R- How to use map() into map() - r

Question

I need access a tibble table that is in a nest() function inside of another nest() function.
x <- list( factory = c('a','b','c','d'), cost = c(21,30,44,100))
x <- as.data.frame(x)
x <- x %>%
melt('cost','factory')
colnames(x) <- c('cost','client','type')
x <- x %>%
group_by(client)%>%
nest()
for (m in 1:4) {
if(m==1){
x$scene <- m
x2 <- x
}else{
x3 <- x
x3$scene <- m
x2 <- rbind(x2,x3)
}
}
x2 <- x2 %>%
group_by(scene) %>%
nest()
What am I trying to do is applying a function inside of first vector, something like:
test <- function(df){
df$data %>%
mutate(increa = cost + 15)
}
x2$data%>%
map(test)
dput(x2) result a:
structure(list(scene = 1:4, data = list(structure(list(client =
structure(1L, .Label = "factory", class = "factor"),
data = list(structure(list(cost = c(21, 30, 44, 100), type = c("a",
"b", "c", "d")), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame")))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame")), structure(list(client =
structure(1L, .Label = "factory", class = "factor"),
data = list(structure(list(cost = c(21, 30, 44, 100), type = c("a",
"b", "c", "d")), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame")))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame")), structure(list(client =
structure(1L, .Label = "factory", class = "factor"),
data = list(structure(list(cost = c(21, 30, 44, 100), type = c("a",
"b", "c", "d")), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame")))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame")), structure(list(client =
structure(1L, .Label = "factory", class = "factor"),
data = list(structure(list(cost = c(21, 30, 44, 100), type = c("a",
"b", "c", "d")), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame")))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame")))), row.names = c(NA, -4L), class =
c("tbl_df", "tbl", "data.frame"))
The expected result:
[[1]]
[[1]]$`factory`
[1] "a" "b" "c" "d"
[[1]]$cost
[1] 21 30 44 100
[[1]]$increa
[1] 36 45 59 115
[[2]]
[[2]]$`factory`
[1] "a" "b" "c" "d"
[[2]]$cost
[1] 21 30 44 100
[[2]]$increa
[1] 36 45 59 115
[[3]]
[[3]]$`factory`
[1] "a" "b" "c" "d"
[[3]]$cost
[1] 21 30 44 100
[[3]]$increa
[1] 36 45 59 115
[[4]]
[[4]]$`factory`
[1] "a" "b" "c" "d"
[[4]]$cost
[1] 21 30 44 100
[[4]]$increa
[1] 36 45 59 115
Someone could help me to solve this issue?
ANSWER
This is the result that I was looking for:
map(x2$data, function(df) map(df$data, function(df) df <- mutate(df,increa = cost + 15)))

Answer 1

To get your desired output I think it is easier first to extract the level of information you want to have and then calculate the new column. If you on the other hand want to manipulate the data in this structure and preserve this, than a nested call of map and mutate is necessary-
library(tidyverse)
First solution - extract information and then calculate new column:
We can get to desired level of information with
map(x2$data, ~ .x$data)
# [[1]]
# [[1]][[1]]
# # A tibble: 4 x 2
# cost type
# <dbl> <chr>
# 1 21 a
# 2 30 b
# 3 44 c
# 4 100 d
#
#
# [[2]]
# [[2]][[1]]
# # A tibble: 4 x 2
# cost type
# <dbl> <chr>
# 1 21 a
# 2 30 b
# 3 44 c
# 4 100 d
#
# ...
As this is a nested list structure a second map is needed to calculate the new column. Here the mutate-function is applied to each of the nested data entries with the additional specification to create a new column inc.
map(x2$data, ~ map(.x$data, mutate, inc = cost + 15))
# [[1]]
# [[1]][[1]]
# # A tibble: 4 x 3
# cost type inc
# <dbl> <chr> <dbl>
# 1 21 a 36
# 2 30 b 45
# 3 44 c 59
# 4 100 d 115
#
#
# [[2]]
# [[2]][[1]]
# # A tibble: 4 x 3
# cost type inc
# <dbl> <chr> <dbl>
# 1 21 a 36
# 2 30 b 45
# 3 44 c 59
# 4 100 d 115
#
# ...
The same result would be obtained with an extra function test which takes a data.frame as input parameter and calculates the new column:
test <- function(df){
mutate(df, increa = cost + 15)
}
map(x2$data, ~ map(.x$data, test))
Second solution - Manipulate in place
If you however want to keep this nested structure, then we use mutate on the first data-column with map and again mutate and map:
x2_new <- x2 %>%
mutate(data = map(data, function(df1) mutate(df1, data = map(data, test))))
To verify that this worked we again extract the needed information as above:
map(x2_new$data, ~ .x$data)
# [[1]]
# [[1]][[1]]
# # A tibble: 4 x 3
# cost type increa
# <dbl> <chr> <dbl>
# 1 21 a 36
# 2 30 b 45
# 3 44 c 59
# 4 100 d 115
#
#
# [[2]]
# [[2]][[1]]
# # A tibble: 4 x 3
# cost type increa
# <dbl> <chr> <dbl>
# 1 21 a 36
# 2 30 b 45
# 3 44 c 59
# 4 100 d 115
#
# ...
Third solution - breaks structure but keep information
This is my favourite solution as it turns the data into a tidy format and keeps all information:
x2 %>%
unnest(data) %>%
unnest(data) %>%
mutate(inc = cost + 15)
# A tibble: 16 x 5
# scene client cost type inc
# <int> <fct> <dbl> <chr> <dbl>
# 1 1 factory 21 a 36
# 2 1 factory 30 b 45
# 3 1 factory 44 c 59
# 4 1 factory 100 d 115
# 5 2 factory 21 a 36
# 6 2 factory 30 b 45
# 7 2 factory 44 c 59
# 8 2 factory 100 d 115
# 9 3 factory 21 a 36
# 10 3 factory 30 b 45
# 11 3 factory 44 c 59
# 12 3 factory 100 d 115
# 13 4 factory 21 a 36
# 14 4 factory 30 b 45
# 15 4 factory 44 c 59
# 16 4 factory 100 d 115
Data
generic_data <- structure(
list(client = structure(1L, .Label = "factory", class = "factor"),
data = list(structure(list(cost = c(21, 30, 44, 100),
type = c("a", "b", "c", "d")),
row.names = c(NA, -4L),
class = c("tbl_df", "tbl", "data.frame")))),
row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame"))
x2 <- structure(
list(scene = 1:4,
data = list(generic_data, generic_data, generic_data, generic_data)),
row.names = c(NA, -4L), class = c("tbl_df", "tbl", "data.frame"))

Answer 2

Based on your description, I think you're looking for map_depth
From the documentation https://purrr.tidyverse.org/reference/map_if.html:
map_depth(x, 2, fun) is equivalent to x <- map(x, ~ map(., fun))
which looks like the answer/solution you settled on.