问题
I was expecting to see the same results between these two runs, and they are different. Makes me question if I really understand what how the dplyr code is working (I have read pretty much everything I can find about dplyr in the package and online). Can anyone explain why the results are different, or how to obtain similar results?
library(dplyr)
x <- iris
x <- x %.%
group_by(Species, Sepal.Width) %.%
summarise (freq=n()) %.%
summarise (mean_by_group = mean(Sepal.Width))
print(x)
x <- iris
x <- tapply(x$Sepal.Width, x$Species, mean)
print(x)
Update: I don't think this is the most efficient way to do this, but the following code gives a result that matches the tapply approach. Per Hadley's suggestion, I scrutinized the results line by line, and this is the best I could come up with using dplyr
library(dplyr)
x <- iris
x <- x %.%
group_by(Species, Sepal.Width) %.%
summarise (freq=n()) %.%
mutate (mean_by_group = sum(Sepal.Width*freq)/sum(freq)) %.%
print(x)
Update: for some reason I thought I had to group all variables I wanted to analyse, which is what sent things in the wrong direction. This is all I needed, which is closer to the examples in the package.
x <- iris %.%
group_by(Species) %.%
summarise(Sepal.Width = mean(Sepal.Width))
print(x)
回答1:
Maybe this...
- dplyr
:
require(dplyr)
iris %>% group_by(Species) %>% summarise(mean_width = mean(Sepal.Width))
# Source: local data frame [3 x 2]
#
# Species mean_width
# 1 setosa 3.428
# 2 versicolor 2.770
# 3 virginica 2.974
- tapply
:
tapply(iris$Sepal.Width, iris$Species, mean)
# setosa versicolor virginica
# 3.428 2.770 2.974
NOTE: tapply()
simplifies output by default whereas summarise()
does not:
typeof(tapply(iris$Sepal.Width, iris$Species, mean, simplify=TRUE))
# [1] "double"
it returns a list
otherwise:
typeof(tapply(iris$Sepal.Width, iris$Species, mean, simplify=FALSE))
# [1] "list"
So to actually get the same type of output form tapply()
you would need:
tbl_df(
data.frame(
mean_width = tapply( iris$Sepal.Width,
iris$Species,
mean )))
# Source: local data frame [3 x 1]
#
# mean_width
# setosa 3.428
# versicolor 2.770
# virginica 2.974
and this still isn't the same! as unique(iris$Species)
is an attribute
here and not a column of the df...
来源:https://stackoverflow.com/questions/23878678/understanding-difference-in-results-between-dplyr-group-by-vs-tapply