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I'd like to join multiple vectors across separate lists and output a single list of matrices. The idea is that all items of the list with the same name, for example all the a items, are joined by rows as a matrix. The added complication is that these vectors can be of different lengths, so rbind is not straightforward to implement; the missing values in the matrix can be appended with NAs.
Input lists:
list1 <- list(a = 1:5, b = 6:10, c = 11:15)
list2 <- list(a = 1:4, b = 6:9, c = 11:14)
list3 <- list(a = 1:3, b = 6:8, c = 11:13)
list1
# $a
# [1] 1 2 3 4 5
#
# $b
# [1] 6 7 8 9 10
#
# $c
# [1] 11 12 13 14 15
#
The desired output I'm hoping to obtain is a list with as many matrices as there are unique list items, where each matrix consists of the vectors of differing lengths bound by rows:
# $a
# [,1] [,2] [,3] [,4] [,5]
# [1,] 1 2 3 4 5
# [2,] 1 2 3 4 NA
# [3,] 1 2 3 NA NA
#
# $b
# [,1] [,2] [,3] [,4] [,5]
# [1,] 6 7 8 9 10
# [2,] 6 7 8 9 NA
# [3,] 6 7 8 NA NA
#
# $c
# [,1] [,2] [,3] [,4] [,5]
# [1,] 11 12 13 14 15
# [2,] 11 12 13 14 NA
# [3,] 11 12 13 NA NA
How would I go about writing a function that does this that also scales up to merging longer lists with vectors of varying lengths?
480
Use do.call (rbind,...) You may also be interested in the rbind.fill.matrix () function from the "plyr" package, which will also let you bind matrices with differing columns, filling in with NA where necessary. Show activity on this post. Another option, if you have data.frame and not matrix , is to use rbindlist from data.table package.
The following R code explains how to construct a matrix containing our two vectors. For this, we can use the cbind function: Note that all variables of matrices need to have the same data class. For that reason our numeric vector was automatically converted to the character class.
The second vector consists of ten character elements. Note that both vectors have the same length (i.e. 10). This is important if we want to merge our two vectors in a data frame or matrix later on. This example shows how to create a data frame based on our two vectors using the data.frame function:
It consists of ten numeric values ranging from 1 to 10. Let’s create a second vector object: The second vector consists of ten character elements. Note that both vectors have the same length (i.e. 10).
One option is to transpose the list of lists, then reduce the list elements to a single dataset with cbind.fill, get the transpose (t) and assign the row names to NULL
library(tidyverse)
library(rowr)
list(list1, list2, list3) %>%
transpose %>%
map(~ reduce(.x, cbind.fill, fill = NA) %>%
t %>%
`row.names<-`(NULL))
#$a
# [,1] [,2] [,3] [,4] [,5]
#[1,] 1 2 3 4 5
#[2,] 1 2 3 4 NA
#[3,] 1 2 3 NA NA
#$b
# [,1] [,2] [,3] [,4] [,5]
#[1,] 6 7 8 9 10
#[2,] 6 7 8 9 NA
#[3,] 6 7 8 NA NA
#$c
# [,1] [,2] [,3] [,4] [,5]
#[1,] 11 12 13 14 15
#[2,] 11 12 13 14 NA
#[3,] 11 12 13 NA NA
Or using base R
do.call(Map, c(f = function(...) {l1 <- list(...)
do.call(rbind, lapply(l1, `length<-`, max(lengths(l1))))},
mget(paste0("list", 1:3))))
a data.table solution, just for fun:
plouf <- list(list1,list2,list3)
lapply(names(list1),function(name){
lapply(plouf,function(x){
as.data.table(t(x[[name]]))
}) %>%
rbindlist(.,fill =T) %>%
`colnames<-`(NULL)
}) %>% setNames(names(list1))
$a
1: 1 2 3 4 5
2: 1 2 3 4 NA
3: 1 2 3 NA NA
$b
1: 6 7 8 9 10
2: 6 7 8 9 NA
3: 6 7 8 NA NA
$c
1: 11 12 13 14 15
2: 11 12 13 14 NA
3: 11 12 13 NA NA
the first loop is on the list name. The second loop loop is on the list of list, and extract the element of each list, transpose it into a data.table with unique row, to be able to use rbindlist which can fill missing columns.
without data.table, so similar but less good than what akrun proposed:
library(plyr)
lapply(names(list1),function(name){
lapply(plouf,function(x){
t(x[[name]])%>%
as.data.frame
}) %>%
rbind.fill %>%
`colnames<-`(NULL)
}) %>% setNames(names(list1))
You may use 1. rapply to adjust the lengths of the sublists, and 2. t(mapply) to get the matrices by selecting with '[['.
listn <- list(list1, list2, list3)
setNames(lapply(seq(listn), function(x)
t(mapply(`[[`, rapply(listn, `length<-`, value=5, how="list"), x))), names(el(listn)))
# $a
# [,1] [,2] [,3] [,4] [,5]
# [1,] 1 2 3 4 5
# [2,] 1 2 3 4 NA
# [3,] 1 2 3 NA NA
#
# $b
# [,1] [,2] [,3] [,4] [,5]
# [1,] 6 7 8 9 10
# [2,] 6 7 8 9 NA
# [3,] 6 7 8 NA NA
#
# $c
# [,1] [,2] [,3] [,4] [,5]
# [1,] 11 12 13 14 15
# [2,] 11 12 13 14 NA
# [3,] 11 12 13 NA NA
In case the lengths are unknown use this code:
max(rapply(listn, length))
# [1] 5
Just a doodle of mine:
library(magrittr)
list(list1, list2, list3) %>%
do.call("rbind", .) %>%
as.data.frame() %>%
sapply(., function(x) lapply(x, `length<-`, max(lengths(x)))) %>%
apply(., 2, as.list) %>%
lapply(., function(x) do.call(rbind, x))
# $a
# [,1] [,2] [,3] [,4] [,5]
# [1,] 1 2 3 4 5
# [2,] 1 2 3 4 NA
# [3,] 1 2 3 NA NA
#
# $b
# [,1] [,2] [,3] [,4] [,5]
# [1,] 6 7 8 9 10
# [2,] 6 7 8 9 NA
# [3,] 6 7 8 NA NA
#
# $c
# [,1] [,2] [,3] [,4] [,5]
# [1,] 11 12 13 14 15
# [2,] 11 12 13 14 NA
# [3,] 11 12 13 NA NA
Using base R, we can concatenate all the lists together at same level (list_df). Loop through unique names in list_df and subset them and create a list of matrices of similar named elements.
list_df <- c(list1, list2, list3)
lapply(unique(names(list_df)), function(x) {
temp <- list_df[names(list_df) == x]
t(sapply(temp, `[`, seq_len(max(lengths(temp)))))
})
#[[1]]
# [,1] [,2] [,3] [,4] [,5]
#a 1 2 3 4 5
#a 1 2 3 4 NA
#a 1 2 3 NA NA
#[[2]]
# [,1] [,2] [,3] [,4] [,5]
#b 6 7 8 9 10
#b 6 7 8 9 NA
#b 6 7 8 NA NA
#[[3]]
# [,1] [,2] [,3] [,4] [,5]
#c 11 12 13 14 15
#c 11 12 13 14 NA
#c 11 12 13 NA NA
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