dplyr summarise: Equivalent of ".drop=FALSE" to keep groups with zero length in output

Question

When using summarise with plyr's ddply function, empty categories are dropped by default. You can change this behavior by adding .drop = FALSE. However, this doesn't work when using summarise with dplyr. Is there another way to keep empty categories in the result?

Here's an example with fake data.

library(dplyr)

df = data.frame(a=rep(1:3,4), b=rep(1:2,6))

# Now add an extra level to df$b that has no corresponding value in df$a
df$b = factor(df$b, levels=1:3)

# Summarise with plyr, keeping categories with a count of zero
plyr::ddply(df, "b", summarise, count_a=length(a), .drop=FALSE)

  b    count_a
1 1    6
2 2    6
3 3    0

# Now try it with dplyr
df %.%
  group_by(b) %.%
  summarise(count_a=length(a), .drop=FALSE)

  b     count_a .drop
1 1     6       FALSE
2 2     6       FALSE

Not exactly what I was hoping for. Is there a dplyr method for achieving the same result as .drop=FALSE in plyr?

Answer 1

The issue is still open, but in the meantime, especially since your data are already factored, you can use complete from "tidyr" to get what you might be looking for:

library(tidyr)
df %>%
  group_by(b) %>%
  summarise(count_a=length(a)) %>%
  complete(b)
# Source: local data frame [3 x 2]
# 
#        b count_a
#   (fctr)   (int)
# 1      1       6
# 2      2       6
# 3      3      NA

If you wanted the replacement value to be zero, you need to specify that with fill:

df %>%
  group_by(b) %>%
  summarise(count_a=length(a)) %>%
  complete(b, fill = list(count_a = 0))
# Source: local data frame [3 x 2]
# 
#        b count_a
#   (fctr)   (dbl)
# 1      1       6
# 2      2       6
# 3      3       0