Take a moment to check if these load and install them if you need to.
Journals often require certain modifications to your plots to make them publication-ready
Other modifications to the appearance of your plot are a good idea, but less often required by journals or reviewers
Choose a color palette that is:
The viridis color palettes meet most of these criteria and are built-in to ggplot2
. They are available with scale_fill_viridis_*()
and scale_color_viridis_*()
functions.
Other viridis palettes are available by changing option
in the scale function
The upper end of viridis palettes tends to be very bright yellow. You can limit the range of colors used with the begin
and end
arguments
The viridis palette can be used for discrete / categorical data with scale_color_viridis_d()
.
Uh oh!
This only applied the new palette to the color
aesthetic!
Usually color
and fill
are mapped to the same data. You can add both scale_color_*()
and scale_fill_*()
to a plot OR you can use the aesthetics
argument.
There are many places to get additional color palettes.
A few of my favorites:
Activity
Let’s find a palette we like using cols4all::4a_gui()
You can always use your own colors using scale_color_manual()
if you know the hex codes.
Use a named vector to specify which colors go with which factor level
We can set the name for scales a few ways: with labs()
or with the name=
argument of the scale.
Legends for scales with the same name will be combined if possible
Let’s do the same for p3
What if we want to use the Latin name for the penguin species? We can use the labels
argument and a named vector.
Let’s do the same for p3
Tip
If there are many aesthetics that map to the same variable, it might be easier to change the factor levels in the data once instead of inside of every scale
Let’s apply what we learned to p1
to capitalize the words in the legend
Which scale_
function?
Which argument changes legend title?
Which argument changes labels?
Axes are also a type of scale. In p1
the x-axis corresponds to scale_x_discrete()
and the y-axis corresponds to scale_y_continuous()
.
Use what we learned before to customize the categorical x-axis labels in p1
!
If you only want to change the axis title, you can also do that in labs()
Change the (approximate) number of breaks with n.breaks=
Specify breaks exactly with breaks=
There are several complete themes built-in to ggplot2
, and many more available from other packages such as ggthemes
.
You can customize font size and family with complete themes.
Customizing themes “manually” involves knowing the name of the theme element and it’s corresponding element_*()
function.
It’s best to find a built-in theme_*()
function that gets you most of the way there and then customize with theme()
Activity
Name some things about the appearance of p1
that you want to change and we’ll figure it out together!
Tip
Check the examples in the help page for theme()
https://ggplot2.tidyverse.org/reference/theme.html to figure out the names of theme elements
You can save a custom theme as an R object and supply it to your plots.
Or you can set your theme as the default at the top of your R script
The patchwork
package makes it easy to combine ggplot2
plots
plot_layout(guides = "collect")
combines duplicate legendsplot_annotation(tag_levels = "a")
adds labels to sub-plotsIf you know the dimensions, it’s good to save plots early on and adjust theme to fit.
library(tidyverse) #includes ggplot2
library(palmerpenguins) #for data
library(patchwork) #multi-panel figures
# Custom theme
my_theme <-
theme_minimal(base_size = 10) +
theme(
axis.line = element_line(linewidth = 0.5, lineend = "round"),
axis.ticks = element_line(linewidth = 0.2),
legend.background = element_rect(linewidth = 0.2)
)
theme_set(my_theme)
# plot 1
## Custom function for stat_summary
mean_sd <- function(x) {
data.frame(y = mean(x), ymin = mean(x) - sd(x), ymax = mean(x) + sd(x))
}
## For labeling with latin names
scinames <- c("Adelie" = "P. adeliae",
"Chinstrap" = "P. antarticus",
"Gentoo" = "P. papua")
p1 <-
ggplot(penguins |> filter(!is.na(sex)),
aes(x = species, y = body_mass_g, shape = sex)) +
geom_point(alpha = 0.2,
position = position_jitterdodge(dodge.width = 0.75)) +
stat_summary(fun.data = mean_sd,
position = position_dodge(width = 0.75)) +
scale_x_discrete(labels = scinames) +
scale_y_continuous(n.breaks = 12) +
labs(x = "Species", y = "Body Mass (g)")
# Color palette for plot 2 and 3
my_cols <-
c("Chinstrap" = "#B60A1C", "Gentoo" = "#E39802", "Adelie" = "#309143")
p2 <-
ggplot(penguins,
aes(
x = flipper_length_mm,
y = bill_length_mm,
color = species,
fill = species,
shape = species
)) +
geom_point() +
geom_smooth(method = "lm") +
scale_color_manual(
values = my_cols,
labels = scinames,
aesthetics = c("color", "fill")
) +
scale_shape_discrete(labels = scinames) +
labs(
color = "Penguin Species",
shape = "Penguin Species",
fill = "Penguin Species",
x = "Flipper Length (mm)",
y = "Bill Length (mm)"
)
p3 <-
ggplot(penguins,
aes(
x = flipper_length_mm,
y = bill_depth_mm,
color = species,
fill = species,
shape = species
)) +
geom_point() +
geom_smooth(method = "lm") +
scale_color_manual(
values = my_cols,
labels = scinames,
aesthetics = c("color", "fill")
) +
scale_shape_discrete(labels = scinames) +
labs(
color = "Penguin Species",
shape = "Penguin Species",
fill = "Penguin Species",
x = "Flipper Length (mm)",
y = "Bill Length (mm)"
)
# combine into multi-panel figure
p_combined <-
p1 /
(p2 + p3 + plot_layout(guides = "collect")) +
plot_annotation(tag_levels = "a", tag_suffix = ")")
p_combined
The ggplot2
documentation itself, in particular the examples on the theme()
page and the FAQ on customization.
The R Graph Gallery has a wide variety of plots with R code walkthroughs.
The ggplot2
book: https://ggplot2-book.org/
You can always come by our drop-in hours to ask questions as well!
ggplot2
extensions”🗓️ June 26
⌚️ 11:00am–1:00pm