5.1 titles/labels

To correct the title in the previous density plot we could use an xlab() layer (short for “x-axis label”), but I prefer the labs() function instead because you can use it to modify multiple labels/titles in the same layer, e.g. both the x and y axis labels:

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) + 
  geom_histogram() +
  labs(x = "body mass (g)", y = "count (using 30 bins)")

Other labels that can be modified with labs() layer include:

• “title”: the title of the ggplot, which appears at the top and is left-aligned by default.

• “subtitle”: a subtitle for the ggplot that is in a slightly smaller font size and positioned just below the title (if there is one)

• “caption”: a caption for the ggplot, which appears at the bottom and is right-aligned by default.

• “tag”: adds a tag to the plot. Think about this as putting a letter “a” (or whatever text you want) in the top left corner of the graph (by default) to tag it as part a of multi-plot figure.

• “colour”/“color”: if a variable has been assigned to “colour” (“color” is also valid if you prefer); this controls the title of colour legend.

• “fill”: if a variable has been assigned to “fill”, this controls the title of fill legend.

• “size”: if a variable has been assigned to “size”, this controls the title of size legend.

• “shape”: if a variable has been assigned to “shape”, this controls the title of shape legend.

• “linetype”: if a variable has been assigned to “linetype”, this controls the title of the linetype legend.

N.B. you can remove any of these labels by setting them to NULL in a labs() layer, e.g.:

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) + 
  geom_histogram() +
  labs(x = "body mass (g)", y = NULL,
       caption = "source: palmerpenguins R package")

5.2 fill

The fill aesthetic in ggplot2 controls the fill colour of ggplot geometric components. You can either map a variable to fill using the aes() function,

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(aes(fill = species)) +
  labs(fill = "Species")

…or you can set it to a single value (as a character string) to be applied globally by specifying it in the geometry layer outside of aes().

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(fill = "blue2")

As you can see, when you assign a variable to the fill (colour) aesthetic, you get different fill colours for each value/level of the specified variable, which is a convenient way of splitting the data in a ggplot. You’ll also get a legend by default. Other aesthetics covered below behave similarly (colour, shape, size, etc.).

Recall from the last post that we can see the colour options available in base R via elucidate::colour_options()…

elucidate::colour_options()

…which will be easy to read if you run it yourself and expand it to full screen viewing mode.

ggplot2 also provides scale_* functions that give you finer control over many aesthetic features of the graphs you build. For example, we can add a scale_fill_manual() layer to change the fill colours assigned to groups in a histogram…

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(aes(fill = species)) +
  scale_fill_manual(values = c("royalblue3", "darkorchid2", "green3"))

…or scale_fill_continuous()/scale_fill_gradient() if a continuous variable has been assigned to the fill aesthetic in a scatter plot.

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, fill = body_mass_g)) +
  geom_point(shape = 21, size = 4) +
  scale_fill_continuous(low = "blue", high = "red")

5.3 colour/color

The colour aesthetic in ggplot2 controls the outline colour of ggplot geometric components. Note that both the British/Canadian (colour) and American (color) English spellings are valid thanks to the ggplot2 authors. Like fill, you can either map a variable to outline colour using the aes() function,

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(aes(colour = species))

…or you can set it to a single value (outside of aes()).

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(colour = "blue2",
                 fill = "white")

For finer control, you can use scale_colour_manual()…

penguins2 %>% 
  ggplot(aes(y = body_mass_g, x = species, colour = species)) +
  geom_boxplot() +
  scale_colour_manual(values = c("green3", "blue3", "red2"))

…or scale_colour_continuous(), just like the scale_fill_* functions. If you want to specify more than 2 colours to use in constructing a colour gradient, then try the scale_colour_gradientn() function instead³.

