Getting started with EraBrewer

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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.

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

	Scatter (`color = …`)	Companion geom
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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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