[英]can I switch the grouping variable in a single dplyr statement?
Here's a simple example to illustrate the issue: 这是一个简单的例子来说明这个问题:
library(data.table)
dt = data.table(a = c(1,1,2,2), b = 1:2)
dt[, c := cumsum(a), by = b][, d := cumsum(a), by = c]
# a b c d
#1: 1 1 1 1
#2: 1 2 1 2
#3: 2 1 3 2
#4: 2 2 3 4
Attempting to do the same in dplyr
I fail because the first group_by
is persistent and the grouping is by both b
and c
: 尝试在
dplyr
执行相同的dplyr
我失败了,因为第一个group_by
是持久性的,并且分组是由b
和c
:
df = data.frame(a = c(1,1,2,2), b = 1:2)
df %.% group_by(b) %.% mutate(c = cumsum(a)) %.%
group_by(c) %.% mutate(d = cumsum(a))
# a b c d
#1 1 1 1 1
#2 1 2 1 1
#3 2 1 3 2
#4 2 2 3 2
Is this a bug or a feature? 这是一个错误还是一个功能? If it's a feature, then how would one replicate the
data.table
solution in a single statement? 如果它是一个功能,那么如何在单个语句中复制
data.table
解决方案?
Try this: 试试这个:
> df %>% group_by(b) %>% mutate(c = cumsum(a)) %>%
+ group_by(c) %>% mutate(d = cumsum(a))
Source: local data frame [4 x 4]
Groups: c
a b c d
1 1 1 1 1
2 1 2 1 2
3 2 1 3 2
4 2 2 3 4
Update 更新
With newer version of dplyr use %>%
rather than %.%
and ungroup
is no longer needed (as per David Arenburg's comment). 随着dplyr使用的新版本
%>%
而非%.%
并ungroup
不再需要(根据大卫Arenburg的评论)。
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