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, colour = body_mass_g)) +
  geom_point(shape = "square", size = 4) +
  scale_colour_gradientn(colours = c("blue", "yellow", "red")) #in order from low to medium to high

5.4 reordering factors

To move the box plot for Gentoo penguins to the far left in the second last graph, we can use forcats::fct_relevel() in a call to dplyr::mutate() before passing the data to the ggplot() function.

penguins2 %>% 
  mutate(species = fct_relevel(species, "Gentoo")) %>% 
  ggplot(aes(y = body_mass_g, x = species, colour = species)) +
  geom_boxplot() +
  #the order of colours should match the order of factor levels
  scale_colour_manual(values = c("green3", "blue3", "red2")) 

See the earlier post on factors and the forcats package for more information on manipulating factor levels in R.

5.5 colour palettes

If you don’t want to pick individual colours to use yourself, you should be aware that there are several packages that provide conveniently organized colour palettes. See this link for the most comprehesive list I’ve come across so far. For the purposes of this blog post I will only cover four of these packages: the RColorBrewer, ggsci, colorblindr, and viridis packages.

RColorBrewer, which is installed automatically for you with ggplot2, was perhaps the first package to offer convenient color palettes for data visualization in R, and provides three types of palettes: sequential, diverging, and qualitative. According to the package documentation:

1. Sequential palettes are appropriate for ordered variables that progress from low to high values, where low values are assigned light colours and high values are assigned dark colours. Sequential palettes are useful if you want to emphasize extreme values.

2. Diverging palettes are useful when you want to place equal emphasis on low, middle, and high values of an ordered variable.

3. Qualitative palettes are useful for unordered categorical variables where the goal is to simply use distinct colours for each group. I tend to use these ones most often out of the RColorBrewer palettes.

You can view the RColorBrewer palette options in your R session’s active graphics device (e.g. R studio “plot” tab) with RColorBrewer::display.brewer.all(). Here I’ll show you the different palette types separately so they all appear on the screen legibly, but you will be able to see them all at once if you run the default display.brewer.all() command yourself.

#sequential
RColorBrewer::display.brewer.all(type = "seq")

#diverging
RColorBrewer::display.brewer.all(type = "div")

#qualitative
RColorBrewer::display.brewer.all(type = "qual")

ggplot2 provides scale_fill_brewer() and scale_colour_brewer() (or scale_color_brewer() if you prefer) as convenience functions that allow you to access these palettes via the “palette” argument. E.g.

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(aes(fill = species)) +
  scale_fill_brewer(palette = "Dark2")

Alternatively, you can use the RColorBrewer::brewer.pal() function with the “n” and “name” arguments to generate a character vector of colour names of length n that can be passed to any plotting function in R.

RColorBrewer::brewer.pal(n = 3, name = "Set1")

## [1] "#E41A1C" "#377EB8" "#4DAF4A"

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(aes(fill = species)) +
  scale_fill_manual(values = RColorBrewer::brewer.pal(n = 3, name = "Set1"))

Those of us who are interested in publishing our work in peer-reviewed journals may be interested in using the ggsci package, which provides palettes inspired by common colour schemes used by prominent journals, like Nature, Science, JAMA, and The New England Journal of Medicine (and several others inspired by science fiction). When ggsci is loaded, you can access these palettes with additional scale_fill_* and scale_colour/color_* functions, where * can be any of the ggsci palette names. See the package website for a full list. For the sake of brevity, I will just demonstrate the Nature-themed palette because, like most other scientists, I’ve always dreamed of one day publishing in Nature.

library(ggsci)

penguins2 %>% 
  ggplot(aes(y = body_mass_g)) +
  geom_boxplot(aes(fill = species)) +
  scale_fill_npg() #npg = Nature Publishing Group

As soon as you have some data worthy of publication in Nature (good luck!) you’ll now have no trouble making figures for it!

5.6 colourblind-friendly palettes

To assess the friendliness of your graphs to colour blind individuals, you should check out the website for the colorblindr package by ggplot2’ developer and author of The Fundamentals of Data Visualization, Claus Wilke. You can even upload an image file of a finished graph and simulate how it will look like to colour blind individuals with this cool simulation tool.

