計算特定列的平均值

C1<-c(3,2,4,4,5) 
C2<-c(3,7,3,4,5) 
C3<-c(5,4,3,6,3) 
DF<-data.frame(ID=c("A","B","C","D","E"),C1=C1,C2=C2,C3=C3) 

DF 
    ID Type C1 C2 C3 
1 A 1 3 3 5 
2 B 2 2 7 4 
3 C 1 4 3 3 
4 D 2 4 4 6 
5 E 2 5 5 3

如何根據類型計算每列分組的均值並忽略ID列？即：計算特定列的平均值

Type C1 C2 C3 
    1 3.50 3.00 4.00 
    2 3.67 5.00 4.33

謝謝！

來源

2014-01-12 user3186691

沒有「Ty pe「，但這是一個非常簡單的」聚合「任務。 – A5C1D2H2I1M1N2O1R2T1

創建具有Type列中的數據：

DF <- read.table(header=TRUE, text=' ID Type C1 C2 C3 
1 A 1 3 3 5 
2 B 2 2 7 4 
3 C 1 4 3 3 
4 D 2 4 4 6 
5 E 2 5 5 3')

然後，用知識的ID列是在位置1 aggregate一個簡單的應用程序得到你想要的東西：

aggregate(.~Type, data=DF[-1], FUN=mean) 
    Type  C1  C2  C3 
1 1 3.500000 3.000000 4.000000 
2 2 3.666667 5.333333 4.333333

來源

2014-01-12 05:46:25

謝謝！其實我的問題並不完整，但我從你的答案中找到了答案！再次感謝！ – user3186691

其他一些方法：

### plyr was written with this type of problem in mind 
library(plyr) 
ddply(DF[-1], .(Type), colMeans) 

### staying in base; these are more unwieldly than `aggregate` 
t(sapply(split(DF[-c(1,2)], DF$Type), colMeans)) 
### `ave` also written for similar problems; however will replace all elements 
### by vector average (mean) so need to use `unique` afterwards: 
unique(with(DF, ave(C1, Type))) 
with(DF, 
    lapply(lapply(DF[-c(1,2)], ave, Type), unique) 
    ) 

### faster and scales well on large datasets 
library(data.table) 
DFt <- as.data.table(DF) 
DFt[, list(mean(C1), mean(C2), mean(C3)), by=Type]

來源

2014-01-12 07:01:48 dardisco

計算特定列的平均值

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