Creating color palettes in R

In the R post, we will present how to create your own color palettes and how to work with other palettes such as RColorBrewer, wesanderson and hex codes from for exciting color palettes.

There are several color palettes available in R such as rainbow(), heat.colors(), terrain.colors(), and topo.colors(). We can visualize these  as 3D pie charts using the plotrix R package.

# Let's create a pie chart with n=7 colors using each palette
sliceValues <- rep(10, 7) # each slice value=10 for proportionate slices
pie3D(sliceValues,explode=0, theta=1.2, col=rainbow(n=7), main="rainbow()")

# Let's create a figure with all 4 base color palettes
par(mfrow=c(1, 4))
pie3D(sliceValues,explode=0, theta=1.2, col=rainbow(n=7), main="rainbow()")
pie3D(sliceValues,explode=0, theta=1.2, col=heat.colors(n=7), main="heat.colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=terrain.colors(n=7), main="terrain.colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=topo.colors(n=7), main="topo.colors()")

Screen Shot 2016-07-10 at 9.01.30 AM

Other popular color palettes include the RColorBrewer package that has a variety of sequential, divergent and qualitative palettes and the wesanderson package that has color palettes derived from his films.


# To see all palettes available in this package
par(mfrow=c(1, 1))

# To create pie charts from a sequential, divergent and qualitative RColorBrewer palette
par(mfrow=c(1, 4))
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "RdPu"), main="Sequential RdPu")
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "RdGy"), main="Divergent RdGy")
pie3D(sliceValues,explode=0, theta=1.2, col=brewer.pal(7, "Set1"), main="Qualitative Set1")

# And add pie chart with a wes_anderson palette
# we will only use 5 slices in the example since the Darjeeling palette only has 5 colors
pie3D(sliceValues[1:5],explode=0, theta=1.2, col=wes_palette("Darjeeling2"), main="Darjeeling2")

Screen Shot 2016-07-10 at 9.01.45 AMYou can also create your own color palettes in R with your colors of choice with the colors() function or creating a vector with the color names. A great review and cheat sheet for R colors can be found at

# To get an idea of the colors available
length(colors()) # 657

# To see all 657 colors as a color chart you can source the R script to generate a pdf version in your working directory

Screen Shot 2016-07-09 at 5.18.32 PM

# We can create choose a palette based on the R chart as follow:
mycols <- colors()[c(8, 5, 30, 53, 118, 72)] #
# or you could enter the color names directly
# mycols <- c("aquamarine", "antiquewhite2", "blue4", "chocolate1", "deeppink2", "cyan4")

# You could also get and store all distinct colors in the cl object and use the sample function to select colors at random
cl <- colors(distinct = TRUE)
set.seed(15887) # to set random generator seed
mycols2 <- sample(cl, 7)

You can also create color palettes with hex color codes. A great example of this is to work with popular color palettes available on the website. This website has various palettes you can choose from and even derive color palettes from your favorite websites. For example, let’s grab the color palette from the website at .

Screen Shot 2016-07-09 at 5.36.02 PM

After entering the URL of our website, we will receive the hex codes for the color scheme used on the website.

Screen Shot 2016-07-09 at 5.38.34 PM

We can even export the colors as little pencils 🙂


You can also choose from hundred of color schemes based on your color of choice. For example, we will also create a color palette based on the color olive – ColorCombo382.


# For the color palette
mycols3 <- c("#c6d4e1", "#2f2016", "#fcfaea", "#456789")

# For ColorCombos382 palette
mycols4 <- c("#C3D938", "#772877", "#7C821E", "#D8B98B", "#7A4012")

# Now to get the pie charts for the last four palettes
pie3D(sliceValues,explode=0, theta=1.2, col=mycols, main="colors()")
pie3D(sliceValues,explode=0, theta=1.2, col=mycols2, main="sample(colors(distinct=TRUE)")
pie3D(sliceValues[1:4],explode=0, theta=1.2, col=mycols3, main=" color grab")
pie3D(sliceValues[1:5],explode=0, theta=1.2, col=mycols4, main="ColorCombos382")

Screen Shot 2016-07-10 at 9.01.56 AM

We can also create a color palette with the colorRampPalette() to use for heatmaps and other plots. For this example, we will use the leukemia dataset available in the GSVAdata package, which corresponds to microarray data from 37 human acute leukemias where 20 of these cases are Acute lymphoblastic leukemia (ALL) and the other 17 are ALL’s with Mixed leukemia gene rearrangements. For more information on the study please see Armstrong et al. Nat Genet 30:41-47, 2002.