The viridis package’s colour palettes are all colourblind-friendly, examples of which are shown here.

viridis doesn’t come with a built-in convenience function to display all of the colour palettes at once, but we can show them each using scales::show_col() and viridis::viridis_pal(). Five options can be displayed this way:

#both of these packages are installed for you with ggplot2 so we can just load them
library(scales)
library(viridis)

## Warning: package 'viridisLite' was built under R version 4.0.5

show_col(viridis_pal(option = "magma")(9)) #equivalent to option = "A".

show_col(viridis_pal(option = "inferno")(9)) #equivalent to option = "B".

show_col(viridis_pal(option = "plasma")(9)) #equivalent to option = "C".

show_col(viridis_pal(option = "viridis")(9)) #the default, equivalent to option = "D".

show_col(viridis_pal(option = "cividis")(9)) #equivalent to option = "E".

We can use one of these palettes in a ggplot with viridis::scale_fill_viridis() or viridis::scale_colour_viridis() (or viridis::scale_color_viridis())

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(aes(fill = species)) +
  scale_fill_viridis(option = "viridis", #the default
                     discrete = TRUE) 

                     #discrete = TRUE needs to be added here to use
                     #scale_*_viridis() for a discrete variable

5.7 size

The obviously named “size” aesthetic parameter adjusts the size of a geom. Like other ggplot2 aesthetics, you would either assign a numeric variable to size in aes()…

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, 
             size = body_mass_g)) + #point size will be proportional to body_mass_g
  geom_point(colour = "blue2") #scatter plot

…or set the geom size to a specific value.

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4) 

As you can see, setting an additional continuous variable to the size aesthetic of a scatter plot is an easy way to explore relationships between 3 variables at once (also called a “bubble chart”). I also find it easier to read than extending the plot into a third spatial dimension, which we can do with plotly instead of ggplot2:

penguins2 %>% 
  plotly::plot_ly(x = ~bill_length_mm, 
                  y = ~bill_depth_mm, 
                  z = ~body_mass_g,
                  type = "scatter3d", 
                  color = I("blue2"))

#hint: try hovering your mouse over this plot and clicking/dragging it in
#different directions to rotate it.

N.B. I recommend against using 3D scatter plots like this for anything other than data exploration on your own or if you are planning to personally demonstrate the relationships between the three variables by rotating the graph for an audience.

5.8 shape

As you may have guessed, the “shape” parameter affects the shape of the chosen geom, which is typically the points of a scatter plot. You can either map a categorical variable to it…

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, 
             shape = island)) + 
  geom_point(size = 3) #scatter plot

…or set it to a specific shape choice (see here for options).

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(size = 3, shape = 21)

5.9 linetype

Changes the type of line (solid, dashed, dotted, etc.) in a line graph. Set globally in the geom_line() layer…

penguins2 %>%
  mutate(year = factor(year, ordered = TRUE)) %>% 
  group_by(year) %>% 
  summarise(avg_body_mass_g = mean(body_mass_g), .groups = "drop") %>% #to get a single value per year
  ungroup() %>% 
  ggplot(aes(x = year, y = avg_body_mass_g)) + 
  geom_line(aes(group = 1), linetype = "dashed", size = 1.5)

#set the group aesthetic to 1 to disable grouping of lines

…or map a categorical variable to it with aes():

penguins2 %>%
  mutate(year = factor(year, ordered = TRUE)) %>%
  group_by(year, species) %>%
  summarise(avg_body_mass_g = mean(body_mass_g), .groups = "drop") %>% #to get a single value per year
  ungroup() %>%
  ggplot(aes(x = year, y = avg_body_mass_g)) + 
  geom_line(aes(group = species, linetype = species), size = 1.5)

  #In aes() for geom_line(), "group" splits the lines and "linetype" assigns a
  #different line type to each group

