Data Visualization in R

Careful inspection of your data is a necessary pre-requisite for any phylogenetic analyses. One of the best ways to inspect your data prior to conducting phylogenetic analyses is to visualize your data on your tree(s). Such visualizations can be key to identifying interesting research questions. Gaining an intuitive and visual appreciation for the patterns in your data can also be key to determining if the outcomes of your analyses are sensible. The best available option for visualizing data on phylogenetic trees is the R statistical computing framework. R offers remarkably flexible plotting options that permit visualization of a wide range of data formats and types.

The figure to the left is from Price et al. (2012) and illustrates reef specialization in haemulid fish. The figure on the right will be generated from the same data using this tutorial.

The figure to the left is from Price et al. (2012) and illustrates reef specialization in haemulid fish. The figure on the right will be generated from the same data using this tutorial.

#############################################################################
#Tutorial on visualization of character data on phylogenetic trees in R.
#The data for this tutorial are derived from Price et al. 2012. Elevated
#rates of morphological and functional diversification in reef-dwelling
#Haemulid fishes. Evolution 67:417-428.
#These data are available via the Dryad data repository: http://datadryad.org/resource/doi:10.5061/dryad.s049s
#Tutorial by R. Glor, March 2014
#############################################################################

##############################
#Preliminaries
##############################
rm(list = ls()) #clear your work environment
setwd("~/Dropbox/Projects/Bodega_Bay/Dryad_Data/") #set working directory. You'll obviously need to change this line to reflect the location of the relevant files on your computer

#load relevant libraries
library(ape)
library(geiger)
library(diversitree)

##############################
#Load tree
##############################
read.nexus("Trees4dryad.nex") -> haemulidTrees #Load set of 500 ultrametric trees obtained from analyses of a multilocus data in BEAST
plot(haemulidTrees[[1]], cex=0.5) #Plot one of our trees to ensure that they loaded properly and are ultrametric. The double square brackets designate the first tree in a set of 500 trees 
lapply(haemulidTrees, ladderize) -> haemulidTreesLadderized #Use the lapply and ladderize functions to ladderize all 500 of our trees. Ladderization is a largely aesthetic change to how the tree is plotted
plot(haemulidTreesLadderized[[1]], cex=0.5) #Plot the first ladderized tree
add.scale.bar() #Add a simple scale bar indicating the scale for the branches in your tree
lapply(haemulidTreesLadderized, rescale, "depth", 1) -> haemulidTreesLadderized1 #Use the lapply and rescale functions to rescale all 500 of our trees to have a root to tip distance of 1. This rescaling will make subsequent plotting functions somewhat easier. Even more importantly, it will often improve the performance of likelihood functions.
plot(haemulidTreesLadderized1[[1]], cex=0.5) #Plot the first ladderized and rescaled tree
add.scale.bar() #Confirm that the tree has been rescaled

#In order to use trees in R we need to learn how trees are stored in R
help(read.tree) #Obtain information on how trees are read and stored in R. Notice the various elements of a tree object included under the Value heading
haemulidTreesLadderized1[[1]]$tip.label #Show list of tip labels
haemulidTreesLadderized1[[1]]$edge #Show list of branches designated by their start and end points
haemulidTreesLadderized1[[1]]$edge.length #Show list of branch lengths
plot(haemulidTreesLadderized1[[1]], label.offset=0.05, cex=0.5) #plot our tree
nodelabels(cex=0.5) #Plot node labels. Confirm that rows in the $edge component correspond with branches in this labeled tree
tiplabels(cex=0.5) #Plot tip labels. Confirm that numbers in the plot correspond with row numbers in the $tip.label component of the tree

