R summarizing multiple columns with data.table -
i'm trying use data.table speed processing of large data.frame (300k x 60) made of several smaller merged data.frames. i'm new data.table. code far follows
library(data.table) = 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 wondered if there more efficient way following summarize 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 want type 50 column calculations hand , eval(paste(...)) seems clunky somehow.
i had @ example below seems bit complicated needs. thanks
you can use simple lapply statement .sd
dt[, lapply(.sd, sum, na.rm=true), by=category ] category index 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: 27 25.91616 31.12624 0.00000 29.197343 31.71285 if want summarize on columns, can add .sdcols argument
# note .sdcols allows reordering of columns dt[, lapply(.sd, sum, na.rm=true), by=category, .sdcols=c("a", "c", "z") ] category c z 1: c 51.13289 9.535588 42.50884 2: b 17.34860 11.764105 10.32514 3: 25.91616 29.197343 0.00000 this of course, not limited sum , can use function lapply, including anonymous functions. (ie, it's regular lapply statement).
lastly, there no need use i=t , j= <..>. personally, think makes code less readable, style preference.
edit: documentation
you find documentation .sdand several other special variables under the
section of ?"[.data.table" (in arguments section, under info by).
also have @ data.table faq 2.1
Comments
Post a Comment