5.10 alpha (transparency/opacity)

The “alpha” aesthetic adjusts the transparency of a geom and can be specified using a value ranging between 0 (0% opaque/100% transparent) and 1 (100% opaque/0% transparent). It is especially helpful in cases where “overplotting” obscures some of the data because there are too many overlapping elements, like the scatter plot I made of penguin bill depth by bill length…

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4, alpha = 0.5) 

…or density plots of penguin body masses grouped by species.

penguins2 %>% 
  ggplot(aes(x = body_mass_g, fill = species, group = species)) +
  geom_density(alpha = 0.6)

5.11 jittering to reduce overplotting

In addition to modifying the transparency of a geom to deal with overplotting, we can also slightly move geometric objects in random directions, or “jitter” them by adding random noise. This should retain most statistical properties of the data (because the displacements are random), while making it easier to see more of the data. Jittering can be used with any ggplot2 geom that accepts the output of the ggplot2::position_jitter() function as an option for a “position” argument. This is so common in scatter plots that there is even a shortcut function, ggplot2::geom_jitter(), that you can use instead of ggplot2::geom_point(position = position_jitter()).

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +  
  geom_jitter(colour = "blue2", size = 3, alpha = 0.5) 

  #or, equivalently: 
  # geom_point(colour = "blue2", size = 3, alpha = 0.5, 
  #            position = position_jitter())

One exception to this advice is the situation when the geom you want to jitter happens to be text, e.g. labels for the species of a penguin that were rendered via ggplot2::geom_text(aes(label = species)), like this:

set.seed(2021) #set random number generator seed for reproducibility

#I'll just sample 50 rows at random (i.e. data for 50 penguins) for demo
#purposes
penguins_sample <- penguins2[sample(seq_len(nrow(penguins2)), 50), ] 

penguins_sample %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +  
  geom_jitter(colour = "blue", size = 3, alpha = 0.5) +
  geom_text(aes(label = species)) 

The best option for moving the labels out of the way of the points and other labels is to use the ggplot2 extension package ggrepel, which contains a geom_text_repel() function to repel labels from one another in ggplots. It also conveniently works with geom_jitter(), as shown here:

penguins_sample %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +  
  geom_jitter(colour = "blue", size = 3, alpha = 0.5) +
  ggrepel::geom_text_repel(aes(label = species))

That’s so much easier to read!

5.12 coordinates

set.seed(2021) #set the random number generator seed for reproducibility

gapminder::gapminder %>% 
  filter(country %in% sample(unique(gapminder::gapminder$country), 20)) %>% #we'll try to plot 20 for now 
  ggplot(aes(x = country, y = gdpPercap)) +
  geom_violin(fill = "limegreen")

gapminder::gapminder %>% 
  filter(country %in% sample(unique(gapminder::gapminder$country), 20)) %>% 
  ggplot(aes(x = country, y = gdpPercap)) +
  geom_violin(fill = "limegreen") +
  coord_flip()

gapminder::gapminder %>% 
  filter(country %in% sample(unique(gapminder::gapminder$country), 20)) %>% 
  ggplot(aes(x = country, y = gdpPercap)) +
  geom_violin(fill = "limegreen") +
  coord_cartesian(ylim = c(0, 2500)) 

gapminder::gapminder %>% 
  filter(country %in% sample(unique(gapminder::gapminder$country), 20)) %>% 
  ggplot(aes(x = country, y = gdpPercap)) +
  geom_violin(fill = "limegreen") +
  coord_cartesian(ylim = c(0, 2500)) +
  coord_flip()

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

gapminder::gapminder %>% 
  filter(country %in% sample(unique(gapminder::gapminder$country), 20)) %>% 
  ggplot(aes(x = country, y = gdpPercap)) +
  geom_violin(fill = "limegreen") +
  coord_flip(ylim = c(0, 2500)) 

5.13 guides (legends)

I’m sure you’ve noticed that whenever we map a variable to an aesthetic parameter inside the aes() function, a guide (AKA legend) is automatically added to the plot for us. If you want to omit a guide, simply set it to FALSE for that aesthetic parameter, like this:

penguins2 %>% 
  ggplot(aes(x = species, y = body_mass_g, fill = species)) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE) +
  guides(fill = FALSE)

In this case, it makes sense to omit the guide for fill because it isn’t needed. I suppose the fill colours aren’t needed for this graph either, but you might want to use them for consistency with other plots where the guide was needed.

