如何使用 R 数据框中的两个因子列来查找累积和?
r programmingserver side programmingprogramming更新于 2025/4/11 6:52:17
通常，累积和是针对单个变量计算的，在某些情况下是基于单个分类变量计算的，我们很少需要对两个分类变量进行计算。如果我们想为两个分类变量找到它，那么我们需要将数据框转换为 data.table 对象，并使用 cumsum 函数定义具有累积和的列。
示例
考虑以下数据框:
> set.seed(1361)
> Factor1<-as.factor(sample(LETTERS[1:4],20,replace=TRUE))
> Factor2<-as.factor(sample(c("T1","T2","T3","T4"),20,replace=TRUE))
> Response<-rpois(20,5)
> df1<-data.frame(Factor1,Factor2,Response)
> df1
输出
Factor1 Factor2 Response 1 A T2 9 2 B T1 8 3 B T1 2 4 A T2 3 5 B T1 7 6 B T2 7 7 D T2 7 8 D T4 7 9 C T4 6 10 B T1 6 11 A T2 4 12 A T2 4 13 C T1 7 14 B T3 1 15 A T3 6 16 D T1 3 17 B T1 8 18 D T4 5 19 D T2 3 20 C T1 4
加载 data.table 包:
> library(data.table)
将数据框 df1 转换为 data.table 对象:
> dt1<-data.table(df1)
根据 Factor1 和 Factor2 创建具有累积总和的 CumulativeSums 列:
示例
> dt1[,CumulativeSums:=cumsum(Response),by=list(Factor1,Factor2)] > dt1
输出
Factor1 Factor2 Response CumulativeSums 1: A T2 9 9 2: B T1 8 8 3: B T1 2 10 4: A T2 3 12 5: B T1 7 17 6: B T2 7 7 7: D T2 7 7 8: D T4 7 7 9: C T4 6 6 10: B T1 6 23 11: A T2 4 16 12: A T2 4 20 13: C T1 7 7 14: B T3 1 1 15: A T3 6 6 16: D T1 3 3 17: B T1 8 31 18: D T4 5 12 19: D T2 3 10 20: C T1 4 11
我们来看另一个例子:
示例
> G1<-as.factor(sample(c("Hot","Cold"),20,replace=TRUE))
> G2<-as.factor(sample(c("Low","Medium","Large"),20,replace=TRUE))
> Y<-sample(1:100,20)
> df2<-data.frame(G1,G2,Y)
> df2
输出
G1 G2 Y 1 Hot Medium 60 2 Cold Low 94 3 Hot Low 22 4 Cold Medium 90 5 Hot Medium 16 6 Hot Large 32 7 Cold Low 44 8 Hot Low 73 9 Hot Medium 99 10 Hot Medium 68 11 Cold Medium 41 12 Cold Large 77 13 Cold Large 48 14 Cold Medium 20 15 Cold Medium 18 16 Cold Low 12 17 Cold Low 30 18 Hot Low 23 19 Cold Medium 26 20 Cold Medium 4
示例
> dt2<-data.table(df2) > dt2[,CumulativeSums:=cumsum(Y),by=list(G1,G2)] > dt2
输出
G1 G2 Y CumulativeSums 1: Hot Medium 60 60 2: Cold Low 94 94 3: Hot Low 22 22 4: Cold Medium 90 90 5: Hot Medium 16 76 6: Hot Large 32 32 7: Cold Low 44 138 8: Hot Low 73 95 9: Hot Medium 99 175 10: Hot Medium 68 243 11: Cold Medium 41 131 12: Cold Large 77 77 13: Cold Large 48 125 14: Cold Medium 20 151 15: Cold Medium 18 169 16: Cold Low 12 150 17: Cold Low 30 180 18: Hot Low 23 118 19: Cold Medium 26 195 20: Cold Medium 4 199
