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)