5.14 x & y axis modifications

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4, alpha = 0.5) +
  scale_x_continuous(limits = c(30, 60),
                     breaks = seq(from = 30, #start at 
                                   to = 60, #end at
                                   by = 5)) #by this increment

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4, alpha = 0.5) +
  scale_x_continuous(name = "bill length (mm)",
                     trans = "reverse",
                     #keep in mind the transformation when specifying axis limit
                     #and breaks
                     limits = c(60, 30),
                     breaks = seq(60, 30, -5))

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4, alpha = 0.5) +
  scale_y_continuous(position = "right")

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(colour = "blue2", size = 4, alpha = 0.5) +
  scale_x_continuous(name = "bill length (mm)",
                     sec.axis = sec_axis(~ . /10, name = "bill length (cm)")) +
  scale_y_continuous(name = "bill depth (mm)",
                     sec.axis = sec_axis(~ . /10, name = "bill depth (cm)"))

#where "." in this case represents the value of the original y axis

bw <- 100 #define bin width to be used in the histogram
n_obs <- sum(!is.na(penguins2$body_mass_g)) #count of non-missing observations

#define a function that returns the normal density curve scaled by the bin width
#and number of observations
scaled_dnorm <- function(x) {
    dnorm(x, mean = mean(penguins2$body_mass_g), sd = sd(penguins2$body_mass_g)) * (bw * n_obs)
}

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(binwidth = bw) +
  #pass the custom scaled_dnorm function to the fun argument of a
  #stat_function() layer
  stat_function(fun = scaled_dnorm, colour = "blue2", size = 1.5, alpha = 0.8) + 
  #then reverse the scaling in the sec.axis = sec_axis() part of
  #scale_y_continuous so the secondary y-axis shows the density values
  scale_y_continuous(sec.axis = sec_axis(trans = ~./(bw*n_obs), name = "density", labels = scales::scientific)) +
  #optionally, make the secondary y-axis the same colour as the density curve
  #using theme()
  theme(axis.ticks.y.right = element_line(colour = "blue2"),
        axis.text.y.right = element_text(colour = "blue2"), 
        axis.title.y.right = element_text(colour = "blue2")) 

penguins2 %>% 
  ggplot(aes(x = species, y = body_mass_g)) +
  geom_boxplot()

penguins2 %>% 
  ggplot(aes(x = species, y = body_mass_g)) +
  geom_boxplot() +
  scale_x_discrete(name = "Species", 
                   position = "top", 
                   limits = c("Chinstrap", "Gentoo"),
                   labels = c("C", "G"))

## Warning: Removed 146 rows containing missing values (stat_boxplot).

5.15 axis label scales

The scales package, a ggplot2 dependency⁴, makes it incredibly easy to reformat x and y axis labels (among other things). For example, if you wanted to insert commas in every three digits of the labels for a continuous variable, you could use scales::label_comma() by passing it to the “labels” argument of scale_x_continuous(), like this:

penguins2 %>% 
  ggplot(aes(x = body_mass_g, y = flipper_length_mm)) +
  geom_point() +
  scale_x_continuous(labels = scales::label_comma())

or convert very large/small values, e.g. populations of countries in the gapminder data, to scientific notation on the x axis of a scatter plot with scales::label_scientific()

gapminder::gapminder %>% #using data frame gapminder from the gapminder package
  ggplot(aes(x = pop, y = gdpPercap)) +
  geom_point(colour = "blue2", size = 2, alpha = 0.5) +
  scale_x_continuous(labels = scales::label_scientific())

or add dollar symbols to the GDP per capita values on the y-axis with scales::label_dollar(). It’s definitely worth spending a few minutes to peruse the reference page and take stock of the many options.

