possible to make a ggplot and to save it from the screen using the function ggsave():

# 1. Create a plot: displayed on the screen (by default)
ggplot(mtcars, aes(wt, mpg)) + geom_point()
# 2.1. Save the plot to a pdf
ggsave("myplot.pdf")
# 2.2 OR save it to png file
ggsave("myplot.png")

Conclusion

This article explains the basics of ggplot2 and shows how to export a ggplot to a PDF or PNG file.