Getting started with EraBrewer
Getting started with EraBrewer
EraBrewer provides color palettes inspired by Taylor
Swift’s albums. This vignette walks through the two main ways to use
them in a ggplot2 workflow: as a discrete
scale (one color per category) and as a continuous
scale (a smooth gradient over a numeric variable). For each palette we
then pair the canonical scatter plot with a second geom that matches
that scale — a bar chart for the discrete case, and a diverging heatmap
for the continuous case.
library(EraBrewer)
library(ggplot2)
Step 1 — Picking palettes
All available palettes live in the exported list
EraPalettes:
names(EraPalettes)
#> [1] "Red1" "Red2" "NineteenEightyNine"
#> [4] "Showgirl1" "Showgirl2" "SpeakNow1"
#> [7] "SpeakNow2" "TorturedPoet" "Fearless"
#> [10] "Evermore1" "Evermore2" "Reputation"
#> [13] "Lover1" "Lover2" "TaylorSwift"
#> [16] "Midnight1" "Midnight2"
We pick one palette to use discretely and one to use
continuously:
discrete_pal <- era.brewer("Lover2", n = 3)
continuous_pal <- era.brewer("Midnight2", n = 100, type = "continuous")
era.brewer() returns a "palette" object — a
character vector of hex codes with a print() method that
returns a ggplot swatch.
print(era.brewer("Lover2")) + transparent_theme
print(era.brewer("Midnight2")) + transparent_theme
When type = "discrete" (the default whenever
n does not exceed the palette length),
era.brewer() returns the colors hand-picked by the
palette’s curator as the best subset of that size. When
type = "continuous", it interpolates the stored colors with
grDevices::colorRampPalette() to produce as many shades as
you ask for.
Step 2 — A discrete scale (Lover2)
For the discrete case we color by Species in
iris and feed the palette to
scale_color_manual() / scale_fill_manual() via
the values argument.
Scatter plot
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point(size = 2.4, alpha = 0.9) +
scale_color_manual(values = discrete_pal) +
labs(title = "Iris sepals, colored by species",
x = "Sepal length (cm)", y = "Sepal width (cm)")
Bar chart
species_means <- aggregate(Sepal.Length ~ Species, iris, mean)
ggplot(species_means, aes(Species, Sepal.Length, fill = Species)) +
geom_col(width = 0.65) +
scale_fill_manual(values = discrete_pal) +
labs(title = "Mean sepal length by species",
x = NULL, y = "Sepal length (cm)") +
theme(legend.position = "none")
Step 3 — A continuous scale (Midnight2)
Midnight2 runs from a warm red through a neutral gray to
a cool blue, which makes it a natural fit for quantities that are signed
or otherwise diverging around a meaningful center. We use it on a
numeric color aesthetic via scale_color_gradientn() /
scale_fill_gradientn(), passing the interpolated palette
through colors =.
Scatter plot
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Petal.Length)) +
geom_point(size = 2.4, alpha = 0.95) +
scale_color_gradientn(colors = continuous_pal) +
labs(title = "Iris sepals, shaded by petal length",
x = "Sepal length (cm)", y = "Sepal width (cm)",
color = "Petal length")
Diverging heatmap
A correlation matrix is the canonical case for a diverging palette:
the values live in [-x, x], the sign carries meaning, and
zero is the neutral midpoint we want mapped to the palette’s gray
center.
cor_mat <- cor(mtcars)
cor_df <- as.data.frame(as.table(cor_mat))
names(cor_df) <- c("Var1", "Var2", "rho")
ggplot(cor_df, aes(Var1, Var2, fill = rho)) +
geom_tile(color = "white", linewidth = 0.4) +
scale_fill_gradientn(colors = continuous_pal,
limits = c(-1, 1),
breaks = c(-1, -0.5, 0, 0.5, 1)) +
coord_fixed() +
labs(title = "mtcars correlation matrix",
x = NULL, y = NULL, fill = expression(rho)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid = element_blank())
Recap
| Discrete |
scale_color_manual(values = era.brewer("Lover2", n = 3)) |
Bar chart with scale_fill_manual() |
| Continuous |
scale_color_gradientn(colors = era.brewer("Midnight2", n = 100, type = "continuous")) |
Diverging heatmap with
scale_fill_gradientn(limits = c(-1, 1)) |
Notes:
direction = -1 reverses the palette.override_order = TRUE returns the colors in storage
order instead of our preferred order-of-use (only relevant for discrete
palettes).- If you request more colors than a palette holds,
era.brewer() switches to "continuous"
automatically.