如何在 dplyr 中定义一个函数? [英] How to define a function in dplyr?
问题描述
我在 R 的 dplyr
包中创建了一个简单的数据透视表.这是我的工作示例:
I created a simple pivot table in the dplyr
package in R. Here is my working example:
library(dplyr)
mean_mpg <- mean(mtcars$mpg)
# creating a new variable that shows that Miles/(US) gallon is greater than the mean or not
mtcars <-
mtcars %>%
mutate(mpg_cat = ifelse(mpg > mean_mpg, 1,0))
mtcars %>%
group_by(as.factor(cyl)) %>%
summarise(sum=sum(mpg_cat),total=n()) %>%
mutate(percentage=sum*100/total)
现在,我想写一个函数来重用这段代码:
Now, I want to write a function to reuse this code:
get_pivot <- function(data, predictor,target) {
result <-
data %>%
group_by(as.factor(predictor)) %>%
summarise(sum=sum(target),total=n()) %>%
mutate(percentage=sum*100/total);
print(result)
}
但我收到以下错误:
is.factor(x) 中的错误:找不到对象cyl"
Error in is.factor(x) : object 'cyl' not found
我也试过
get_pivot(mtcars, "cyl", "mpg_cat" )
但是没有用.
我该怎么办?
推荐答案
如果您有最新的 rlang
库更新 v0.4.0(2019 年 6 月),您可以使用双大括号 {{ }}
(又名curly curly")使使用 dplyr 编程更容易.
If you have the most recent rlang
library update v0.4.0 (June 2019), you can use double curly brackets {{ }}
(aka "curly curly") to make programming with dplyr easier.
# Note: needs installation of rlang 0.4.0 or later
get_pivot <- function(data, predictor,target) {
result <-
data %>%
group_by(as.factor( {{ predictor }} )) %>%
summarise(sum=sum( {{ target }} ),total=n()) %>%
mutate(percentage=sum*100/total);
print(result)
}
# Edit -- thank you Rui Barradas
> get_pivot(mtcars, cyl, mpg_cat)
# A tibble: 3 x 4
`as.factor(cyl)` sum total percentage
<fct> <dbl> <int> <dbl>
1 4 11 11 100
2 6 3 7 42.9
3 8 0 14 0
需要这样做的原因是 dplyr
和其他 tidyverse
包使用非标准评估",就像你遇到一些基本的 R 函数一样,比如 lm(mpg~factor(am),data=mtcars)
.这种做法通常使交互式"代码更短、更简单、更易于阅读,但代价是使编程更加复杂.在这种情况下,{{}}
运算符用于将您指定的列传输到函数的上下文中.
The reason this is required is that dplyr
and other tidyverse
packages use "non-standard evaluation" like you encounter with some base R functions, like lm(mpg~factor(am),data=mtcars)
. This practice often makes "interactive" code shorter, simpler, and easier to read, but at the cost of making programming more complicated. In this case, the {{ }}
operator serves to transport the column you specify into the context of the function.
https://www.tidyverse.org/文章/2019/06/rlang-0-4-0/
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