如何在R数据帧上执行Oracle ROW_NUMBER(),RANK()或DENSE_RANK()函数(见
http://www.orafaq.com/node/55)等分析函数？ CRAN包“plyr”非常接近但仍然不同.
解决方法
data.table包,特别是从1.8.1版开始,提供了sql术语中分区的大部分功能. R中的rank(x,ties.method =“min”)类似于Oracle RANK(),并且有一种使用因子(如下所述)来模拟DENSE_RANK()函数的方法.一个模仿ROW_NUMBER的方法应该是显而易见的.
以下是一个示例：从R-Forge加载最新版本的data.table：
install.packages("data.table",repos= c("http://R-Forge.R-project.org",getOption("repos")))
library(data.table)
创建一些示例数据：
set.seed(10)
DT<-data.table(ID=seq_len(4*3),group=rep(1:4,each=3),value=rnorm(4*3),info=c(sample(c("a","b"),4*2,replace=TRUE),sample(c("c","d"),4,replace=TRUE)),key="ID")
> DT
ID group value info
1: 1 1 0.01874617 a
2: 2 1 -0.18425254 b
3: 3 1 -1.37133055 b
4: 4 2 -0.59916772 a
5: 5 2 0.29454513 b
6: 6 2 0.38979430 a
7: 7 3 -1.20807618 b
8: 8 3 -0.36367602 a
9: 9 3 -1.62667268 c
10: 10 4 -0.25647839 d
11: 11 4 1.10177950 c
12: 12 4 0.75578151 d
通过减小组内的值排序每个ID(注意 – 在值前面,表示递减顺序)：
> DT[,valRank:=rank(-value),by="group"]
ID group value info valRank
1: 1 1 0.01874617 a 1
2: 2 1 -0.18425254 b 2
3: 3 1 -1.37133055 b 3
4: 4 2 -0.59916772 a 3
5: 5 2 0.29454513 b 2
6: 6 2 0.38979430 a 1
7: 7 3 -1.20807618 b 2
8: 8 3 -0.36367602 a 1
9: 9 3 -1.62667268 c 3
10: 10 4 -0.25647839 d 3
11: 11 4 1.10177950 c 1
12: 12 4 0.75578151 d 2
对于DENSE_RANK(),该值的值被排序,您可以将该值转换为一个因子,然后返回基础整数值.例如,根据组内的信息(比较infoRank与infoRankDense)对每个ID排序：
DT[,infoRank:=rank(info,ties.method="min"),by="group"]
DT[,infoRankDense:=as.integer(factor(info)),by="group"]
R> DT
ID group value info valRank infoRank infoRankDense
1: 1 1 0.01874617 a 1 1 1
2: 2 1 -0.18425254 b 2 2 2
3: 3 1 -1.37133055 b 3 2 2
4: 4 2 -0.59916772 a 3 1 1
5: 5 2 0.29454513 b 2 3 2
6: 6 2 0.38979430 a 1 1 1
7: 7 3 -1.20807618 b 2 2 2
8: 8 3 -0.36367602 a 1 1 1
9: 9 3 -1.62667268 c 3 3 3
10: 10 4 -0.25647839 d 3 2 2
11: 11 4 1.10177950 c 1 1 1
12: 12 4 0.75578151 d 2 2 2
附：马修·道尔
LEAD和LAG
为了模仿LEAD和LAG,从here提供的答案开始.我将根据组内的ID顺序创建一个等级变量.这对于假冒数据并不是必需的,但是如果ID在组内不是顺序的,那么这会使生活更困难.所以这里有一些新的假数据与非顺序ID：
set.seed(10)
DT<-data.table(ID=sample(seq_len(4*3)),key="ID")
DT[,idRank:=rank(ID),by="group"]
setkey(DT,group,idRank)
> DT
ID group value info idRank
1: 4 1 -0.36367602 b 1
2: 5 1 -1.62667268 b 2
3: 7 1 -1.20807618 b 3
4: 1 2 1.10177950 a 1
5: 2 2 0.75578151 a 2
6: 12 2 -0.25647839 b 3
7: 3 3 0.74139013 c 1
8: 6 3 0.98744470 b 2
9: 9 3 -0.23823356 a 3
10: 8 4 -0.19515038 c 1
11: 10 4 0.08934727 c 2
12: 11 4 -0.95494386 c 3
然后获取前一个记录的值,使用组和idRank变量,并从idRank中减去1,并使用multi =’last’参数.要从上面记录的两个条目获取值,减去2.
DT[,prev:=DT[J(group,idRank-1),value,mult='last']]
DT[,prev2:=DT[J(group,idRank-2),mult='last']]
ID group value info idRank prev prev2
1: 4 1 -0.36367602 b 1 NA NA
2: 5 1 -1.62667268 b 2 -0.36367602 NA
3: 7 1 -1.20807618 b 3 -1.62667268 -0.3636760
4: 1 2 1.10177950 a 1 NA NA
5: 2 2 0.75578151 a 2 1.10177950 NA
6: 12 2 -0.25647839 b 3 0.75578151 1.1017795
7: 3 3 0.74139013 c 1 NA NA
8: 6 3 0.98744470 b 2 0.74139013 NA
9: 9 3 -0.23823356 a 3 0.98744470 0.7413901
10: 8 4 -0.19515038 c 1 NA NA
11: 10 4 0.08934727 c 2 -0.19515038 NA
12: 11 4 -0.95494386 c 3 0.08934727 -0.1951504
对于LEAD,将适当的偏移添加到idRank变量并切换到multi =’first’：
DT[,nex:=DT[J(group,idRank+1),mult='first']]
DT[,nex2:=DT[J(group,idRank+2),mult='first']]
ID group value info idRank prev prev2 nex nex2
1: 4 1 -0.36367602 b 1 NA NA -1.62667268 -1.2080762
2: 5 1 -1.62667268 b 2 -0.36367602 NA -1.20807618 NA
3: 7 1 -1.20807618 b 3 -1.62667268 -0.3636760 NA NA
4: 1 2 1.10177950 a 1 NA NA 0.75578151 -0.2564784
5: 2 2 0.75578151 a 2 1.10177950 NA -0.25647839 NA
6: 12 2 -0.25647839 b 3 0.75578151 1.1017795 NA NA
7: 3 3 0.74139013 c 1 NA NA 0.98744470 -0.2382336
8: 6 3 0.98744470 b 2 0.74139013 NA -0.23823356 NA
9: 9 3 -0.23823356 a 3 0.98744470 0.7413901 NA NA
10: 8 4 -0.19515038 c 1 NA NA 0.08934727 -0.9549439
11: 10 4 0.08934727 c 2 -0.19515038 NA -0.95494386 NA
12: 11 4 -0.95494386 c 3 0.08934727 -0.1951504 NA NA