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Color Palettes from Node.js Colormap module.

This is an R package that allows you to generate colors from color palettes defined in Node.js's colormap module. In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors.

There are also scale functions that can be used with ggplot2.

Changelog

2016-11-15 0.1.4 With Viridis as default theme.
2016-10-21 0.1.3 Now on CRAN.
2016-09-06 Ability to generate a custom palette.
2016-08-30 Input Validation and ggplot2 scales.
2016-08-29 First Release.

Credits

The colormap Node.js module which does all the heavylifting.
The V8 package which allows R code to call Javascript code.
Bob Rudis's zoneparser package which I used as a skeleton for this pacakge.
Simon Garnier's viridis package for ggplot2 scale functions.

Installation

Requires V8

if(!require("V8")) install.packages("V8")
if(!require("devtools")) install.packages("devtools")
devtools::install_github("bhaskarvk/colormap")

Usage

The main function is colormap which takes 5 optional arguments

colormap: A string representing one of the 44 built-in colormaps.You can use the colormaps list to specify a value. e.g. colormaps$density OR A vector of colors in hex e.g. c('#000000','#777777','#FFFFFF') OR A list of list e.g. list(list(index=0,rgb=c(255,255,255)),list(index=1,rgb=c(255,0,0)))
nshades: Number of colors to generate.
format: one of 'hex', 'rgb', 'rgbaString'
alpha: Between 0 & 1 to specify the transparency.
reverse: Boolean. Whether to reverse the order of the colors returned or not.

Example

library(colormap)

# Defaults to 72 colors from the 'viridis' palette.
scales::show_col(colormap(), labels = F)

# Specify a different palette from a list of pre-defined palette.
scales::show_col(colormap(colormap=colormaps$temperature, nshades=20))

# Specify opacity value.
scales::show_col(colormap(colormap=colormaps$temperature, nshades=20, alpha=0.7))

# Specify colormap as vector of colors.
scales::show_col(colormap(colormap=c('#FFFFFF','#FF0000'),nshades = 12))

# Specify colormap as list of lists.
scales::show_col(colormap(colormap=list(list(index=0,rgb=c(0,255,0)),
                                        list(index=1,rgb=c(255,0,255))),
                          nshades=12, alpha=0.65))

You can also get the colors in a 'rgb' matrix and a rgba string vector format

colormap(format='rgb',nshades=5) # As rgb
#>      [,1] [,2] [,3] [,4]
#> [1,]   68    1   84    1
#> [2,]   59   81  139    1
#> [3,]   33  144  141    1
#> [4,]   92  200   99    1
#> [5,]  253  231   37    1

colormap(format='rgbaString',nshades=10) # As rgba string
#>  [1] "rgba(68,1,84,1)"    "rgba(71,39,117,1)"  "rgba(62,72,135,1)" 
#>  [4] "rgba(49,102,141,1)" "rgba(38,130,141,1)" "rgba(36,157,136,1)"
#>  [7] "rgba(55,181,120,1)" "rgba(109,204,88,1)" "rgba(176,221,49,1)"
#> [10] "rgba(253,231,37,1)"

You also get scale_fill_colormap and scale_color_colormap functions for using these palettes in ggplot2 plots. Check ?colormap::scale_fill_colormap for details.

ensureCranPkg <- function(pkg) {
  if(!suppressWarnings(requireNamespace(pkg, quietly = TRUE))) {
    install.packages(pkg)
  }
}

ensureCranPkg('ggplot2')

library(ggplot2)

# Continuous color scale
ggplot(mtcars,aes(x=wt,y=mpg)) + geom_point(aes(color=hp)) +
  theme_minimal() +
  scale_color_colormap('Horse Power',
                       discrete = F,colormap = colormaps$viridis, reverse = T)

ggplot(mtcars,aes(x=wt,y=mpg)) + geom_point(aes(color=as.factor(cyl))) +
  theme_minimal() +
  scale_color_colormap('Cylinder',
                       discrete = T,colormap = colormaps$warm, reverse = T)

Here are two choroplethes using scale_fill_colormap.

ensureCranPkg('maptools')
ensureCranPkg('scales')
ensureCranPkg('ggplot2')
ensureCranPkg('ggthemes')
ensureCranPkg('devtools')

if(!suppressWarnings(requireNamespace('albersusa', quietly = TRUE))) {
  devtools::install_github('hrbrmstr/albersusa')
}

library(maptools)
#> Loading required package: sp
#> Checking rgeos availability: TRUE
library(scales)
library(ggplot2)
library(albersusa)
library(ggthemes)
library(colormap)

us <- usa_composite()
us_map <- fortify(us, region="fips_state")

gg_usa <- ggplot(us@data, aes(map_id=fips_state,fill=pop_2014)) +
  geom_map(map=us_map, color='#ffffff', size=0.1) + 
  expand_limits(x=us_map$long,y=us_map$lat) +
  theme_map() +  
  theme(legend.position="right") 

gg_usa +
  coord_map("albers", lat0=30, lat1=40) +
  scale_fill_colormap("State Population\n(2014 Estimates)", labels=comma,
                      colormap = colormaps$copper, reverse = T, discrete = F)

counties <- counties_composite()

counties_map <- fortify(counties, region="fips")

gg_counties <- ggplot(counties@data, 
                      aes(map_id=fips,fill=census_area)) +
  geom_map(map=counties_map, color='#ffffff', size=0.1) + 
  expand_limits(x=counties_map$long,y=counties_map$lat) +
  theme_map() +  
  theme(legend.position="right") 

gg_counties +
  coord_map("albers", lat0=30, lat1=40) +
  scale_fill_colormap("County Area", labels=comma, trans = 'log10',
                      colormap = colormaps$freesurface_red, reverse = T, discrete = F) +
  theme(#panel.border = element_rect(colour = "black", fill=NA, size=1),
        legend.position = 'bottom', legend.direction = "horizontal")

Here is a plot showing all 44 pre-defined color palettes and the colors they generate.

ensureCranPkg('purrr')
par(mfrow=c(44,1))
par(mar=rep(0.25,4))
purrr::walk(colormaps, function(x) { 
  barplot(rep(1,72), yaxt="n", space=c(0,0),border=NA,
          col=colormap(colormap=x), main = sprintf("\n\n%s",x))
  })