knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(ProvidenceThemes) library(ggplot2)
# make fake data for plots ---- points <- data.frame(x = 1:10, y = 1:10) distrb <- data.frame(x = rep(1:6, 10), y = rnorm(60, 0, 1)) # make plots for themes ---- g_points <- ggplot(points, aes(x, y, col = factor(x))) + geom_point(size = 5) g_distrb <- ggplot(distrb, aes(y, fill = factor(x))) + geom_density() + facet_wrap(. ~ x, ncol = 3) g_points_cont <- ggplot(points, aes(x, y, col = x)) + geom_point(size = 5)
ADD SOMETHING HERE!
The colors in the discrete palette are:
scales::show_col(providence_pal)
The colors used to define the continuous palette spectrum are:
scales::show_col(providence_pal_cont)
The colors used in the diverging palette are:
scales::show_col(providence_pal_div)
Consider the following plots:
g_points g_distrb
Fixed-length discrete palettes use the palette's selected colors until they run out, then it uses grey for all additional levels of the aesthetic.
g_points + scale_color_discrete_providence(palette = "providence") g_distrb + scale_fill_discrete_providence(palette = "providence")
Interpolated palettes extend the discrete palette by interpolating additional values.
g_points + scale_color_discrete_providence(palette = "providence", extend = TRUE) g_distrb + scale_fill_discrete_providence(palette = "providence", extend = TRUE)
Opinionated palettes color specific values for "good", "bad", and "neutral" in your dataset.
points$assessment <- "neutral" points$assessment[points$x > 5] <- "good" points$assessment[points$x < 3] <- "bad" ggplot(points, aes(x, y, col = assessment)) + geom_point() + scale_color_opinionated_providence(palette = "providence")
Consider the following plots:
g_points_cont
Diverging palettes grow more extreme at the endpoints. You can specify the midpoint
of where the center should be.
g_points_cont + scale_color_diverging_providence(palette = "providence", midpoint = 5)
Normal continuous scales gradually move between two ends of a spectrum.
g_points_cont + scale_color_continuous_providence(palette = "providence")
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