I have two lists of different sizes. One list (named * trees * ) is composed of phylogenetic trees (class phylo) and the second list (named * data_values*) is composed of numeric values. The tips names of each phylogenetic tree of the list * tree* match with the names of each element inside of the list of values. But the list data_values is composed of a greater number of elements than the tips of each tree.
library(phytools)
library(ape)
#original tree:
tree_original = rtree(12, tip.label = paste0("species", LETTERS[1:12]))
##list of trees:
nodes = 14:23
trees = lapply(nodes,extract.clade,phy=tree_orignal)
names(trees) <- paste0("", 14:23)
data_values <- list()
for (i in 1:17) { data_values[[paste0('species', LETTERS[i])]] <- round(rnorm(10, 5, 4), 1) }
I would like to match both lists (trees and data_values) using species as an index to have a data frame for each tree (see example below). I can do this operation for each tree of the list trees individually but, as my list of species is much bigger than this example, I would like to know if I can do this operation (below) for the all list of trees and not run tree by tree, like this:
tree14 = data_values[match(trees$`14`$tip.label, names(data_values))]
tree14 = llply(tree14, function(x) sapply(x, as.numeric))
tree14_df = ldply(tree14, .fun=identity) **I will need each result as a data.frame**
.id 1 2 3 4 5 6 7 8 9 10
1 speciesE -0.5 3.4 2.0 5.3 3.7 8.2 3.5 -2.0 3.1 10.2
2 speciesL 6.8 4.3 7.1 5.5 4.9 2.5 0.3 -3.8 4.1 6.4
3 speciesA 2.5 2.5 9.6 10.6 2.2 7.1 4.1 4.4 6.0 6.7
4 speciesI -3.5 7.2 6.8 2.8 7.5 8.9 13.4 13.1 1.8 5.5
5 speciesC 4.3 2.2 10.0 7.4 4.4 8.3 -0.7 3.6 9.2 6.3
6 speciesH 6.3 6.1 2.2 4.6 7.4 7.3 2.9 0.6 3.0 5.2
7 speciesB 8.3 1.7 -0.1 4.5 9.4 -0.2 7.5 1.4 -0.3 4.6
8 speciesD 6.2 5.8 6.6 1.1 5.4 11.1 -1.1 0.0 7.9 0.4
9 speciesG 3.5 2.8 1.4 11.6 -2.8 11.0 3.5 2.8 3.1 4.8
10 speciesK 0.9 4.9 5.4 2.7 -0.7 5.1 18.3 4.9 2.5 -0.7
tree15 = data_values[match(trees$`15`$tip.label, names(data_values))]
tree15 = llply(tree15, function(x) sapply(x, as.numeric))
tree15_df = ldply(tree15, .fun=identity)
.id 1 2 3 4 5 6 7 8 9 10
1 speciesE -0.5 3.4 2.0 5.3 3.7 8.2 3.5 -2.0 3.1 10.2
2 speciesL 6.8 4.3 7.1 5.5 4.9 2.5 0.3 -3.8 4.1 6.4
3 speciesA 2.5 2.5 9.6 10.6 2.2 7.1 4.1 4.4 6.0 6.7
4 speciesI -3.5 7.2 6.8 2.8 7.5 8.9 13.4 13.1 1.8 5.5
5 speciesC 4.3 2.2 10.0 7.4 4.4 8.3 -0.7 3.6 9.2 6.3
6 speciesH 6.3 6.1 2.2 4.6 7.4 7.3 2.9 0.6 3.0 5.2
7 speciesB 8.3 1.7 -0.1 4.5 9.4 -0.2 7.5 1.4 -0.3 4.6
... this operation goes until tree23