Pivoting single rows into rows with their combined values

I would like to transform the column word into another column, but with the original rows combined together, separed by _.

• Original:

> head(df, 10)
# A tibble: 10 x 1
   word    
   <chr>   
 1 Jason   
 2 Oscar   
 3 Maleeka 
 4 Janet   
 5 Gabriel 
 6 Raheema 
 7 Bryce   
 8 Nasreen 
 9 Hishaam 
10 Thadduse

• desired output:

word 
Jason_Oscar_Maleeka_Janet_Gabriel_Raheema_Bryce_Nasreen_Hishaam_Thadduse
.
.
.

Question

1 How do I pivot word so that each new row contains 10 words per row separated by "_" ? (Tidyverse and stringr approaches would be much appreciated - thanks!)

• Data:

> dput(df)
structure(list(word = c("Jason", "Oscar", "Maleeka", "Janet", 
"Gabriel", "Raheema", "Bryce", "Nasreen", "Hishaam", "Thadduse", 
"Marcos", "Daijah", "Chassity", "Carlito", "Chidiebere", "Matthew", 
"Maureene", "Jillian", "Markus", "Aaron", "Ramziyya", "Marquez", 
"Kiera", "Farajallah", "Larisa", "Davier", "Shujaa", "Vincent", 
"Orlando", "Joseph", "Desean", "Chelsea", "Faadil", "Christopher", 
"Aarifa", "Joel", "Matthew", "Jacob", "Aeones", "Matthew", "Jacob", 
"Savannah", "Nadia", "Kaleem", "Tanner", "Sabeeha", "Caitlyn", 
"Taylor", "Sydney", "Devin")), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -50L))

What you are describing isn't a pivot exactly, but you can achieve it as follows:

library(tidyverse)

df %>%
  group_by((row_number() - 1) %/% 10) %>%
  summarise(word = paste(word, collapse = '_')) %>%
  select(word)
#> # A tibble: 5 x 1
#>   word                                                                           
#>   <chr>                                                                          
#> 1 Jason_Oscar_Maleeka_Janet_Gabriel_Raheema_Bryce_Nasreen_Hishaam_Thadduse       
#> 2 Marcos_Daijah_Chassity_Carlito_Chidiebere_Matthew_Maureene_Jillian_Markus_Aaron
#> 3 Ramziyya_Marquez_Kiera_Farajallah_Larisa_Davier_Shujaa_Vincent_Orlando_Joseph  
#> 4 Desean_Chelsea_Faadil_Christopher_Aarifa_Joel_Matthew_Jacob_Aeones_Matthew     
#> 5 Jacob_Savannah_Nadia_Kaleem_Tanner_Sabeeha_Caitlyn_Taylor_Sydney_Devin 

Here is a base R option with matrix approach

data.frame(
    word =
        do.call(
            paste,
            c(as.data.frame(matrix(unlist(df), ncol = 10, byrow = TRUE)),
                sep = "_"
            )
        )
)

which gives

                                                                             word
1        Jason_Oscar_Maleeka_Janet_Gabriel_Raheema_Bryce_Nasreen_Hishaam_Thadduse
2 Marcos_Daijah_Chassity_Carlito_Chidiebere_Matthew_Maureene_Jillian_Markus_Aaron
3   Ramziyya_Marquez_Kiera_Farajallah_Larisa_Davier_Shujaa_Vincent_Orlando_Joseph
4      Desean_Chelsea_Faadil_Christopher_Aarifa_Joel_Matthew_Jacob_Aeones_Matthew
5          Jacob_Savannah_Nadia_Kaleem_Tanner_Sabeeha_Caitlyn_Taylor_Sydney_Devin

Another way to do it with tidyverse, with real pivoting.

library(tidyverse)

df %>% 
  mutate(rn = rep(1:10, nrow(.) %/% 10),
         group = rep(1:5, each = nrow(.) %/% 5)) %>% 
  pivot_wider(names_from = rn, values_from = word) %>% 
  select(-group) %>% 
  unite(col = word, sep = "_")

<hr>

The following modified version is ugly, but is more dynamic (i.e. you only need to change the value to word_number without the need to modify the piped operation).

word_number <- 10
set_number <- nrow(df) %/% word_number

df %>% 
  mutate(rn = rep(1:word_number, set_number),
         group = rep(1:set_number, each = nrow(.) %/% set_number)) %>% 
  pivot_wider(names_from = rn, values_from = word) %>% 
  select(-group) %>% 
  unite(col = word, sep = "_")

<hr>

Output

# A tibble: 5 × 1
  word                                                                           
  <chr>                                                                          
1 Jason_Oscar_Maleeka_Janet_Gabriel_Raheema_Bryce_Nasreen_Hishaam_Thadduse       
2 Marcos_Daijah_Chassity_Carlito_Chidiebere_Matthew_Maureene_Jillian_Markus_Aaron
3 Ramziyya_Marquez_Kiera_Farajallah_Larisa_Davier_Shujaa_Vincent_Orlando_Joseph  
4 Desean_Chelsea_Faadil_Christopher_Aarifa_Joel_Matthew_Jacob_Aeones_Matthew     
5 Jacob_Savannah_Nadia_Kaleem_Tanner_Sabeeha_Caitlyn_Taylor_Sydney_Devin