我可以在单个dplyr语句中切换分组变量吗? [英] can I switch the grouping variable in a single dplyr statement?
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问题描述
下面是一个简单的例子来说明问题:
Here's a simple example to illustrate the issue:
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
尝试在 dplyr中执行相同操作
我失败,因为第一个
: group_by
是持久的,分组由 b
code> c
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
:
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
这是错误还是功能?如果它是一个特性,那么如何在一个语句中复制 data.table
解决方案?
Is this a bug or a feature? If it's a feature, then how would one replicate the data.table
solution in a single statement?
推荐答案
尝试:
> 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
更新
使用较新版本的dplyr使用%>%
,而不是%。 / code>和
ungroup
不再需要(按照David Arenburg的评论)。
With newer version of dplyr use %>%
rather than %.%
and ungroup
is no longer needed (as per David Arenburg's comment).
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