README.md

In jlaffy/brewerland: Generate (RColorbrewerland) Colours For Data Vectors Of Any Type and Length

brewerland

The goal of brewerland is: to make your life colourful

The following data are made availabe:

• discrete_colours : A character vector of 297 discrete RColorBrewer colours.

The following functions are implemented:

• colourScale : Colour Vectors of any length for any data type
• colourPal : Flexile RColorBrewer Palettes
• areColours : Test if character strings are valid colours
• brewerPalNames : RColorBrewr Palette Names
• isBrewerPal : Test if a name is a valid RColorBrewer Palette name
• isBrewerDiv : Test if a name is a divergent RColorBrewer Palette
• brewerPalLength : Maximum length of an RColorBrewer Palette
• brewerPalInfo : Retrieve information on RColorBrewer Palettes

Note: brewerland's two most useful functions are colourPal() and colourScale(). See ?colourPal and ?colourScale for more information.

Installation

You can install the released version of brewerland from GitHub with:

# install.packages("devtools")
devtools::install_github("jlaffy/brewerland")

Examples

Some basic examples which show you how to solve some common problems:

library(brewerland)

# discrete_colours is a brewerland character vector with 297 brewer colours
scales::show_col(discrete_colours, labels = F)

### Choose a palette by type ###
# default palette for continuous data is 'YlOrBr'
scales::show_col(colourPal(name = NULL, type = 'seq'), labels = FALSE)

# default palette for divergent data is 'RdBu' (reversed)
scales::show_col(colourPal(name = NULL, type = 'div'), labels = FALSE) 

# default palette for qualitative/categorical data is 'Dark2'
scales::show_col(colourPal(name = NULL, type = 'qual'), labels = FALSE) 

### Choose a palette by RColorBrewer palette names ###
scales::show_col(colourPal(name = 'Spectral'), labels = FALSE)
scales::show_col(colourPal(name = 'Spectral', reverse = T), labels = FALSE)
scales::show_col(colourPal(name = 'Spectral', reverse = T, shuffle = T), labels = FALSE)


### An Example with continuous (sequential) data ###
set.seed(1492)
toy_numeric_data = sort(rnorm(1:100))

# default colours for sequential data
scales::show_col(colourScale(data = toy_numeric_data), labels = FALSE)

# different RColorBrewer Palette
scales::show_col(colourScale(data = toy_numeric_data, pal = 'RdPu'), labels = F)

# data split into 4 equally-spaced groups
scales::show_col(colourScale(data = toy_numeric_data, pal = 'RdPu', bin = T, n = 4), labels = F) # data binned into 4 groups

# data split into 4 equally-sized groups
scales::show_col(colourScale(data = toy_numeric_data, pal = 'RdPu', quantile = T, n = 4), labels = F) # data binned into 4 groups

# neutral colour for NA values. If you want a neutral colour for a subset of points, set these equal to 'NA'.
toy_numeric_data[toy_numeric_data >= -0.5 & toy_numeric_data <= 0.5] <- NA
scales::show_col(colourScale(data = toy_numeric_data, na.colour = "#808080"), labels = F)

# semi transparent colours
scales::show_col(colourScale(data = toy_numeric_data, alpha = 0.5), labels = F)


### An Example with qualitative data ###
toy_categorical_data = sort(rep(letters[1:5], 10))
scales::show_col(colourScale(data = toy_categorical_data), labels = FALSE)

# choose levels
scales::show_col(colourScale(data = toy_categorical_data, levels = letters[5:1]), labels = FALSE)

# provide a different colour palette
scales::show_col(colourScale(data = toy_categorical_data, levels = letters[5:1], pal = discrete_colours))

# return colour key for qualitative colours
colourScale(data = toy_categorical_data, levels = letters[5:1], pal = discrete_colours, return.legend = T)$legend

jlaffy/brewerland documentation built on June 27, 2019, 3:36 a.m.