bind list of lists based on the lists location - r

Question

I have some data similar to mainList below.
List of 2
$ :List of 3
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "1" "2" "3"
.. .. ..$ col2: chr [1:3] "a" "b" "c"
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "3" "7" "4"
.. .. ..$ col2: chr [1:3] "e" "d" "g"
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "2" "7" "4"
.. .. ..$ col2: chr [1:3] "l" "o" "i"
$ :List of 3
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "8" "3" "4"
.. .. ..$ col2: chr [1:3] "r" "t" "q"
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "7" "5" "2"
.. .. ..$ col2: chr [1:3] "h" "w" "p"
..$ :List of 1
.. ..$ :'data.frame': 3 obs. of 2 variables:
.. .. ..$ col1: chr [1:3] "9" "3" "6"
.. .. ..$ col2: chr [1:3] "x" "y" "z"
I want to merge, or bind the lists based on the lists location in the list of lists.
That is, I want to merge splt1 with splt11, and then merge splt2 with splt22 and finally splt3 with splt33.
So it would take the first data frame from the first List of 3 and merge it with the first data frame from the second List of 3.
This does not get what I want
mainList %>%
map(., ~bind_rows(., .id = "split"))
Since all of the splits are merged into a single data frame (I want them kept separate).
Data:
splt1 <- list(
data.frame(
col1 = c("1", "2", "3"),
col2 = c("a", "b", "c")
)
)
splt2 <- list(
data.frame(
col1 = c("3", "7", "4"),
col2 = c("e", "d", "g")
)
)
splt3 <- list(
data.frame(
col1 = c("2", "7", "4"),
col2 = c("l", "o", "i")
)
)
nestList1 <- list(
splt1,
splt2,
splt3
)
splt11 <- list(
data.frame(
col1 = c("8", "3", "4"),
col2 = c("r", "t", "q")
)
)
splt22 <- list(
data.frame(
col1 = c("7", "5", "2"),
col2 = c("h", "w", "p")
)
)
splt33 <- list(
data.frame(
col1 = c("9", "3", "6"),
col2 = c("x", "y", "z")
)
)
nestList2 <- list(
splt11,
splt22,
splt33
)
mainList <- list(
nestList1,
nestList2
)
EDIT:
Screenshot of the lists:
I am trying to bind together all of the split's, i.e.
split1 will contain the results from 08001, 08003, 08005 ... 0801501 for each of the lists in catalunya_madrid.
split2 will contain the same results 08001, 08003, 08005 ... 0801501
and so on.
EDIT2:
# Function to invert the list structure
invertListStructure <- function(ll) {
nms <- unique(unlist(lapply(ll, function(X) names(X))))
ll <- lapply(ll, function(X) setNames(X[nms], nms))
ll <- apply(do.call(rbind, ll), 2, as.list)
lapply(ll, function(X) X[!sapply(X, is.null)])
}
invertedList <- map(analysis, ~invertListStructure(.) %>%
map(., ~bind_rows(.x, .id = "MITMA")))

Answer 1

You can use purrr::transpose() to group list elements with the same location (i.e. the first element in list 1 with the first element in list 2 and list 3 and so on) for any number of lists. In your case, transpose will convert 592 lists of 216 into 216 lists of 592, each properly titled. With transpose, l[[x]][[y]] becomes l[[y]][[x]].
library(tidyverse)
mainList %>% purrr::transpose() %>%
map(function(x) {
flatten(x) %>% bind_rows(.id = 'id')
})
# $splt1
# id col1 col2
# 1 1 1 a
# 2 1 2 b
# 3 1 3 c
# 4 2 8 r
# 5 2 3 t
# 6 2 4 q
#
# $splt2
# id col1 col2
# 1 1 3 e
# 2 1 7 d
# 3 1 4 g
# 4 2 7 h
# 5 2 5 w
# 6 2 2 p
#
# $splt3
# id col1 col2
# 1 1 2 l
# 2 1 7 o
# 3 1 4 i
# 4 2 9 x
# 5 2 3 y
# 6 2 6 z
Note that you only need to flatten if the data.frame is in a list of length 1, by itself. If you have a list of data.frames (as opposed to a list of lists, each of which contains one data.frame, as in your example data), you can ignore the flatten() command and just bind the rows.
Your example dataset doesn't quite match your actual data, but if you make a list of two mainLists, it's closer. These types of operations are heavily dependent on the structure of the data, though, so I can't be sure this is what you need. All you need to do here is add a subscript.
mainList2 <- list(mainList, mainList) # First is Madrid, second is Valencia
# Operations are done on Madrid only
mainList2[[1]] %>%
transpose() %>%
map(function(x) {
flatten(x) %>% bind_rows(.id = 'id')
})
If you want to do this for both elements in mainList2, you can wrap the whole thing in map.
mainList2 %>% map(function(x) {
transpose(x) %>%
map(function(x) {
flatten(x) %>% bind_rows(.id = 'id')
})
})

Answer 2

You can combine the pairs in following way :
Map(rbind, unlist(mainList[[1]], recursive = FALSE),
unlist(mainList[[2]], recursive = FALSE))
Or using purrr you can also add an id column easily.
library(purrr)
map2(mainList[[1]] %>% flatten,
mainList[[2]] %>% flatten, dplyr::bind_rows, .id = 'id')
#[[1]]
# id col1 col2
#1 1 1 a
#2 1 2 b
#3 1 3 c
#4 2 8 r
#5 2 3 t
#6 2 4 q
#[[2]]
# id col1 col2
#1 1 3 e
#2 1 7 d
#3 1 4 g
#4 2 7 h
#5 2 5 w
#6 2 2 p
#[[3]]
# id col1 col2
#1 1 2 l
#2 1 7 o
#3 1 4 i
#4 2 9 x
#5 2 3 y
#6 2 6 z