Rename columns in nested lists and row bind - r

Question

I've a nested list of objects that I'd like to first rename some variables and row bind its object, but selecting only some variables.
In the example below, I'd like to rename columns A to a in the second object, and w to x in the third object to, then row bind all three object selecting only columns a and x using.
Data:
df <- list(structure(list(a = 1:3,
x = c(-1.99, -1.11, -0.34),
y = c("C", "B", "A")), .Names = c("a", "x", "y"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -3L)), structure(list(a = 1:3, x = c(-0.44, -1.07, -0.23)), .Names = c("A", "x"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -3L)),
structure(list(a = 1:3, x = c(-0.62, -0.60, -0.06),
y = c(3L, 2L, 1L)), .Names = c("a", "w", "y"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -3L)))
List structure:
> lapply(df, names)
[[1]]
[1] "a" "x" "y"
[[2]]
[1] "A" "x"
[[3]]
[1] "a" "w" "y"
Then, row binding then:
library(plyr)
df2 <- ldply(df, data.frame)

Answer 1

using purrr (map), dplyr(rename,select,bind_rows,%>%) and magrittr (%<>%,%>%) ):
library(purrr)
library(dplyr)
library(magrittr)
df[[2]] %<>% rename(.,a = A)
df[[3]] %<>% rename(.,x = w)
df %>% map_df(. %>% select("a","x"))
# # A tibble: 9 x 2
# a x
# <int> <dbl>
# 1 1 -1.99
# 2 2 -1.11
# 3 3 -0.34
# 4 1 -0.44
# 5 2 -1.07
# 6 3 -0.23
# 7 1 -0.62
# 8 2 -0.60
# 9 3 -0.06
Or in base R:
names(df[[2]])[names(df[[2]]) == "A"] <- "a"
names(df[[3]])[names(df[[3]]) == "w"] <- "x"
do.call(rbind,lapply(df,"[",c("a","x")))

Answer 2

You could achieve that with:
library(plyr)
df = lapply(df, function(x) {plyr::rename(x,c("A"="a","w"="x"),warn_missing = F)})
df2 <- ldply(lapply(df, function(x) {x[,c("a","x")]}), data.frame)
Output:
a x
1 1 -1.99
2 2 -1.11
3 3 -0.34
4 1 -0.44
5 2 -1.07
6 3 -0.23
7 1 -0.62
8 2 -0.60
9 3 -0.06
Hope this helps.

Answer 3

Another idea could be to create a named vector v with the replacement values, loop over your list, rename if there is a match and select the desired columns.
v <- c("a" = "A", "x" = "w")
map_df(df, .f = ~ rename_if(
.x,
.p = names(.x) %in% v,
.f = funs(stringi::stri_replace_all_fixed(., v, names(v), vectorize_all = FALSE))) %>%
select(names(v))
)
Which gives:
## A tibble: 9 x 2
# a x
# <int> <dbl>
#1 1 -1.99
#2 2 -1.11
#3 3 -0.34
#4 1 -0.44
#5 2 -1.07
#6 3 -0.23
#7 1 -0.62
#8 2 -0.60
#9 3 -0.06