Get column names into a new variable based on conditions - r

Question

I have a data frame like this and I am doing this on R. My problems can be divided into two steps.
SUBID
ABC
BCD
DEF
192838
4
-3
2
193928
-6
-2
6
205829
4
-5
9
201837
3
4
4
I want to make a new variable that contains a list of the column names that has a negative value for each SUBID. The output should look something like this:
SUBID
ABC
BCD
DEF
output
192838
4
-3
2
"BCD"
193928
-6
-2
6
"ABC","BCD"
205829
4
-5
9
"BCD"
201837
3
4
4
" "
And then, in the second step, I would like to collapse the SUBID into a more general ID and get the number of unique strings from the output variable for each ID (I just need the number, the specific strings in the parenthesis are just for illustration).
SUBID
output
19
2 ("ABC","BCD")
20
1 ("BCD")
Those are the two steps that I thing should be done, but maybe there is a way that can skip the first step and goes to the second step directly that I don't know.
I would appreciate any help since right now I am not sure where to start on this. Thank you!

Answer 1

Another way:
library(dplyr)
library(tidyr)
df <- df %>% pivot_longer(-SUBID)
df1 <- df %>%
group_by(SUBID) %>%
summarise(output = paste(name[value < 0L], collapse = ','))
df2 <- df %>%
group_by(SUBID = substr(SUBID, 1, 2)) %>%
summarise(output_count = n_distinct(name[value < 0L]),
output = paste0(output_count, ' (', paste(name[value < 0L], collapse = ','), ')'))
Outputs (two columns are created in the second case, one with just the count and another following your example):
df1
# A tibble: 4 x 2
SUBID output
<int> <chr>
1 192838 "BCD"
2 193928 "ABC,BCD"
3 201837 ""
4 205829 "BCD"
df2
# A tibble: 2 x 3
SUBID output_count output
<chr> <int> <chr>
1 19 2 2 (BCD,ABC,BCD)
2 20 1 1 (BCD)

Answer 2

This answers the first part of your question, the second one, I didn't understand
df$output <-apply(df[,-1], 1, function(x) paste(names(df)[-1][x<0], collapse = ","))
df
SUBID ABC BCD DEF output
1 192838 4 3 -2 DEF
2 193928 -6 -2 6 ABC,BCD
3 205829 4 -5 9 BCD
4 201837 3 4 4
For the second part, try this:
id <- sapply(strsplit(sub("\\W+", "", df$output), split = ""), function(x){
sum(!(duplicated(x) | duplicated(x, fromLast = TRUE)))
} )
data.frame(SUBID = substr(df$SUBID, 1,2), output = id, string = df$output)
SUBID output string
1 19 3 DEF
2 19 2 ABC,BCD
3 20 3 BCD
4 20 0
I added the variable string for you make sure your count of unique values is ok.

Answer 3

One option is to take advantage of dplyr::cur_data() to access the names() of the data and subset based on your criteria. Then you can take advantage of tibble list-columns to hold on to a set of column names of arbitrary length and finally calculate the number of unique values in that list.
library(tidyverse)
d <- structure(list(SUBID = c(192838, 193928, 205829, 201837), ABC = c(4, -6, 4, 3), BCD = c(-3, -2, -5, 4), DEF = c(2, 6, 9, 4)), row.names = c(NA, -4L), class = "data.frame")
d %>%
rowwise() %>%
mutate(neg_col_names = list(names(cur_data())[cur_data() < 0])) %>%
group_by(ID_grp = str_sub(SUBID, 1, 2)) %>%
summarize(neg_col_count = n_distinct(unlist(c(neg_col_names))))
#> # A tibble: 2 × 2
#> ID_grp neg_col_count
#> <chr> <int>
#> 1 19 2
#> 2 20 1
Created on 2022-11-22 with reprex v2.0.2