dplyr cross tab with missing values

Question

I would like to make a cross tab in R using dplyr. I have good reasons for not just using the base table() command.

table(mtcars$cyl, mtcars$gear)
     3  4  5
  4  1  8  2
  6  2  4  1
  8 12  0  2

library(dplyr)
library(tidyr)
mtcars %>% 
  group_by(cyl, gear) %>%
  tally() %>%
  spread(gear, n, fill = 0)
Source: local data frame [3 x 4]

  cyl  3 4 5
1   4  1 8 2
2   6  2 4 1
3   8 12 0 2

This is all well and good. But it seems to fall apart when there are missing values in the group_by() variables.

mtcars %>%
  mutate(
    cyl = ifelse(cyl > 6, NA, cyl),
    gear = ifelse(gear > 4, NA, gear)
  ) %>%
  group_by(cyl, gear) %>%
  tally()
Source: local data frame [8 x 3]
Groups: cyl

  cyl gear  n
1   4    3  1
2   4    4  8
3   4   NA  2
4   6    3  2
5   6    4  4
6   6   NA  1
7  NA    3 12
8  NA   NA  2

# DITTO # %>%
  spread(gear, n)
Error in if (any(names2(x) == "")) { : 
  missing value where TRUE/FALSE needed

I guess what I would like is for a NA column like when you do table(..., useNA = "always"). Any tips?

Answer 1

One option is to replace the NAs with a label. This can be accomplished easily with mutate_each:

mtcars %>%
  mutate(
    cyl = ifelse(cyl > 6, NA, cyl),
    gear = ifelse(gear > 4, NA, gear)
  ) %>%
  group_by(cyl, gear) %>%
  tally() %>%
  ungroup() %>%
  mutate_each(funs(replace(., is.na(.), 'missing'))) %>%
  spread(gear, n)

#       cyl  3  4 missing
# 1       4  1  8       2
# 2       6  2  4       1
# 3 missing 12 NA       2

Answer 2

Agreed that the permanent solution to this should be a tidyr bug fix, but in the meantime, this can be worked around by dropping the dplyr tbl_df format:

mtcars %>%
  mutate(
    cyl = ifelse(cyl > 6, NA, cyl),
    gear = ifelse(gear > 4, NA, gear)
  ) %>%
  group_by(cyl, gear) %>%
  tally() %>%
  data.frame() %>% ### <-- go from tbl_df to data.frame
  spread(gear, n)

  cyl  3  4 NA
1   4  1  8  2
2   6  2  4  1
3  NA 12 NA  2

The addition of the data.frame() call allows your code to run, though it produces a column named NA so this is probably best suited for exploratory analyses that print to the console.