While we’re here, I’ll incorporate some other features in the scale_x_continuous() layer by applying a base 10 logarithmic transformation to both axes to spread things out a bit from the bottom-left corner. Perhaps it would also be easier to read if we mapped another variable to the colour aesthetic, like life expectancy, using the “plasma” viridis colour palette.

gapminder::gapminder %>% #using data frame gapminder from the gapminder package
  ggplot(aes(x = pop, y = gdpPercap, fill = lifeExp)) +
  geom_point(size = 3, alpha = 0.7, colour = "black", shape = 21) +
  scale_x_continuous(trans = "log10",
                     labels = scales::label_scientific()) +
  scale_y_continuous(trans = "log10",
                     labels = scales::label_dollar())  +
  scale_fill_viridis(option = "plasma", end = 0.9) 

Much better for only a few extra lines of code!

5.16 themes

The theme() function in ggplot2 gives you much finer control over graphical features unrelated to the data being plotted, like the background color of the ggplot() foundation layer, or the font typeface (i.e. bold, italic). There are far too many graphical feature modifications accessible to you through theme() to cover them in this post. I will therefore leave it as an exercise to the reader to simply consult the function’s reference page as needed when building graphs.

Fortunately, you won’t need to use the theme() function directly too often, as there are many theme_* shortcut functions that control all non-data elements of a ggplot which are available from ggplot2 itself and expansion packages like ggthemes that you can use instead.

The themes available in ggplot2 itself are:

theme_grey()/theme_gray(); the ggplot2 default

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_grey(base_size = 14) #base_size = 14 changes the base font size to 14

Note that the theme_* layer is the key part of the code in these examples.

theme_bw()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_bw(14) #base_size is the first argument so you don't need to specify it

theme_classic(); the elucidate package default

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_classic(14) 

theme_dark()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_dark(14) 

theme_light()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_light(14) 

theme_linedraw()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_linedraw(14) 

theme_minimal()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_minimal(14) 

…and theme_void()

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_void(14)

The ggthemes package offers a nice variety of additional themes like theme_wsj() combined with matching fill colour palette scale_fill_wsj()…

penguins2 %>% 
  ggplot(aes(x = body_mass_g, fill = species)) +
  geom_density(alpha = 0.5) +
  ggthemes::theme_wsj(14) + #wsj = Wall Street Journal
  ggthemes::scale_fill_wsj()

…that you should try out on a rainy day.

One last thing to be aware of for now is that you can set a theme for all of the ggplots you make with the ggplot2::theme_set() function. Just wrap it around the theme*, like this:

theme_set(theme_classic())

…to change the default ggplot2 theme from theme_grey() to theme_classic() for the duration of your R session.

5.17 text formatting with ggtext

The ggtext extension package for ggplot2 makes it considerably easier to change the formatting of text in ggplot graphs via Markdown and html rendering. ggtext provides a few new theme elements to simplify text formatting, such as element_markdown() & element_textbox() (or element_textbox_simple()). According to the linked webpage, the following text element customization features are currently supported:

• bold and/or italics (*text* for italics, **text** for bold, and ***text*** for both)
• changing the font
• colour
• size
• subscript or superscript placement of text (<sub>text to be subscripted</sub> or <sup>text to be superscripted</sup>)
• “rudimentary” support for images

For example, to bold segments of the x and y axis titles we can use the **text** double asterisk Markdown syntax and then call ggtext::element_markdown() (in place of ggplot2::element_text()), in a theme() layer. We can also specify a line break in a title using <br>, and adjust the colour of a portion of a label between an html-defined “span”, using:

