How to group data.table by multiple columns?

Question

I'm using the data.table package to speed up some summary statistic collection on a data set.

I'm curious if there's a way to group by more than one column. My data looks like this:

  purchaseAmt        adShown        url
   15.54            00001         150000001
    4.82            00002         150000001
  157.99            05005         776300044
   ...               ...            ...

I can do something like this:

adShownMedian <- df1[,median(purchaseAmt),by="adShown"]

to get each ad's median. How would I do something that combines adShown and url?

I've tried this:

adShownMedian <- df1[,median(purchaseAmt),by=c("adShown","url")]

but no luck.

Any suggestions?

Answer 1

Use by=list(adShown,url) instead of by=c("adShown","url")

Example:

set.seed(007)  DF <- data.frame(X=1:20, Y=sample(c(0,1), 20, TRUE), Z=sample(0:5, 20, TRUE))  library(data.table) DT <- data.table(DF) DT[, Mean:=mean(X), by=list(Y, Z)]        X Y Z      Mean  1:  1 1 3  1.000000  2:  2 0 1  9.333333  3:  3 0 5  7.400000  4:  4 0 5  7.400000  5:  5 0 5  7.400000  6:  6 1 0  6.000000  7:  7 0 3  7.000000  8:  8 1 2 12.500000  9:  9 0 5  7.400000 10: 10 0 2 15.000000 11: 11 0 4 14.500000 12: 12 0 1  9.333333 13: 13 1 1 13.000000 14: 14 0 1  9.333333 15: 15 0 2 15.000000 16: 16 0 5  7.400000 17: 17 1 2 12.500000 18: 18 0 4 14.500000 19: 19 1 5 19.000000 20: 20 0 2 15.000000 

Answer 2

To add on Jilber Urbina answer, and address kahlo comment:
if you want to get a single row for each Y - Z combination with the aggregated values you can do

DT[, .(X=mean(X)), by=list(Y, Z)]

that is the same as doing

DT[, .(X=mean(X)), by=.(Y, Z)] 
# or
DT[, .(X=mean(X)), by=c('Y','Z')]
# or specify column names in vector
names = c('Y','Z')
DT[, .(X=mean(X)), by=names] 

(data.table version 1.12.6)