Should pivot_wider(values_fill =) fill existing missing values?

[DavisVaughan]

Currently it only fills "new" missing values. spread() filled both new and pre-existing missing values.

If it also filled existing missing values, that would help with the complete() + pivot_wider() story

library(tidyverse)

d <- head(mtcars) %>% 
  group_by(gear, cyl) %>% 
  summarise(mean_hp = mean(hp)) %>%
  ungroup()
#> `summarise()` has grouped output by 'gear'. You can override using the `.groups` argument.

d
#> # A tibble: 4 × 3
#>    gear   cyl mean_hp
#>   <dbl> <dbl>   <dbl>
#> 1     3     6    108.
#> 2     3     8    175 
#> 3     4     4     93 
#> 4     4     6    110

pivot_wider(d, names_from = cyl, values_from = mean_hp, values_fill = 0)
#> # A tibble: 2 × 4
#>    gear   `6`   `8`   `4`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3  108.   175     0
#> 2     4  110      0    93

spread(d, cyl, mean_hp, fill = 0)
#> # A tibble: 2 × 4
#>    gear   `4`   `6`   `8`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3     0  108.   175
#> 2     4    93  110      0

# but maybe we want gear and cyl in numeric ordering
d <- complete(d, gear, cyl)
d
#> # A tibble: 6 × 3
#>    gear   cyl mean_hp
#>   <dbl> <dbl>   <dbl>
#> 1     3     4     NA 
#> 2     3     6    108.
#> 3     3     8    175 
#> 4     4     4     93 
#> 5     4     6    110 
#> 6     4     8     NA

# it won't fill existing NAs
pivot_wider(d, names_from = cyl, values_from = mean_hp, values_fill = 0)
#> # A tibble: 2 × 4
#>    gear   `4`   `6`   `8`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3    NA  108.   175
#> 2     4    93  110     NA

spread(d, cyl, mean_hp, fill = 0)
#> # A tibble: 2 × 4
#>    gear   `4`   `6`   `8`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3     0  108.   175
#> 2     4    93  110      0

^{Created on 2021-12-14 by the reprex package (v2.0.1)}

[hadley]

I'm pretty sure this was deliberate (I have a vague recollection that reshape2 filled all missings). We could revisit the decision, but it makes me a little uncomfortable.

[DavisVaughan]

You could always fill at the complete() step, but that does fill both implicit and explicit missings, so you can't ever get it exactly right

library(tidyverse)

mtcars$hp[1] <- NA

d <- head(mtcars) %>% 
  group_by(gear, cyl) %>% 
  summarise(mean_hp = mean(hp)) %>%
  ungroup()
#> `summarise()` has grouped output by 'gear'. You can override using the `.groups` argument.

d
#> # A tibble: 4 × 3
#>    gear   cyl mean_hp
#>   <dbl> <dbl>   <dbl>
#> 1     3     6    108.
#> 2     3     8    175 
#> 3     4     4     93 
#> 4     4     6     NA

# - doesn't fill the explicit NA (right)
# - doesn't have desired column order (wrong)
pivot_wider(d, names_from = cyl, values_from = mean_hp, values_fill = 0)
#> # A tibble: 2 × 4
#>    gear   `6`   `8`   `4`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3  108.   175     0
#> 2     4   NA      0    93

# fills in both explicit and implicit NAs
d <- complete(d, gear, cyl, fill = list(mean_hp = 0))
d
#> # A tibble: 6 × 3
#>    gear   cyl mean_hp
#>   <dbl> <dbl>   <dbl>
#> 1     3     4      0 
#> 2     3     6    108.
#> 3     3     8    175 
#> 4     4     4     93 
#> 5     4     6      0 
#> 6     4     8      0

# - explicit NA is filled (wrong)
# - has desired column order (right)
pivot_wider(d, names_from = cyl, values_from = mean_hp)
#> # A tibble: 2 × 4
#>    gear   `4`   `6`   `8`
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     3     0  108.   175
#> 2     4    93    0      0

^{Created on 2021-12-14 by the reprex package (v2.0.1)}

[DavisVaughan]

Possibly worth considering adding an option to complete() to support not filling "explicit" missing values.

2015 Hadley seemed to think this was a good idea #127 (comment)

complete2 <- function(data, 
                      ..., 
                      fill = list(), 
                      explicit = TRUE) {
  full <- expand(data, ...)
  names <- names(full)
  
  full <- dplyr::full_join(full, data, by = names)
  
  if (explicit) {
    full <- replace_na(full, replace = fill)
  } else {
    new <- !vec_in(full[names], data[names])
    slice <- vec_slice(full, new)
    slice <- replace_na(slice, replace = fill)
    full <- vec_assign(full, new, slice)
  }
  
  reconstruct_tibble(data, full)
}

complete2(d, gear, cyl, fill = list(mean_hp = 0), explicit = FALSE)
#> # A tibble: 6 × 3
#>    gear   cyl mean_hp
#>   <dbl> <dbl>   <dbl>
#> 1     3     4      0 
#> 2     3     6    108.
#> 3     3     8    175 
#> 4     4     4     93 
#> 5     4     6     NA 
#> 6     4     8      0

[hadley]

2021 Hadley still thinks this is a good idea.