I have two lists as a result of some processing. The objective is to grab only those columns from the lists that is TRUE in the second list.
Here’s some example data:
> first_try
[[1]]
X1 X2 X3 X4
1 status income verbal sex
[[2]]
X1 X2 X3 X4
1 status income verbal sex
2 income verbal sex status
> second_try
$`1`
X1 X2 X3 X4
1 FALSE FALSE FALSE TRUE
$`2`
X1 X2 X3 X4
2 FALSE FALSE TRUE TRUE
So for the first list we only select X4 from [[1]] because it has a value TRUE in second at [[1]], and from list [[2]] we only select X3,X4 because it has the value TRUE, and so forth.
I thought that I could somehow merge the two lists and try getting it that way, but I was having difficulties getting this to work.
something that I tried:
first <- list()
second <- list()
for(i in 1:length(second_try)){
second[[i]]<-second_try[[i]] %>% pivot_longer(-c())
first[[i]]<-first_try[[i]] %>% pivot_longer(-c())
}
first %>% mapply(function(x, y)inner_join(x, y, by='name'), second, .)
Expected output:
> first_try
[[1]]
X4
sex
[[2]]
X3 X4
verbal sex
sex status
Reproducible code:
first_try<-list(structure(list(X1 = "status", X2 = "income", X3 = "verbal",
X4 = "sex"), row.names = 1L, class = "data.frame"), structure(list(
X1 = c("status", "income"), X2 = c("income", "verbal"), X3 = c("verbal",
"sex"), X4 = c("sex", "status")), row.names = 1:2, class = "data.frame"))
second_try<-list(`1` = structure(list(X1 = FALSE, X2 = FALSE, X3 = FALSE,
X4 = TRUE), row.names = 1L, class = "data.frame"), `2` = structure(list(
X1 = FALSE, X2 = FALSE, X3 = TRUE, X4 = TRUE), row.names = 2L, class = "data.frame"))
>Solution :
You can do this with a single line of code in base R. Use Map to subset each member of first_try by the unlisted member at the equivalent position of second_try.
Map(function(a, b) a[unlist(b)], first_try, second_try)
#> [[1]]
#> X4
#> 1 sex
#>
#> [[2]]
#> X3 X4
#> 1 verbal sex
#> 2 sex status