问题
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?
回答1:
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)
回答2:
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
回答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
回答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
来源：https://stackoverflow.com/questions/22523131/dplyr-summarise-equivalent-of-drop-false-to-keep-groups-with-zero-length-in