<span style = 'color:r_colour_name;'>text</span>

or

<span style = 'color:r_colour_hex_code;'>text</span>

penguins2 %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, fill = species)) +  
  geom_jitter(size = 4, alpha = 0.5, shape = 21) +
  scale_fill_manual(values = c("Red3", "blue3", "green3")) +
  #change the x and y axis titles with the **text** markdown syntax to bold part
  #of the text
  labs(x = "**bill length** (mm)", 
       y = "**bill depth** (mm)",
       #customize the color of parts of the plot title using the html 
       # <span>style = 'color:colorname;'>text to be coloured</span> syntax
       #Notice from this example that you can also use the markdown bold/italics
       #syntax on text between the <span> </span> segments
       title = "Scatterplot of bill lengths and bill depths of <span style = 'color:red3;'>***P. adeliae*** **(Adelie)**</span>, 
       <span style = 'color:blue3;'>***P. antarcticus*** **(Chinstrap)**</span>, 
       and <span style = 'color:green3;'>***P. papua*** **(Gentoo)**</span> penguins in the Palmer Archipelago") +
  theme_bw() +
  theme(plot.title = ggtext::element_textbox_simple(size = 12), #adjust the font size of the title
        #adjust the colour of the entire label in the theme layer like this
        axis.title.x = ggtext::element_markdown(colour = "darkorchid4", 
                                                size = 14), 
        #adjust the colour of the entire label in the theme layer like this
        axis.title.y = ggtext::element_markdown(colour = "darkorchid4", 
                                                size = 14)) + 
  #because we've identified the species fill colour in the plot title we no
  #longer need the fill legend
  guides(fill = FALSE)  

It’s a bit beyond the scope of this post, but if you’re interested in learning how to write your own theme_* function, I recommend checking out this other blog post by Thomas Mock, the creator of TidyTuesday.

5.18 fonts

If you want to change the font family of the text elements of a graph, you can use the “base_family” argument accepted by most theme_* layers. With just ggplot2 installed, you’ll only be able to access these three font options⁵:

1. “serif” = Times New Roman

2. “sans” = Arial, which is the ggplot2 default.

3. “mono” = Courier New

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue2", alpha = 0.5) +
  theme_bw(base_size = 16, #font size 
           base_family = "serif") #font family

Winston Chang’s extrafont package makes it easy to access many more font family options. Simply run extrafont::font_import() once after installing the package on a computer to import the available True Type fonts from your operating system. It can take a few minutes for extrafont::font_import() to locate the font information located on your computer and copy it over to R so be patient.

extrafont::font_import() #run once per computer or after installing a new font

Afterwards, you should see a much longer list of font options if you use the extrafont::fonts() or extrafont::fonttable() function, like this:

font_vector <- extrafont::fonts()

#I'll just print a random sample of 20 font options as examples here
sample(font_vector, 20)

##  [1] "Californian FB"         "Felix Titling"          "Corbel"                
##  [4] "Book Antiqua"           "Vivaldi"                "Stencil"               
##  [7] "SimSun-ExtB"            "Myanmar Text"           "Perpetua Titling MT"   
## [10] "Trebuchet MS"           "Calisto MT"             "HP Simplified"         
## [13] "Bell MT"                "Bradley Hand ITC"       "Palatino Linotype"     
## [16] "Agency FB"              "Franklin Gothic Medium" "Script MT Bold"        
## [19] "Harrington"             "Britannic Bold"

After importing fonts from your OS with extrafont::font_import() I recommend restarting R ([ctrl] + [shift] + [F10] on Windows/Linux; [cmd] + [shift] + [F10] on Mac). Then you will just need to load the package with library(extrafont) to make the additional fonts available as options to ggplot theme_* layers using the “base_family” argument, like this:

library(extrafont)

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density() +
  theme_bw(base_size = 16, base_family = "Broadway") 

…or you can use them in a separate theme() layer if you don’t want to change other aspects of the theme, like this:

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density() +
  theme(text = element_text(size = 14, family = "Broadway"))

As pointed out in a webinar I attended today by my colleague Sam Albers, this is a really nice feature if you want to align the font of your graphs with the rest of the text in a PowerPoint presentation or other document format created with R markdown. R markdown and reproducible reporting will be covered in detail later on in the “communication” part of the blog series.