##############################
#Load data
##############################
read.csv("haemulidDryadData.csv") -> haemulidDataInput #Load character data for haemulids. This file includes only a subset of the full dataset available via Dryad. Saved in comma delimited text format, this input file should include six columns with taxonomic, habitat, morphological and functional data: genus, species, habitat (reef[0] or non-reef[1]), standard length, raker length, and suction index.
haemulidDataInput #Check out your data
data.frame(haemulidDataInput[,3:6]) -> haemulidData #Convert the habitat, phenotypic, and functional data into a dataframe in R. Dataframes are helpful in R because they store each column of a matrix as a separate object, which will make them easier to call later.
paste(haemulidDataInput[,1], haemulidDataInput[,2], sep="_", collapse=NULL) -> rownames(haemulidData) #Use the paste function to combine the genus and species names in columns 1 and 2 of our data table. The purpose of doing this is to obtain row names for our table that correspond with the names of taxa in our phylogenetic tree, where OTUs are designated as genus_species.
name.check(haemulidTreesLadderized1[[1]], haemulidData) #Use the name.check function of geiger to test if the same taxa are found in the phylogenetic tree and the dataset. You should see that many taxa differ between the two datasets, many apparently due to typographical errors.

read.csv("haemulidDryadData_corrected.csv") -> haemulidDataInput #Load in a new dataset where names in the data matrix have been changed to match those in the tree
data.frame(haemulidDataInput[,3:6]) -> haemulidData
paste(haemulidDataInput[,1], haemulidDataInput[,2], sep="_", collapse=NULL) -> rownames(haemulidData)
name.check(haemulidTreesLadderized1[[1]], haemulidData)
haemulidData[! rownames(haemulidData) %in% name.check(haemulidTreesLadderized1[[1]], haemulidData)$data_not_tree,] -> haemulidData #Delete taxa from the habitat dataset that are not in tree (i.e., all those taxa that are in the $data_not_tree component of the name.check output)
name.check(haemulidTreesLadderized1[[1]], haemulidData) #Confirm that tree and data are now in agreement

##############################
#Plot the data
##############################
plot(haemulidTreesLadderized1[[1]], cex=0.5, label.offset=0.05) #Plot a tree that leaves some room between the tree tips and taxon labels so that we can plot habitat use in this space
haemulidHabitatLabel  haemulidTreesLadderized1 #Reread the new tree file
plot(haemulidTreesLadderized1[[1]], cex=0.5, label.offset=0.05)
tiplabels(cex=0.5) #You should now see that the sequence of taxon labels in your tree object corresponds with the sequence of taxon labels in your plot.

plot(haemulidTreesLadderized1[[1]], cex=0.5, y.lim=c(0,55), x.lim=c(0,3))
points(rep(2, length(haemulidTreesLadderized1[[1]]$tip.label)), 1:length(haemulidTreesLadderized1[[1]]$tip.label), pch=21, bg=haemulidHabitatLabel[match(haemulidTreesLadderized1[[1]]$tip.label, names(haemulidHabitatLabel))], cex=1) #Check to see if the tips are now lined up accordingly
text(2, 52, "Habitat", cex=0.4)
#Add some quantitative data
abline(v=2.1, col="gray")
segments(2.1, 1:nrow(haemulidData), 2.1 + (haemulidData[,2]/1200), 1:nrow(haemulidData))
text(2.02, 0, "Std. Length", pos=4, cex=0.4)
abline(v=2.35, col="gray")
segments(2.35, 1:nrow(haemulidData), 2.35 + (haemulidData[,3]/50), 1:nrow(haemulidData))
text(2.3, 0, "Raker L.", pos=4, cex=0.4)
abline(v=2.6, col="gray")
points(rep(2.6, nrow(haemulidData)), 1:nrow(haemulidData), pch=21, bg="blue", col="white", lwd=0.25, cex=haemulidData[,4]*2.6)
text(2.6, 52, "Suction Index", cex=0.4)

2 thoughts on “Data Visualization in R

  1. André Levy

    Thanks for the tutorial
    In the ##Plot the data## section, isn’t there something missing in the command line:

    haemulidHabitatLabel haemulidTreesLadderized1 #Reread the new tree file

    haemulidHabitatLabel hasn’t been defined yet.

    Thanks

    Reply

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