Modifying Colors and Themes
Professor Ron Yurko
The graphs below don’t have proper titles, axis labels, legends, etc. Please take care to do this on your own graphs.
Throughout this demo we will once again use the palmerpenguins
dataset. To access the data, you will need to install the
palmerpenguins package:
install.packages("palmerpenguins")library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(palmerpenguins)
data(penguins)Notes on colors in plots
Three types of color scales to work with:
- Qualitative: distinguishing discrete items that don’t have an order (nominal categorical). Colors should be distinct and equal with none standing out unless otherwise desired for emphasis.
- Do NOT use a discrete scale on a continuous variable
- Sequential: when data values are mapped to one shade, e.g., in a choropleth, for an ordered categorical variable or low to high continuous variable
- Do NOT use a sequential scale on an unordered variable
- Divergent: think of it as two sequential scales with a natural midpoint midpoint could represent 0 (assuming +/- values) or 50% if your data spans the full scale
- Do NOT use a divergent scale on data without natural midpoint
Options for ggplot2 colors
The default color scheme is pretty bad to put it bluntly, but
ggplot2 has ColorBrewer built in which makes it easy to
customize your color scales. For instance, we change the palette for the
species plot from before.
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
scale_color_brewer(palette = "Set2") +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_bw()## Warning: Removed 2 rows containing missing values (geom_point).
Something you should keep in mind is to pick a color-blind
friendly palette. One simple way to do this is by using the
ggthemes package which has color-blind friendly palettes
included:
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_bw()## Warning: Removed 2 rows containing missing values (geom_point).
In terms of displaying color from low to high, the viridis scales are excellent choices (and are also color-blind friendly!).
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm,
color = flipper_length_mm)) +
geom_point(alpha = 0.5, size = 2) +
scale_color_viridis_c() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)",
color = "Flipper Length (mm)") +
theme_bw()## Warning: Removed 2 rows containing missing values (geom_point).
Notes on themes
We have not explicitly talked about this throughout the semester, but
you have seen various changes to the theme of plots for
customization. You will constantly be changing the theme of your
plots to optimize the display. Fortunately, there are a number
of built-in themes you can use to start with rather than the default
theme_gray():
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_gray()## Warning: Removed 2 rows containing missing values (geom_point).
For instance, you have seen me use theme_bw() many times
throughout the semester:
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_bw()## Warning: Removed 2 rows containing missing values (geom_point).
There are options such as theme_minimal():
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_minimal()## Warning: Removed 2 rows containing missing values (geom_point).
or theme_classic():
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_classic()## Warning: Removed 2 rows containing missing values (geom_point).
There are also packages with popular, such as the
ggthemes package which includes, for example,
theme_economist():
library(ggthemes)
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_economist()## Warning: Removed 2 rows containing missing values (geom_point).
and theme_fivethirtyeight() to name a couple:
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)") +
theme_fivethirtyeight()## Warning: Removed 2 rows containing missing values (geom_point).
With any theme you have picked, you can then modify specific
components directly using the theme() layer. There are many aspects
of the plot’s theme to modify, such as my decision to move the
legend to the bottom of the figure, drop the legend title, and increase
the font size for the y-axis:
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)",
title = "Larger penguins tend to have larger bills",
subtitle = "Positive relationship between mass and length is consistent across species") +
theme_bw() +
theme(legend.position = "bottom",
legend.title = element_blank(),
axis.text.y = element_text(size = 14),
axis.text.x = element_text(size = 6))## Warning: Removed 2 rows containing missing values (geom_point).
If you’re tired of explicitly customizing every plot in the same way
all the time, then you should make a custom theme. It’s quite easy to
make a custom theme for ggplot2 and of course there
are an incredible number of ways to customize your theme. In the
code chunk, I modify the theme_bw() theme using the
%+replace% argument to make my new theme named
my_theme() - which is stored as a function:
my_theme <- function () {
# Start with the base font size
theme_bw(base_size = 10) %+replace%
theme(
panel.background = element_blank(),
plot.background = element_rect(fill = "transparent", color = NA),
legend.position = "bottom",
legend.background = element_rect(fill = "transparent", color = NA),
legend.key = element_rect(fill = "transparent", color = NA),
axis.ticks = element_blank(),
panel.grid.major = element_line(color = "grey90", size = 0.3),
panel.grid.minor = element_blank(),
plot.title = element_text(size = 18,
hjust = 0, vjust = 0.5,
face = "bold",
margin = margin(b = 0.2, unit = "cm")),
plot.subtitle = element_text(size = 12, hjust = 0,
vjust = 0.5,
margin = margin(b = 0.2, unit = "cm")),
plot.caption = element_text(size = 7, hjust = 1,
face = "italic",
margin = margin(t = 0.1, unit = "cm")),
axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 13)
)
}Now I can go ahead and my plot from before with this theme:
penguins %>%
ggplot(aes(x = body_mass_g, y = bill_length_mm, color = species)) +
geom_point(alpha = 0.5, size = 2) +
ggthemes::scale_color_colorblind() +
labs(x = "Body Mass (g)", y = "Bill Length (mm)",
title = "Larger penguins tend to have larger bills",
subtitle = "Positive relationship between mass and length is consistent across species") +
my_theme()## Warning: Removed 2 rows containing missing values (geom_point).