6.1 facet_wrap() and facet_grid()

To split a ggplot into multiple panels, or facets, all we need to do is add a ggplot2::facet_wrap() or ggplot2::facet_grid() layer to a graph.

facet_wrap() is typically used when you want to split the graph into panels based on a single variable, which is done with the ~variable syntax.

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue", alpha = 0.8) +
  facet_wrap(~species) 

#note: the optional nrow or ncol arguments control the number of rows or columns
#to use

I would recommend using facet_grid() instead if you want to use more than one variable for faceting, like penguin species and the islands they were observed on, where one variable goes to the left side of the ~ and the other goes on the right side.

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue", alpha = 0.8) +
  facet_grid(island~species) #split vertically by island ~ split horizontally by species 

There are a few empty panels here because not all species of penguin were observed on all islands. You can also use two variables for faceting like this with facet_wrap(x~y) if you don’t want any empty panels, because facet_wrap() will just render one panel for each unique combination of the faceting variable levels that has some data. In this case, the group labels for the additional faceting variable will appear above or below the labels for the first variable. To diambiguate them, you can update the facet labels by passing the ggplot2::label_both to the “labeller” argument. You may have also noticed that when you use faceting, the x and y axis scales of all panels are standardized by default. If you want to allow them to vary, set the “scales” argument in the facet_* layer to “free” instead of the default value of “fixed”. For the sake of brevity, I’ll apply both changes at once.

penguins2 %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue", alpha = 0.8) +
  facet_wrap(island~species, 
             labeller = label_both,
             scales = "free") #use "free_y" or "free_x" instead to allow just one axis to vary

In case you were wondering, we can use the forcats package to change the order of the facets as well. This is another example of the consistency across ggplot2 functions that makes it so much fun to graph with.

penguins2 %>% 
  mutate(species = fct_relevel(species, c("Gentoo", "Adelie", "Chinstrap")),
         island = fct_relevel(island, c("Torgersen", "Biscoe", "Dream"))) %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue", alpha = 0.8) +
  facet_wrap(island~species, labeller = label_both)

Changing the facet strip label text within the facet_wrap() or facet_grid() layer is just as easy if you simply recode/relabel the variable before passing it to ggplot(). For example, we can add a more detailed prefix to the facet strip labels with forcats::fct_relabel() and paste0() in a dplyr::mutate() layer of our graphing pipeline. We can also move the facet label strips from the top of the graph to the bottom by setting the “strip.position” argument of facet_wrap() or facet_grid() to “bottom”.

penguins2 %>% 
  mutate(species = paste0("Species of penguin: ", species)) %>% 
  ggplot(aes(x = body_mass_g)) +
  geom_density(fill = "blue", alpha = 0.8) +
  facet_wrap(~species, 
             scale = "free",
             strip.position = "bottom")

6.2 too many facets

Sometimes you just have too many facets for them to show up legibly on a single page. If this is a problem for you, check out the trelliscopejs package, which turns the ggplot into an interactive html widget that enables you to browse a smaller number of facets at a time so you can actually see the data.

Just swap out ggplot2::facet_wrap() for trelliscopejs::facet_trelliscope() and set the initial layout of the viewing pane with the “ncol” and “nrow” arguments.

Unfortunately I can’t demonstrate facet_trelliscope() for you in this post because trelliscopejs::facet_trelliscope() is currently incompatible with blogdown (the package I use to write my blog), but it should work for you if you try it yourself. There is also an introductory vignette here that you can visit to see some examples.