A package that facilitates nice colors created for the Buenrostro Lab. The base of the package including some divergent color schemes were taken from the wesanderson package. This README provides a quick overview of how to use the color schemes with ggplot2 and which palettes are available.
devtools::install_github("caleblareau/BuenColors")library(BuenColors)With the library loaded, just type this to get either the continuous or discrete (by default) palette printed in your plot console.
jdb_palette("FantasticFox")
jdb_palette("aqua_brick", type = "continuous")
#> [1] "algae_earth" "aqua_brick" "aqua_tan" "berry"
#> [5] "blue_cyan" "BottleRocket" "BottleRocket2" "brewer_blue"
#> [9] "brewer_celsius" "brewer_fire" "brewer_green" "brewer_heat"
#> [13] "brewer_jamaica" "brewer_marine" "brewer_orange" "brewer_purple"
#> [17] "brewer_red" "brewer_spectra" "brewer_violet" "brewer_yes"
#> [21] "calma_azules" "calma_bosque" "calma_manudo" "calma_marino"
#> [25] "calma_morado" "calma_musgos" "Cavalcanti" "Chevalier"
#> [29] "china_basics" "china_dragon" "china_novice" "china_ranges"
#> [33] "china_sunset" "china_theory" "china_weirdo" "citric"
#> [37] "citric_yellow" "citrus" "corona" "cyan_brick"
#> [41] "cyan_green" "cyan_pink" "cyan_purple" "cyan_tan"
#> [45] "cyan_violet" "Darjeeling" "Darjeeling2" "dark_blue"
#> [49] "dark_citrus" "dark_cyan" "dark_violet" "dusk_dawn"
#> [53] "FantasticFox" "flame_artic" "flame_blind" "flame_flame"
#> [57] "flame_light" "flame_macaw" "flame_polar" "flame_volts"
#> [61] "flame_watts" "flame_weird" "flame_wings" "forest"
#> [65] "forest_citric" "forest_yellow" "GrandBudapest" "GrandBudapest2"
#> [69] "horizon" "horizon_extra" "lawhoops" "Moonrise1"
#> [73] "Moonrise2" "Moonrise3" "ocean_aqua" "ocean_brick"
#> [77] "ocean_citrus" "ocean_earth" "ocean_green" "ocean_pink"
#> [81] "ocean_red" "ocean_teal" "purple_baby" "purple_pink"
#> [85] "Royal1" "Royal2" "Rushmore" "samba_color"
#> [89] "samba_light" "samba_night" "solar_basic" "solar_blues"
#> [93] "solar_extra" "solar_flare" "solar_glare" "solar_rojos"
#> [97] "teal_orange" "teal_violet" "white_grove" "white_jungle"
#> [101] "white_mango" "white_orange" "white_tango" "wolfgang_basic"
#> [105] "wolfgang_extra" "Zissou"
To keep consistent color designations, one can use the color_map function to link features to their specific hex color annoations. For example,
jdb_color_map(c("HSC"))
#> [1] "#00441B"returns the hex code associated with HSC in the Buenrostro Lab paradigm. This function may be applied over multiple features--
jdb_color_map(c("HSC", "CMP", "HSC"))
#> [1] "#00441B" "#FFC179" "#00441B"and will error out when a feature is not recognized--
jdb_color_map(c("WHAT"))
#> Error: all(name %in% names(jdb_color_maps)) is not TRUE #> [1] "B" "CD4" "CD8" "CLP" "CMP" "Ery" "GMP" "GMP-A" "GMP-B"
#> [10] "GMP-C" "HSC" "LMPP" "MEP" "mono" "MPP" "NK" "pDC"
To coordinate a ggplot feature (e.g. data point in a scatter plot) with a particular color, this post was a life-saver. Specifically, we'll use a named vector to coordinate the discrete values. Here's an example--
xy <- 1:7
cell <- c("GMP-A", "Ery", "CD4", "Ery", "LMPP", "ERY", "MEP")
df <- data.frame(
xy = xy,
cell = cell, stringsAsFactors = FALSE
)
ggplot(df, aes(x = xy, y = xy, color = cell)) +
geom_point(size = 10) + pretty_plot() +
scale_color_manual(values = jdb_color_maps)
#> Warning: Removed 1 rows containing missing values (geom_point).
P.S.-- this call will return a blank color for features that are not found in the color map ("ERY" in this example).
The above ggplot command works because jdb_color_maps (with an 's') exists as a named vector in the BuenColors NAMESPACE. The provided function (jdb_color_map) does not have an 's' by the way. This same syntax of supplying a named vector should work for all discrete color scale functionalities in ggplot.
The trick here is to use scale_color_manual like it is shown here--
library(ggplot2)
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() + pretty_plot() +
scale_color_manual(values = jdb_palette("brewer_spectra"))
The trick here is to use scale_color_gradientn like it is shown here--
df <- data.frame(x = rnorm(1000), y = 0)
ggplot(df, aes(x=x, y=y, colour=x)) + geom_point() +
scale_color_gradientn(colors = jdb_palette("flame_light")) +
pretty_plot()
Best way that I've found to make the density color function represented in the points. Thanks to Kamil Slowikowski for figuring this out.
dat <- data.frame(
x = c(
rnorm(1e4, mean = 0, sd = 0.1),
rnorm(1e3, mean = 0, sd = 0.1)
),
y = c(
rnorm(1e4, mean = 0, sd = 0.1),
rnorm(1e3, mean = 0.1, sd = 0.2)
)
)
dat$density <- get_density(dat$x, dat$y)
ggplot2::ggplot(dat) + geom_point(aes(x, y, color = density)) +
scale_color_gradientn(colors = jdb_palette("solar_extra")) +
pretty_plot()
Quick wrapper using shuf to change the order of plotting points (to a random presentation) to avoid hiding effects.
tdf<-paste(system.file('rds',package='BuenColors'),'basicTSNE.rds',sep='/')
df <- readRDS(tdf)
ggplot(shuf(df)) + geom_point(aes(X1, X2, color = counts)) +
scale_color_gradientn(colors = jdb_palette("solar_extra")) +
pretty_plot()
Here's what each palette looks like on a continuous scale.
Here are the discrete color units that go into each scale. If n is small and discrete, note that the colors that are selected are read from left to right.
