dplyr 中每组的 r cumsum [英] r cumsum per group in dplyr
问题描述
我开始喜欢 dplyr，但我遇到了一个用例.我希望能够将 cumsum 应用到带有包的数据框中的每个组中，但我似乎无法正确使用.
I am starting to enjoy dplyr but I got stuck on a use case. I want to be able to apply cumsum per group in a dataframe with the package but I can't seem to get it right.
对于演示数据框，我生成了以下数据:
For a demo dataframe I've generated the following data:
set.seed(123)
len = 10
dates = as.Date('2014-01-01') + 1:len
grp_a = data.frame(dates=dates, group='A', sales=rnorm(len))
grp_b = data.frame(dates=dates, group='B', sales=rnorm(len))
grp_c = data.frame(dates=dates, group='C', sales=rnorm(len))
df = rbind(grp_a, grp_b, grp_c)
这将创建一个如下所示的数据框:
This creates a dataframe that looks like:
dates group sales
1 2014-01-02 A -0.56047565
2 2014-01-03 A -0.23017749
3 2014-01-04 A 1.55870831
4 2014-01-05 A 0.07050839
5 2014-01-06 A 0.12928774
6 2014-01-02 B 1.71506499
7 2014-01-03 B 0.46091621
8 2014-01-04 B -1.26506123
9 2014-01-05 B -0.68685285
10 2014-01-06 B -0.44566197
11 2014-01-02 C 1.22408180
12 2014-01-03 C 0.35981383
13 2014-01-04 C 0.40077145
14 2014-01-05 C 0.11068272
15 2014-01-06 C -0.55584113
然后我继续创建一个用于绘图的数据框，但是我想用更干净的东西替换一个 for 循环.
I then go on to create a dataframe for plotting, but with a for loop that I'd like to replace with something cleaner.
pdf = data.frame(dates=as.Date(as.character()), group=as.character(), sales=as.numeric())
for(grp in unique(df$group)){
subs = filter(df, group == grp) %>% arrange(dates)
pdf = rbind(pdf, data.frame(dates=subs$dates, group=grp, sales=cumsum(subs$sales)))
}
我使用这个 pdf 来创建一个情节.
I use this pdf to create a plot.
p = ggplot()
p = p + geom_line(data=pdf, aes(dates, sales, colour=group))
p + ggtitle("sales per group")
有没有更好的方法(使用 dplyr 方法的方法)来创建此数据框?我查看了 summarize 方法，但这似乎从 N 项 -> 1 项中聚合了一组.这个用例目前似乎打破了我的 dplyr 流程.有什么建议可以更好地解决这个问题吗?
Is there a better way (a way by using the dplyr methods) to create this dataframe? I've looked at the summarize method but this seems to aggregate a group from N items -> 1 item. This use case seems to break my dplyr flow at the moment. Any suggestions to better approach this?
推荐答案
啊.在摆弄之后我似乎找到了它.
Ah. After fiddling around I seem to have found it.
pdf = df %>% group_by(group) %>% arrange(dates) %>% mutate(cs = cumsum(sales))
有问题的 forloop 输出:
> pdf = data.frame(dates=as.Date(as.character()), group=as.character(), sales=as.numeric())
> for(grp in unique(df$group)){
+ subs = filter(df, group == grp) %>% arrange(dates)
+ pdf = rbind(pdf, data.frame(dates=subs$dates, group=grp, sales=subs$sales, cs=cumsum(subs$sales)))
+ }
> pdf
dates group sales cs
1 2014-01-02 A -0.56047565 -0.5604756
2 2014-01-03 A -0.23017749 -0.7906531
3 2014-01-04 A 1.55870831 0.7680552
4 2014-01-05 A 0.07050839 0.8385636
5 2014-01-06 A 0.12928774 0.9678513
6 2014-01-02 B 1.71506499 1.7150650
7 2014-01-03 B 0.46091621 2.1759812
8 2014-01-04 B -1.26506123 0.9109200
9 2014-01-05 B -0.68685285 0.2240671
10 2014-01-06 B -0.44566197 -0.2215949
11 2014-01-02 C 1.22408180 1.2240818
12 2014-01-03 C 0.35981383 1.5838956
13 2014-01-04 C 0.40077145 1.9846671
14 2014-01-05 C 0.11068272 2.0953498
15 2014-01-06 C -0.55584113 1.5395087
用这行代码输出:
> pdf = df %>% group_by(group) %>% mutate(cs = cumsum(sales))
> pdf
Source: local data frame [15 x 4]
Groups: group
dates group sales cs
1 2014-01-02 A -0.56047565 -0.5604756
2 2014-01-03 A -0.23017749 -0.7906531
3 2014-01-04 A 1.55870831 0.7680552
4 2014-01-05 A 0.07050839 0.8385636
5 2014-01-06 A 0.12928774 0.9678513
6 2014-01-02 B 1.71506499 1.7150650
7 2014-01-03 B 0.46091621 2.1759812
8 2014-01-04 B -1.26506123 0.9109200
9 2014-01-05 B -0.68685285 0.2240671
10 2014-01-06 B -0.44566197 -0.2215949
11 2014-01-02 C 1.22408180 1.2240818
12 2014-01-03 C 0.35981383 1.5838956
13 2014-01-04 C 0.40077145 1.9846671
14 2014-01-05 C 0.11068272 2.0953498
15 2014-01-06 C -0.55584113 1.5395087
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