R : How to iterate sum across multiple columns?

We may reshape to 'long' format with pivot_longer and do a group by sum

library(dplyr)
library(tidyr)
df1 <- df %>%
   pivot_longer(cols =-ID, names_to = c("item", ".value"), names_sep = "_") %>% 
  filter(item %in% c("itemA", "itemC", "itemD")) %>%
  group_by(ID) %>%
  summarise(across(where(is.numeric), sum, na.rm = TRUE,
       .names = "total_{.col}")) %>%
  left_join(df, .)

-output

> df1
# A tibble: 5 × 19
     ID itemA_1 itemB_1 itemC_1 itemD_1 itemx_1 itemA_3 itemB_3 itemC_3 itemD_3 itemx_3 itemA_n itemB_n itemC_n itemD_n itemx_n total_1
  <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>   <int>
1     1      69      27      56      44      54      53      66      28      67      19      65      38      12      45      33     250
2     2      31      65       7      34      84      19      64      70      27      23      98      65      94      71     100     221
3     3      58      34      68      18      69     100      24      47      54      60      47      48      81      61      22     247
4     4      95      16      85      34       9      28      73      57      79      60      57      31      16      24      84     239
5     5      19      66      43      25      35      31      39      17      15      84      10      23     100       6      74     188
# … with 2 more variables: total_3 <int>, total_n <int>

If we want to use the for loop, then paste the column names with i, evaluate (!!) while assigning (:=)

library(stringr)
for (i in c(1, 3, 'n')) {
     df <- df %>% 
   mutate(!! str_c("total_", i) :=   
      rowSums(across(all_of(str_c(c("itemA_", "itemC_", "itemD_"), i)))))
     }

But, note that this will not be dynamic as we have to manually include the 1, 2, ..., n in the loop

-checking the output from for loop and reshaping

> all.equal(df1$total_1, df$total_1)
[1] TRUE
> all.equal(df1$total_3, df$total_3)
[1] TRUE
> all.equal(df1$total_n, df$total_n)
[1] TRUE

Here's a base R option without pivoting, where we first select the columns that we want to sum, then get the unique suffix names, then we can use rowSums to get the sum of each group (i.e., each unique suffix). Then, I update the column names, and then merge with the original dataframe.

df_sum <- df[, grepl( "ID|itemA|itemC|itemD", names(df))]

suffixes <- unique(sub("^[^_]*_", "", colnames(df_sum)))

df2 <- sapply(suffixes, function(x) rowSums(df_sum[,endsWith(colnames(df_sum), x)]))
colnames(df2)[-1] <- paste("total", colnames(df2)[-1], sep = "_")
merge(x = df, y = df2, by = "ID", all.x = TRUE)

Output

  ID itemA_1 itemB_1 itemC_1 itemD_1 itemx_1 itemA_3 itemB_3 itemC_3 itemD_3 itemx_3 itemA_n itemB_n itemC_n itemD_n itemx_n total_1 total_3 total_n
1  1      92      84      31      74      77      26      71      92      59      70      47      54       7       6      95     197     177      60
2  2      49       6      40       6      94      61      69      58      49      62      66      13      94      52      23      95     168     212
3  3      67      69      34      56      44      94      69       1      52      96      62      64      34      78      67     157     147     174
4  4      86      33      85      87      30      33      26      15      70      97      34      36      74      58      87     258     118     166
5  5      49      25      23      56      63       4      84      35      92      34      33      62      95      77      50     128     131     205

Data

df <- structure(list(ID = 1:5, itemA_1 = c(92L, 49L, 67L, 86L, 49L), 
    itemB_1 = c(84L, 6L, 69L, 33L, 25L), itemC_1 = c(31L, 40L, 
    34L, 85L, 23L), itemD_1 = c(74L, 6L, 56L, 87L, 56L), itemx_1 = c(77L, 
    94L, 44L, 30L, 63L), itemA_3 = c(26L, 61L, 94L, 33L, 4L), 
    itemB_3 = c(71L, 69L, 69L, 26L, 84L), itemC_3 = c(92L, 58L, 
    1L, 15L, 35L), itemD_3 = c(59L, 49L, 52L, 70L, 92L), itemx_3 = c(70L, 
    62L, 96L, 97L, 34L), itemA_n = c(47L, 66L, 62L, 34L, 33L), 
    itemB_n = c(54L, 13L, 64L, 36L, 62L), itemC_n = c(7L, 94L, 
    34L, 74L, 95L), itemD_n = c(6L, 52L, 78L, 58L, 77L), itemx_n = c(95L, 
    23L, 67L, 87L, 50L)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -5L))

Benchmark

df1 <- df
df2 <- df

bm <- microbenchmark::microbenchmark(akrun_tidyverse = {df %>%
                                       pivot_longer(cols =-ID, names_to = c("item", ".value"), names_sep = "_") %>% 
                                       filter(item %in% c("itemA", "itemC", "itemD")) %>%
                                       group_by(ID) %>%
                                       summarise(across(where(is.numeric), sum, na.rm = TRUE,
                                                        .names = "total_{.col}")) %>%
                                       left_join(df, .)},
                                     akrun_loop = {for (i in c(1, 3, 'n')) {
                                       df1 <- df1 %>% 
                                         mutate(!! str_c("total_", i) :=   
                                                  rowSums(across(all_of(str_c(c("itemA_", "itemC_", "itemD_"), i)))))
                                     }},
                                     andrew_baseR = {df_sum <- df2[, grepl( "ID|itemA|itemC|itemD", names(df2))];
                                     suffixes <- unique(sub("^[^_]*_", "", colnames(df_sum)));
                                     df3 <- sapply(suffixes, function(x) rowSums(df_sum[,endsWith(colnames(df_sum), x)]));
                                     colnames(df3)[-1] <- paste("total", colnames(df3)[-1], sep = "_");
                                     merge(x = df, y = df3, by = "ID", all.x = TRUE)}, 
                                     times = 1000)

autoplot(bm)

Another possible solution, based on purrr::map_dfc:

library(tidyverse)

map_dfc(c("1","3","n"),~ data.frame(apply(select(df, ends_with(.x)), 1, sum)) %>% 
    set_names(str_c("Total_",.x))) %>% bind_cols(df, .)

#>   ID itemA_1 itemB_1 itemC_1 itemD_1 itemx_1 itemA_3 itemB_3 itemC_3 itemD_3
#> 1  1      89      33      84      15      86       8       9      18      98
#> 2  2      37       2      32      52      37      79      37      31      57
#> 3  3       5      21      54      58      25      74      43       7      14
#> 4  4      19      76      71      84      75      34      72     100      33
#> 5  5      78      52      58      42       5      28      90      21      95
#>   itemx_3 itemA_n itemB_n itemC_n itemD_n itemx_n Total_1 Total_3 Total_n
#> 1      78       5      52       4      73      88     307     211     222
#> 2      82      88      78       3       3      69     160     286     241
#> 3      41      60      73      59      32      87     163     179     311
#> 4      97      86      33      81      44      22     325     336     266
#> 5      37       4       9      35       5      38     235     271      91