data(leukemia) # loads leukemia_eset

# Create a matrix from the gene expression eset object
M1 <- exprs(leukemia_eset)

# Get a matrix of the top 50 most variable probes accros the samples
topVarGenes <- head(order(-rowVars(M1)), 50)
mat <- M1[ topVarGenes, ]
mat <- mat - rowMeans(mat)

# For sample annotation information

# Get sample group as a factor the ColSideColors
ALLgroup <- as.factor(pData(leukemia_eset)[colnames(M1), 1])

# Get the colors for the ALL subtype
sidecols <- c("#4FD5D6", "#FF0000")

# Here is a fancy color palette inspired by
cool = rainbow(50, start=rgb2hsv(col2rgb('cyan'))[1], end=rgb2hsv(col2rgb('blue'))[1])
warm = rainbow(50, start=rgb2hsv(col2rgb('red'))[1], end=rgb2hsv(col2rgb('yellow'))[1])
cols = c(rev(cool), rev(warm))
mypalette <- colorRampPalette(cols)(255)

library("gplots") # for the heatmap.2 function

png(filename="Heatmap_Example.png", width=12, height=10, units = 'in', res = 300)
heatmap.2(mat, trace="none", col=mypalette, ColSideColors=sidecols[ALLgroup],
labRow=FALSE, labCol=FALSE, mar=c(6,12), scale="row", key.title="")
legend("topright", legend=levels(ALLgroup), fill=sidecols, title="", cex=1.2)


Now you are all set to work with and create your own awesome color palettes! Happy R programing 🙂


Working with Venn Diagrams

In this post, we will learn how to create venn diagrams for gene lists and how to retrieve the genes present in each venn compartment with R.

In this post, we will learn how to create venn diagrams for gene lists and how to retrieve the genes present in each venn compartment with R.

In this particular example, we will generate random gene lists using the molbiotools gene set generator but you can use your own gene lists if you prefer. Specifically, we will generate a random list of 257 genes to represent those that are upregulated in condition and another list of 1570 genes to represent those that are upregulated in condition B.

Screen Shot 2016-06-21 at 2.01.15 PM

Then, we will sort and paste the gene lists in an excel document we will save as randomGeneLists.xlsx.

Now, let’s load the data into R using the gdata package.

geneLists <- read.xls("randomGeneLists.xlsx", sheet=1, stringsAsFactors=FALSE, header=FALSE)

# Notice there are empty strings to complete the data frame in column 1 (V1)

# To convert this data frame to separate gene lists with the empty strings removed we can use lapply() with our home made  function(x) x[x != ""]
geneLS <- lapply(as.list(geneLists), function(x) x[x != ""])

# If this is a bit confusing you can also write a function and then use it in lapply() 
removeEMPTYstrings <- function(x) {

 newVectorWOstrings <- x[x != ""]

geneLS2 <- lapply(as.list(geneLists), removeEMPTYstrings)

# You can print the last 6 entries of each vector stored in your list, as follows:
lapply(geneLS, tail)
lapply(geneLS2, tail) # Both methods return the same results

# We can rename our list vectors
names(geneLS) <- c("ConditionA", "ConditionB")

# Now we can plot a Venn diagram with the VennDiagram R package, as follows:

venn.plot <- venn.diagram(VENN.LIST , NULL, fill=c("darkmagenta", "darkblue"), alpha=c(0.5,0.5), cex = 2, cat.fontface=4, category.names=c("A", "B"), main="Random Gene Lists")

# To plot the venn diagram we will use the grid.draw() function to plot the venn diagram

# To get the list of gene present in each Venn compartment we can use the gplots package

a <- venn(VENN.LIST, show.plot=FALSE)

# You can inspect the contents of this object with the str() function

# By inspecting the structure of the a object created, 
# you notice two attributes: 1) dimnames 2) intersections
# We can store the intersections in a new object named inters
inters <- attr(a,"intersections")

# We can summarize the contents of each venn compartment, as follows:
# in 1) ConditionA only, 2) ConditionB only, 3) ConditionA & ConditionB
lapply(inters, head) 


Now you are ready, to review the genes in each section of the venn diagram separately. Alternatively, you can always use Venny web tool that is a great way to start looking at your data and then write a modified version of this R script to make a more exhaustive figure or facilitate downstream analysis in your script.

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