Summarizing multiple columns with data.table
Rdata.tableR Problem Overview
I'm trying to use data.table to speed up processing of a large data.frame (300k x 60) made of several smaller merged data.frames. I'm new to data.table. The code so far is as follows
library(data.table)
a = data.table(index=1:5,a=rnorm(5,10),b=rnorm(5,10),z=rnorm(5,10))
b = data.table(index=6:10,a=rnorm(5,10),b=rnorm(5,10),c=rnorm(5,10),d=rnorm(5,10))
dt = merge(a,b,by=intersect(names(a),names(b)),all=T)
dt$category = sample(letters[1:3],10,replace=T)
and I wondered if there was a more efficient way than the following to summarize the data.
summ = dt[i=T,j=list(a=sum(a,na.rm=T),b=sum(b,na.rm=T),c=sum(c,na.rm=T),
d=sum(d,na.rm=T),z=sum(z,na.rm=T)),by=category]
I don't really want to type all 50 column calculations by hand and a eval(paste(...))
seems clunky somehow.
I had a look at the example below but it seems a bit complicated for my needs. thanks
R Solutions
Solution 1 - R
You can use a simple lapply
statement with .SD
dt[, lapply(.SD, sum, na.rm=TRUE), by=category ]
category index a b z c d
1: c 19 51.13289 48.49994 42.50884 9.535588 11.53253
2: b 9 17.34860 20.35022 10.32514 11.764105 10.53127
3: a 27 25.91616 31.12624 0.00000 29.197343 31.71285
If you only want to summarize over certain columns, you can add the .SDcols
argument
# note that .SDcols also allows reordering of the columns
dt[, lapply(.SD, sum, na.rm=TRUE), by=category, .SDcols=c("a", "c", "z") ]
category a c z
1: c 51.13289 9.535588 42.50884
2: b 17.34860 11.764105 10.32514
3: a 25.91616 29.197343 0.00000
This of course, is not limited to sum
and you can use any function with lapply
, including anonymous functions. (ie, it's a regular lapply
statement).
Lastly, there is no need to use i=T
and j= <..>
. Personally, I think that makes the code less readable, but it is just a style preference.
Documentation
See ?.SD
, ?data.table
and its .SDcols
argument, and the vignette Using .SD for Data Analysis.
Also have a look at data.table
FAQ 2.1.