Looping over numbered variables within a by= call of a data table in R

Question

It could very well be something silly, but I can't find a (nonexhaustive) workaround of the following problem:

Let's set:

data<-data.table(id=c("a","a","a","b","b"),
                 x1=1:5,
                 x2=6:10,
                 x3=11:15)

I want, say, the means of each column variable grouped according to "id", then append them as a variable to the dataset. With so few, of course, we can use the syntax:

means1<-data[,mean(x1),by=id]

And similarly for x2, x3, to get:

   id x1 x2 x3  V1 V1.1 V1.2
1:  a  1  6 11 2.0  7.0 12.0
2:  a  2  7 12 2.0  7.0 12.0
3:  a  3  8 13 2.0  7.0 12.0
4:  b  4  9 14 4.5  9.5 14.5
5:  b  5 10 15 4.5  9.5 14.5

However, for larger set, we're tempted to loop. Here's what I tried first:

for(nn in 1:3){
   data<-data[data[,mean(paste("x",nn,sep="")),by=id]]
}

But this fails, I guess because even though strings are usually allowed to identify column names, the mean function tries to operate before the outer operator:

Warning messages:
1: In `[.data.table`(data, , mean(paste("x", nn, sep = "")), by = id) :
  argument is not numeric or logical: returning NA

So, a next attempt:

for(nn in 1:3){
   data<-data[data[,mean(data[[paste("x",nn,sep="")]]),by=id]]
}

However, this also fails, again because the mean operates first. (not to mention the syntax is cringe-worthy to anyone used to doing similar things in STATA) So we end up with the overall mean of each x being assigned for every id value:

   id x1 x2 x3 V1 V1.1 V1.2
1:  a  1  6 11  3    8   13
2:  a  2  7 12  3    8   13
3:  a  3  8 13  3    8   13
4:  b  4  9 14  3    8   13
5:  b  5 10 15  3    8   13

So, how might we perform this simple task in a loop?

Syntax like this has worked for me in similar calls, for example:

for(nn in 1:3){
   data[,paste("x_greater_than_4_",nn,sep=""):=(data[[paste("x",nn,sep="")]]>4)]
}

produces what I'd expect:

   id x1 x2 x3 x_greater_than_4_1 x_greater_than_4_2 x_greater_than_4_3
1:  a  1  6 11              FALSE               TRUE               TRUE
2:  a  2  7 12              FALSE               TRUE               TRUE
3:  a  3  8 13              FALSE               TRUE               TRUE
4:  b  4  9 14              FALSE               TRUE               TRUE
5:  b  5 10 15               TRUE               TRUE               TRUE

Answer 1

How about:

> data[, x1Mean := mean(x1), by=id] # this command updates the data table
> data
   id x1 x2 x3 x1Mean
1:  a  1  6 11    2.0
2:  a  2  7 12    2.0
3:  a  3  8 13    2.0
4:  b  4  9 14    4.5
5:  b  5 10 15    4.5

Answer 2

I'd first set a key using setkey and then use lapply in the j expression and self-join the result. You can use .SD in lapply and the associated .SDcols to specify columns by numeric position. Like this:

setkey( data , id )
data[ data[ , lapply( .SD , mean ) , keyby = id , .SDcols = 2:4 ] ]
#   id x1 x2 x3 x1.1 x2.1 x3.1
#1:  a  1  6 11  2.0  7.0 12.0
#2:  a  2  7 12  2.0  7.0 12.0
#3:  a  3  8 13  2.0  7.0 12.0
#4:  b  4  9 14  4.5  9.5 14.5
#5:  b  5 10 15  4.5  9.5 14.5

# If you just want the group means use this:
data[ ,  lapply( .SD , mean ), by = id , .SDcols = 2:4 ]

---

Alternatively, you can use := along with by as follows, which'll avoid the join as well:

sd_cols = c("x1", "x2", "x3")
data[, c(paste0("v", 1:3)) := lapply(.SD, mean), by=id, .SDcols=sd_cols]

#    id x1 x2 x3  v1  v2   v3
# 1:  a  1  6 11 2.0 7.0 12.0
# 2:  a  2  7 12 2.0 7.0 12.0
# 3:  a  3  8 13 2.0 7.0 12.0
# 4:  b  4  9 14 4.5 9.5 14.5
# 5:  b  5 10 15 4.5 9.5 14.5

Answer 3

Try using aggregate with the formula interface to get the group means, then merge into the original data:

merge(data,aggregate(.~id,data=data,mean),by="id",suffixes=c("",".mean"))
   id x1 x2 x3 x1.mean x2.mean x3.mean
1:  a  1  6 11     2.0     7.0    12.0
2:  a  2  7 12     2.0     7.0    12.0
3:  a  3  8 13     2.0     7.0    12.0
4:  b  4  9 14     4.5     9.5    14.5
5:  b  5 10 15     4.5     9.5    14.5