Labels

The first things to personalize in a plot is the labels to make the plot more informative to the audience. We can easily add a title, subtitle, caption and edit axis titles with the labs() function:

p <- ggplot(dat) +
  aes(x = displ, y = hwy) +
  geom_point()

p + labs(
  title = "Fuel efficiency for 38 popular models of car",
  subtitle = "Period 1999-2008",
  caption = "Data: ggplot2::mpg. See more at www.statsandr.com",
  x = "Engine displacement (litres)",
  y = "Highway miles per gallon (mpg)"
)

As you can see in the above code, you can save one or more layers of the plot in an object for later use. This way, you can save your “main” plot, and add more layers of personalization until you get the desired output. Here we saved the main scatter plot in an object called p and we will refer to it for the subsequent personalizations.

Axis ticks

Axis ticks can be adjusted using scale_x_continuous() and scale_y_continuous() for the x and y-axis, respectively:

# Adjust ticks
p + scale_x_continuous(breaks = seq(from = 1, to = 7, by = 0.5)) + # x-axis
  scale_y_continuous(breaks = seq(from = 10, to = 45, by = 5)) # y-axis

Log transformations

In some cases, it is useful to plot the log transformation of the variables. This can be done with the scale_x_log10() and scale_y_log10() functions:

p + scale_x_log10() +
  scale_y_log10()

Limits

The most convenient way to control the limits of the plot is to use again the scale_x_continuous() and scale_y_continuous() functions in addition to the limits argument:

p + scale_x_continuous(limits = c(3, 6)) +
  scale_y_continuous(limits = c(20, 30))

It is also possible to simply take a subset of the dataset with the subset() or filter() function. See how to subset a dataset if you need a reminder.

Legend

By default, the legend is located to the right side of the plot (when there is a legend to be displayed of course). To control the position of the legend, we need to use the theme() function in addition to the legend.position argument:

p + aes(color = class) +
  theme(legend.position = "top")

Replace "top" by "left" or "bottom" to change its position and by "none" to remove it.

The title of the legend can be removed with legend.title = element_blank():

p + aes(color = class) +
  theme(
    legend.title = element_blank(),
    legend.position = "bottom"
  )

The legend now appears at the bottom of the plot, without the legend title.

Shape, color, size and transparency

There are a very large number of options to improve the quality of the plot or to add additional information. These include:

• shape,
• size,
• color, and
• alpha (transparency).

We can for instance change the shape of all points in a scatter plot by adding shape to geom_point(), or vary the shape according to the values taken by another variable (in that case, the shape argument must be inside aes()):²

# Change shape of all points
ggplot(dat) +
  aes(x = displ, y = hwy) +
  geom_point(shape = 4)

# Change shape of points based on a categorical variable
ggplot(dat) +
  aes(x = displ, y = hwy, shape = drv) +
  geom_point()

Following the same principle, we can modify the color, size and transparency of the points based on a qualitative or quantitative variable. Here are some examples:

p <- ggplot(dat) +
  aes(x = displ, y = hwy) +
  geom_point()

# Change color for all points
p + geom_point(color = "steelblue")

# Change color based on a qualitative variable
p + aes(color = drv)

# Change color based on a quantitative variable
p + aes(color = cty)

# Change color based on a criterion (median of cty variable)
p + aes(color = cty > median(cty))

# Change size of all points
p + geom_point(size = 4)

# Change size of points based on a quantitative variable
p + aes(size = cty)

# Change transparency based on a quantitative variable
p + aes(alpha = cty)

We can of course mix several options (shape, color, size, alpha) to build more complex graphics:

p + geom_point(size = 0.5) +
  aes(color = drv, shape = year, alpha = cty)

Smooth and regression lines

In a scatter plot, it is possible to add a smooth line fitted to the data:

p + geom_smooth()

In the context of simple linear regression, it is often the case that the regression line is displayed on the plot. This can be done by adding method = lm (lm stands for linear model) in the geom_smooth() layer:

p + geom_smooth(method = lm)

It is also possible to draw a regression line for each level of a categorical variable:

p + aes(color = drv, shape = drv) +
  geom_smooth(method = lm, se = FALSE)

The se = FALSE argument removes the confidence interval around the regression lines.

Facets

facet_grid allows you to divide the same graphic into several panels according to the values of one or two qualitative variables:

# According to one variable
p + facet_grid(. ~ drv)

# According to 2 variables
p + facet_grid(drv ~ year)

It is then possible to add a regression line to each facet:

p + facet_grid(. ~ drv) +
  geom_smooth(method = lm)

facet_wrap() can also be used, as illustrated in this section.

Themes

Several functions are available in the {ggplot2} package to change the theme of the plot. The most common themes after the default theme (i.e., theme_gray()) are the black and white (theme_bw()), minimal (theme_minimal()) and classic (theme_classic()) themes:

# Black and white theme
p + theme_bw()

# Minimal theme
p + theme_minimal()

# Classic theme
p + theme_classic()

I tend to use the minimal theme for most of my R Markdown reports as it brings out the patterns and points and not the layout of the plot, but again this is a matter of personal taste. See more themes at ggplot2.tidyverse.org/reference/ggtheme.html and in the {ggthemes} package.

In order to avoid having to change the theme for each plot you create, you can change the theme for the current R session using the theme_set() function as follows:

theme_set(theme_minimal())

Interactive plot with {plotly}

You can easily make your plots created with {ggplot2} interactive with the {plotly} package:

library(plotly)
ggplotly(p + aes(color = year))

You can now hover over a point to display more information about that point. There is also the possibility to zoom in and out, to download the plot, to select some observations, etc. More information about {plotly} for R can be found here.

Combine plots with {patchwork}

There are several ways to combine plots made in {ggplot2}. In my opinion, the most convenient way is with the {patchwork} package using symbols such as +, / and parentheses.

We first need to create some plots and save them:

p_a <- ggplot(dat) +
  aes(x = displ, y = hwy) +
  geom_point()

p_b <- ggplot(dat) +
  aes(x = hwy) +
  geom_histogram()

p_c <- ggplot(dat) +
  aes(x = drv, y = hwy) +
  geom_boxplot()

Now that we have 3 plots saved in our environment, we can combine them. To have plots next to each other simply use the + symbol:

library(patchwork)
p_a + p_b + p_c

To display them above each other simply use the / symbol:

p_a / p_b / p_c

And finally, to combine them above and next to each other, mix +, / and parentheses:

p_a + p_b / p_c

(p_a + p_b) / p_c

Flip coordinates

Flipping coordinates of your plot is useful to create horizontal boxplots, or when labels of a variable are so long that they overlap each other on the x-axis. See with and without flipping coordinates below:

# without flipping coordinates
p1 <- ggplot(dat) +
  aes(x = class, y = hwy) +
  geom_boxplot()

# with flipping coordinates
p2 <- ggplot(dat) +
  aes(x = class, y = hwy) +
  geom_boxplot() +
  coord_flip()

library(patchwork)
p1 + p2 # left: without flipping, right: with flipping

This can be done with many types of plot, not only with boxplots. For instance, if a categorical variable has many levels or the labels are long, it is usually best to flip the coordinates for a better visual:

ggplot(dat) +
  aes(x = class) +
  geom_bar() +
  coord_flip()

Save plot

The ggsave() function will save the most recent plot in your current working directory unless you specify a path to another folder:

ggplot(dat) +
  aes(x = displ, y = hwy) +
  geom_point()

ggsave("plot1.pdf")