ggbrookings
is a ggplot2
extension which implements the Brookings
style guide. It offers several color palettes, a custom theme, and a few
helper functions.
In order to install packages from GitHub you need to install the
remotes
package. If you’re using a Windows computer you will also need
to install RTools
, which is available
here and on the
software center if you’re using a Brookings laptop.
install.packages("remotes")
remotes::install_github("BrookingsInstitution/ggbrookings", build_vignettes = TRUE)
Note: If you are on a Brookings laptop, you may need to change your
timezone for devtools
to work. This can be done by running:
Sys.setenv(TZ = 'UTC')
Roboto is Brooking’s main font. You will need to install it by visiting Google fonts and clicking “Download family”.
Once you’ve done this, unzip and open each of the .ttf files and click install. Finaly, run the code chunk below to ensure Roboto is imported and registered:
import_roboto()
If you run into any problems while installing fonts on a Windows computer try the following solution from this issue:
remove.packages("Rttf2pt1")
remotes::install_version("Rttf2pt1", version = "1.3.8")
Currently, the ggbrookings
package only has a few simple user facing
functions:
-
theme_brookings()
overrides the defaultggplot2
theme for a custom one which adheres to the Brookings style guide. -
scale_color_brookings()
andscale_fill_brookings()
provide several color palettes that are consistent with the Brookings brand and designed to provide color accessiblity. -
brookings_view_palette()
is a helper function to see the colors from each palette and extract their hex codes. -
add_logo()
adds a program/center logo to your plots after saving them. See the vignette on adding logos for more details.
I highly recommend that you install librarian by running
install.packages('librarian')
as it lets you quickly install, update,
and attach packages from CRAN, GitHub, and Bioconductor in one function
call.
# Load the necessary libraries
librarian::shelf(tidyverse, palmerpenguins, ggbrookings)
In order to match the Brookings style in scatterplots you should set
geom_point(size = 2)
as below:
ggplot(data = penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
group = species)) +
geom_point(aes(color = species,
shape = species),
size = 2,
alpha = 0.8) +
geom_smooth(method = "lm", se = FALSE, aes(color = species)) +
theme_brookings() +
scale_color_brookings(palette = "misc") +
labs(title = "Penguin bill dimensions",
subtitle = "Bill length and depth for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER",
caption = '**Source:** Palmer Penguins',
tag = 'FIGURE 1',
x = "Bill length (mm)",
y = "Bill depth (mm)",
color = "Penguin species",
shape = "Penguin species")
ggplot(data = penguins, aes(x = flipper_length_mm)) +
geom_histogram(aes(fill = species),
alpha = 0.5,
position = "identity",
bins = 30) +
scale_fill_brookings(palette = "semantic3") +
theme_brookings() +
labs(x = "Flipper length (mm)",
y = "Frequency",
title = "Penguin flipper lengths",
caption = '**Source:** Palmer Penguins',
tag = 'FIGURE 2') +
scale_x_continuous(expand = expansion()) +
scale_y_continuous(expand = expansion())
You can change the size of your text proportionally by setting
theme_brookings(base_size = your_size)
as shown below:
ggplot(penguins, aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point(aes(color = sex),
size = 2,
show.legend = FALSE) +
theme_brookings() +
scale_color_brookings('brand1', na.translate = FALSE) +
labs(title = "Penguin flipper and body mass",
subtitle = "Dimensions for <span style = 'color:#FF9E1B;'>**male**</span> and <span style = 'color:#003A79;'>**female**</span> Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER",
caption = '**Source:** Palmer Penguins',
tag = 'FIGURE 3',
x = "Flipper length (mm)",
y = "Body mass (g)",
color = "Penguin sex") +
facet_wrap(. ~ species, nrow = 3, ncol = 1) +
scale_x_continuous(expand = expansion()) +
scale_y_continuous(expand = expansion())
In order to match the Brookings style in line plots you should set
geom_line(size = 1.5)
as below:
librarian::shelf(gapminder)
gapminder_filtered <-
gapminder %>%
filter(country %in% c('Argentina', 'Brazil', 'Uruguay'))
gapminder_filtered %>%
ggplot(aes(x = year, gdpPercap, color = country, label = country)) +
geom_line(size = 1.5) +
geom_label(data = gapminder_filtered %>% filter(year == last(year)),
aes(label = country,
x = year + 3,
y = gdpPercap + 250,
color = country)) +
theme_brookings(base_size = 16) +
guides(color = 'none') +
scale_x_continuous(breaks = seq(1952, 2007, 5),
limits = c(1952, 2012),
expand = expansion(mult = c(0, 0.1))) +
scale_y_continuous(labels = scales::label_dollar(),
limits = c(0, 15000), breaks = seq(0, 15000, 2500),
expand = expansion(0)) +
scale_color_brookings() +
labs(title = 'Economic growth in South America',
subtitle = 'GDP per capita, 1952-2007',
caption = '**Source:** Gapminder',
tag = 'FIGURE 4',
x = NULL,
y = NULL)