The scale_colour_brewer() is an R function from the ggplot2 package that provides sequential, diverging, and qualitative color schemes from ColorBrewer. These are particularly well suited to display discrete values on a map.
scale_colour_brewer( ..., type = "seq", palette = 1, direction = 1, aesthetics = "colour" )
type: It is one of “seq” (sequential), “div” (diverging), or “qual” (qualitative)
palette: If a string will use that named palette. If a number will index into the list of palettes of the appropriate type. The list of available palettes can be found in the Palettes section.
direction: It sets the order of colors in the scale. If 1, the default colors are as output by RColorBrewer::brewer.pal(). If -1, the order of colors is reversed.
aesthetics: The character string or vector of character strings listing the name(s) of the aesthetic(s) that this scale works with.
…: Other arguments.
library(ggplot2) ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(cyl))) + geom_point() + scale_colour_brewer(palette = "Set1")
In this code example, we created a scatter plot of mpg vs wt from the mtcars dataset. The points are colored based on the number of cylinders (cyl) using the scale_colour_brewer() function with the Set1 palette.
library(ggplot2) # Create a sample data frame data <- data.frame(x = 1:5, y = 2:6, group = c("A", "B", "C", "D", "E")) # Create a scatterplot with different colors for each group using the # RdPu palette ggplot(data, aes(x, y, color = group)) + geom_point() + scale_colour_brewer(type = "qual", palette = "RdPu")
In this example, we created a simple data frame with x and y variables and a categorical grouping variable called “group”.
In the next step, we created a scatterplot with points colored by group using the geom_point() and aes() functions.
Finally, we use scale_colour_brewer() to specify that we want to use a qualitative palette called “RdPu” from the ColorBrewer website.
The type argument is set to “qual” to indicate we want to use a qualitative palette.
Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.