Coalesce multiple pairs of columns by name

Question

I have dataframe of measurements from cancer-treatments that i want to 'merge' pairwise using coalesce(). But the frame contains 100+ columns so i want to use some kind of function or loop.

Here's what my dataframe looks like

I have a vector with the names of the treatments:

drugs <- c("A", "C", "B", "D")

The columns represent multiple measurements from a drug treatment. I want to merge the columns pairwise: A_1 + A_2 into A (new column), B_1, etc

This code works:

df <- df %>% mutate(A = coalesce(A_1, A_2).

But the frame has 100+ columns so I want to use some kind of function or loop, using the value from the vector with drugnames. From each drug there are 2 columns but they are not in the correct order, so I cannot use numbering, I have to use the name of the column. But when I put that into a function it doesn't work.

One addition: I would like to have the resulting columns (A,B,C) etc added to the frame.

Answer 1

Here is an option with split.default - split the data into chunks of data based on the column names pattern, then use coalesce by looping over the list

library(dplyr)
library(stringr)
library(purrr)
df1 %>% 
 split.default(str_remove(names(.), "\\d+$")) %>%
 map_dfc(~ exec(coalesce, !!!.x))

-output

# A tibble: 5 × 3
      A     B     C
  <dbl> <dbl> <dbl>
1     1     6     2
2     5     5     1
3     3     3     3
4     1     6     3
5    10     8     5

data

df1 <- structure(list(A1 = c(1, 5, NA, 1, 10), B1 = c(NA, 5, 3, 6, 8
), C1 = c(2, NA, NA, 3, 5), A2 = c(1, 2, 3, 4, 5), C2 = c(0, 
1, 3, 2, NA), B2 = c(6, NA, 7, NA, NA)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -5L))

Answer 2

Updated for the suffixes _ViMIC and _EtMIC:

library(tidyverse)

old_df <- tribble(
  ~A_EtMIC, ~B_EtMIC, ~C_EtMIC, ~A_ViMIC, ~C_ViMIC, ~B_ViMIC,
  1, NA, 2, 1, 0, 6,
  5, 5, NA, 2, 1, NA,
  NA, 3, NA, 3, 3, 7,
  1, 6, 3, 4, 2, NA,
  10, 8, 5, 5, NA, NA
) 

new_df <- old_df |> 
  mutate(row = row_number()) |> 
  pivot_longer(-row, names_to = c("prefix", "suffix"), 
               names_pattern = "(.*)_(..MIC)") |> 
  pivot_wider(names_from = suffix, 
              values_from = value) |> 
  mutate(coalesced = coalesce(EtMIC, ViMIC)) |> 
  select(- ends_with("MIC")) |> 
  pivot_wider(names_from = prefix, values_from = coalesced,
              names_glue = "{prefix}_MIC")

both_df <- bind_cols(new_df, old_df)

both_df
#> # A tibble: 5 × 10
#>     row A_MIC B_MIC C_MIC A_EtMIC B_EtMIC C_EtMIC A_ViMIC C_ViMIC B_ViMIC
#>   <int> <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1     1     1     6     2       1      NA       2       1       0       6
#> 2     2     5     5     1       5       5      NA       2       1      NA
#> 3     3     3     3     3      NA       3      NA       3       3       7
#> 4     4     1     6     3       1       6       3       4       2      NA
#> 5     5    10     8     5      10       8       5       5      NA      NA

^{Created on 2022-06-05 by the reprex package (v2.0.1)}