R: Reshaping Multiple Columns from Long to Wide

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An option would be to replace the duplicated elements by ‘Letter’ to NA and then in the reshaped data, remove the columns that are all NA

library(data.table)
out <- dcast(setDT(sample_df)[, lapply(.SD, function(x) 
     replace(x, duplicated(x), NA)), Letter], Letter ~ rowid(Letter), 
     value.var = c("Number", "Fruit"))
nm1 <- out[, names(which(!colSums(!is.na(.SD))))]
out[, (nm1) := NULL][]
#   Letter Number_1 Number_2 Fruit_1 Fruit_2 Fruit_3
#1:      a        1        2   Apple    Plum   Peach
#2:      b        3        4    Pear   Peach    <NA>

If we want to use the tidyverse approach, a similar option can be used. Note that pivot_wider is from the dev version of tidyr (tidyr_0.8.3.9000)

library(tidyverse)
sample_df %>% 
     group_by(Letter) %>%
     mutate_at(vars(-group_cols()), ~ replace(., duplicated(.), NA)) %>%
     mutate(rn = row_number()) %>% 
  pivot_wider(
          names_from = rn,
          values_from = c("Number", "Fruit")) %>%
  select_if(~ any(!is.na(.)))
# A tibble: 2 x 6
#  Letter Number_1 Number_2 Fruit_1 Fruit_2 Fruit_3
#  <fct>     <dbl>    <dbl> <fct>   <fct>   <fct>  
#1 a             1        2 Apple   Plum    Peach  
#2 b             3        4 Pear    Peach   <NA>

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