在dplyr中，每组都有一个cumsum [英] r cumsum per group in dplyr
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问题描述
我开始享受 dplyr ，但我陷入了一个用例。我希望能够在包中使用数据框中的每个组应用 cumsum ，但是我似乎无法做到。
对于演示数据框，我生成了以下数据：
set.seed（123）
len = 10
dates = as.Date（'2014-01-01'）+ 1：len
grp_a = data.frame（日期=日期，组='A'，销售= rnorm（len））
grp_b = data.frame（日期=日期，group ='B'，销售= rnorm（len））
grp_c = data.frame（日期=日期，组='C'，销售= rnorm（len））
df = rbind（grp_a，grp_b，grp_c）
这会创建一个如下所示的数据框：
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
然后我创建一个用于绘图的数据框，但是用一个for循环来代替更干净的东西。
pdf = data.frame（dates = as.Date（as.character（）），group = as.character（） ，销售= as.numeric（））pre>
for（grp in unique（df $ group））{
subs = filter（df，group == grp）％>％排列（日期）
pdf = rbind（pdf，data.frame（dates = subs $ dates，group = grp，sales = cumsum（subs $ sales）））
}
我用这个
p = ggplot（）
p = p + geom_line（data = pdf，aes（dates，sales，color =组））
p + ggtitle（每组销售额）
有没有更好的方法dplyr方法）来创建这个数据帧？我查看了
summarize方法，但是这似乎是从N个项目 - > 1个项目中汇总一个组。这个用例似乎目前打破了我的dplyr流程。任何建议，以更好地处理这个？
解决方案啊。摆弄后我似乎找到了它。
pdf = df％>％group_by（group）％>％排列（日期）％>％mutate（cs = cumsum（sales））
使用forloop进行输出：
> 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 （日期= subs $日期，group = grp，sales = subs $ sales，cs = cumsum（subs $销售）））
+}
日期团体销售额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
输出这行代码：
> pdf = df％>％group_by（group）％>％mutate（cs = cumsum（sales））
来源：本地资料框[15 x 4]
团体：团体
日期团体销售额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
I am starting to enjoy
dplyrbut I got stuck on a use case. I want to be able to applycumsumper group in a dataframe with the package but I can't seem to get it right.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)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 -0.55584113I 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))) }I use this
p = ggplot() p = p + geom_line(data=pdf, aes(dates, sales, colour=group)) p + ggtitle("sales per group")Is there a better way (a way by using the dplyr methods) to create this dataframe? I've looked at the
summarizemethod 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?解决方案Ah. After fiddling around I seem to have found it.
pdf = df %>% group_by(group) %>% arrange(dates) %>% mutate(cs = cumsum(sales))
Output with forloop in question:
> 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
Output with this line of code:
> 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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