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

Answer 2

Since dplyr 0.8 group_by gained the .drop argument that does just what you asked for:

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

df %>%
  group_by(b, .drop=FALSE) %>%
  summarise(count_a=length(a))

#> # A tibble: 3 x 2
#>   b     count_a
#>   <fct>   <int>
#> 1 1           6
#> 2 2           6
#> 3 3           0

One additional note to go with @Moody_Mudskipper's answer: Using .drop=FALSE can give potentially unexpected results when one or more grouping variables are not coded as factors. See examples below:

library(dplyr)
data(iris)

# Add an additional level to Species
iris$Species = factor(iris$Species, levels=c(levels(iris$Species), "empty_level"))

# Species is a factor and empty groups are included in the output
iris %>% group_by(Species, .drop=FALSE) %>% tally

#>   Species         n
#> 1 setosa         50
#> 2 versicolor     50
#> 3 virginica      50
#> 4 empty_level     0

# Add character column
iris$group2 = c(rep(c("A","B"), 50), rep(c("B","C"), each=25))

# Empty groups involving combinations of Species and group2 are not included in output
iris %>% group_by(Species, group2, .drop=FALSE) %>% tally

#>   Species     group2     n
#> 1 setosa      A         25
#> 2 setosa      B         25
#> 3 versicolor  A         25
#> 4 versicolor  B         25
#> 5 virginica   B         25
#> 6 virginica   C         25
#> 7 empty_level <NA>       0

# Turn group2 into a factor
iris$group2 = factor(iris$group2)

# Now all possible combinations of Species and group2 are included in the output, 
#  whether present in the data or not
iris %>% group_by(Species, group2, .drop=FALSE) %>% tally

#>    Species     group2     n
#>  1 setosa      A         25
#>  2 setosa      B         25
#>  3 setosa      C          0
#>  4 versicolor  A         25
#>  5 versicolor  B         25
#>  6 versicolor  C          0
#>  7 virginica   A          0
#>  8 virginica   B         25
#>  9 virginica   C         25
#> 10 empty_level A          0
#> 11 empty_level B          0
#> 12 empty_level C          0

Created on 2019-03-13 by the reprex package (v0.2.1)

Answer 3

dplyr solution:

First make grouped df

by_b <- tbl_df(df) %>% group_by(b)

then we summarise those levels that occur by counting with n()

res <- by_b %>% summarise( count_a = n() )

then we merge our results into a data frame that contains all factor levels:

expanded_res <- left_join(expand.grid(b = levels(df$b)),res)

finally, in this case since we are looking at counts the NA values are changed to 0.

final_counts <- expanded_res[is.na(expanded_res)] <- 0

This can also be implemented functionally, see answers: Add rows to grouped data with dplyr?

A hack:

I thought I would post a terrible hack that works in this case for interest's sake. I seriously doubt you should ever actually do this but it shows how group_by() generates the atrributes as if df$b was a character vector not a factor with levels. Also, I don't pretend to understand this properly -- but I am hoping this helps me learn -- this is the only reason I'm posting it!

by_b <- tbl_df(df) %>% group_by(b)

define an "out-of-bounds" value that cannot exist in dataset.

oob_val <- nrow(by_b)+1

modify attributes to "trick" summarise():

attr(by_b, "indices")[[3]] <- rep(NA,oob_val)
attr(by_b, "group_sizes")[3] <- 0
attr(by_b, "labels")[3,] <- 3

do the summary:

res <- by_b %>% summarise(count_a = n())

index and replace all occurences of oob_val

res[res == oob_val] <- 0

which gives the intended:

> res
Source: local data frame [3 x 2]

b count_a
1 1       6
2 2       6
3 3       0

Answer 4

this is not exactly what was asked in the question, but at least for this simple example, you could get the same result using xtabs, for example:

using dplyr:

df %>%
  xtabs(formula = ~ b) %>%
  as.data.frame()

or shorter:

as.data.frame(xtabs( ~ b, df))

result (equal in both cases):

  b Freq
1 1    6
2 2    6
3 3    0