Concatenate columns and rows in just one row

[Gabbi]

Hello,
I am trying to concatenate (paste) several columns and rows.

Here is an example:

My data:

What I need:

it's a simple task, but I didn't find a way to make it work in this case.

Can someone help, please?

[FJCC]

Close enough?

library(tidyr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
DF <- data.frame(ID=rep(1:3,each=3),
                 V1=paste0("A",1:9),
                 V2=paste0("B",1:9),
                 V3=paste0("C",1:9),
                 V4=paste0("D",1:9))
DF               
#>   ID V1 V2 V3 V4
#> 1  1 A1 B1 C1 D1
#> 2  1 A2 B2 C2 D2
#> 3  1 A3 B3 C3 D3
#> 4  2 A4 B4 C4 D4
#> 5  2 A5 B5 C5 D5
#> 6  2 A6 B6 C6 D6
#> 7  3 A7 B7 C7 D7
#> 8  3 A8 B8 C8 D8
#> 9  3 A9 B9 C9 D9
DFlng <- pivot_longer(DF,V1:V4)
DFall <- DFlng %>% group_by(ID) %>% 
  summarize(Alltext=paste(value,collapse=","))
#> `summarise()` ungrouping output (override with `.groups` argument)
DF <- inner_join(DF,DFall,by="ID")
DF
#>   ID V1 V2 V3 V4                             Alltext
#> 1  1 A1 B1 C1 D1 A1,B1,C1,D1,A2,B2,C2,D2,A3,B3,C3,D3
#> 2  1 A2 B2 C2 D2 A1,B1,C1,D1,A2,B2,C2,D2,A3,B3,C3,D3
#> 3  1 A3 B3 C3 D3 A1,B1,C1,D1,A2,B2,C2,D2,A3,B3,C3,D3
#> 4  2 A4 B4 C4 D4 A4,B4,C4,D4,A5,B5,C5,D5,A6,B6,C6,D6
#> 5  2 A5 B5 C5 D5 A4,B4,C4,D4,A5,B5,C5,D5,A6,B6,C6,D6
#> 6  2 A6 B6 C6 D6 A4,B4,C4,D4,A5,B5,C5,D5,A6,B6,C6,D6
#> 7  3 A7 B7 C7 D7 A7,B7,C7,D7,A8,B8,C8,D8,A9,B9,C9,D9
#> 8  3 A8 B8 C8 D8 A7,B7,C7,D7,A8,B8,C8,D8,A9,B9,C9,D9
#> 9  3 A9 B9 C9 D9 A7,B7,C7,D7,A8,B8,C8,D8,A9,B9,C9,D9

^{Created on 2021-02-25 by the reprex package (v0.3.0)}

[lars]

This is a slightly different approach, with an additional step to remove duplicated values. You can, of course, combine elements of both approaches to get the best of both solutions.

library(tidyverse)

df <- tibble(id = rep(1:3, each = 3),
             var1 = str_c("A", 1:9),
             var2 = str_c("B", 1:9),
             var3 = str_c("C", 1:9),
             var4 = str_c("D", 1:9),
             )

df %>% 
  ## first concatenate variables per row
  rowwise() %>% 
  mutate(new_col = str_c(c(var1, var2, var3, var4), collapse = ", ")) %>%
  ## then combine/collapse all values per id
  group_by(id) %>% 
  mutate(new_col = str_c(new_col, collapse = ","),
         ## remove duplicated values
         new_col = if_else(row_number() > 1 & new_col == lag(new_col), "-", new_col),
         ) %>%
  ungroup()
#> # A tibble: 9 x 6
#>      id var1  var2  var3  var4  new_col                                     
#>   <int> <chr> <chr> <chr> <chr> <chr>                                       
#> 1     1 A1    B1    C1    D1    A1, B1, C1, D1,A2, B2, C2, D2,A3, B3, C3, D3
#> 2     1 A2    B2    C2    D2    -                                           
#> 3     1 A3    B3    C3    D3    -                                           
#> 4     2 A4    B4    C4    D4    A4, B4, C4, D4,A5, B5, C5, D5,A6, B6, C6, D6
#> 5     2 A5    B5    C5    D5    -                                           
#> 6     2 A6    B6    C6    D6    -                                           
#> 7     3 A7    B7    C7    D7    A7, B7, C7, D7,A8, B8, C8, D8,A9, B9, C9, D9
#> 8     3 A8    B8    C8    D8    -                                           
#> 9     3 A9    B9    C9    D9    -

^{Created on 2021-02-26 by the reprex package (v1.0.0)}

[Gabbi]

Both solutions worked perfectly! Thank you!!