如何加快团队内的累积总和？

Question

我有以下数据框:

id<-c(1,1,1,1,1,3,3,3,3)
spent<-c(10,20,30,40,50,60,70,80,90)
date<-c("11-11-07","11-11-07","23-11-07","12-12-08","17-12-08","11-11-07","23-11-07","23-   11-07","16-01-08")
df<-data.frame(id,date,spent)
df$date2<-as.Date(as.character(df$date), format = "%d-%m-%y")


 id     date spent      date2
1  1 11-11-07    10 2007-11-11
2  1 11-11-07    20 2007-11-11
3  1 23-11-07    30 2007-11-23
4  1 12-12-08    40 2008-12-12
5  1 17-12-08    50 2008-12-17
6  3 11-11-07    60 2007-11-11
7  3 23-11-07    70 2007-11-23
8  3 23-11-07    80 2007-11-23
9  3 16-01-08    90 2008-01-16

我需要每天计算总和spent,id并将其包含在框架工作中,如下所示:

 id     date spent      date2    sum.spent
1  1 11-11-07    10 2007-11-11    10
2  1 11-11-07    20 2007-11-11    30 
3  1 23-11-07    30 2007-11-23    30
4  1 12-12-08    40 2008-12-12    40
5  1 17-12-08    50 2008-12-17    50
6  3 11-11-07    60 2007-11-11    60
7  3 23-11-07    70 2007-11-23    70
8  3 23-11-07    80 2007-11-23    150
9  3 16-01-08    90 2008-01-16    90 

以下脚本运行良好(除了第一行不是什么大问题):

df$spent2<-NA
for (a in 2:9)
if (df[a,1]==df[a-1,1]&& df[a,4]==df[a-1,4])
(df[a,5]=df[a,3]+df[a-1,3])else(df[a,5]=df[a,3])

但是,由于我的实际数据集中的行数约为150万,因此上述脚本大约需要5天才能执行.我想知道你是否可以建议一种更有效的方法来编写这个代码并实现相同的目标.

Answer 1

data.table非常快,特别是对于如此大的数据集.对于1.5密耳的记录,这应该非常快.

library(data.table)
df <- data.table(df)
df <- df[, sum.spent:=cumsum(spent), by = list(id, date2)]