r - How to avoid same column names when multiple transformations in data.table? -


i tried make multiple transformations same columns in data.table , found this answer. however, if follow steps there identical column names (instead of mean.obs_1, etc.).

library(data.table) set.seed(1) dt = data.table(id=c(1:3), obs_1=rnorm(9), obs_2=rnorm(9), obs_3=rnorm(9))  dt[, c(mean = lapply(.sd, mean), sd = lapply(.sd, sd)), = id] #   id      obs_1      obs_2      obs_3     obs_1     obs_2     obs_3 #1:  1  0.4854187 -0.3238542  0.7410611 1.1108687 0.2885969 0.1067961 #2:  2  0.4171586 -0.2397030  0.2041125 0.2875411 1.8732682 0.3438338 #3:  3 -0.3601052  0.8195368 -0.4087233 0.8105370 0.3829833 1.4705692

is there way avoid behavior , different column names different transformations? use latest (1.9.4) stable version of data.table.

you try

library(data.table) dt[, unlist(lapply(.sd, function(x) list(mean=mean(x),                     sd=sd(x))),recursive=false), by=id] #   id obs_1.mean  obs_1.sd obs_2.mean  obs_2.sd obs_3.mean  obs_3.sd #1:  1  0.4854187 1.1108687 -0.3238542 0.2885969  0.7410611 0.1067961 #2:  2  0.4171586 0.2875411 -0.2397030 1.8732682  0.2041125 0.3438338 #3:  3 -0.3601052 0.8105370  0.8195368 0.3829833 -0.4087233 1.4705692

or variation suggested @david arenburg

 dt[, as.list(unlist(lapply(.sd, function(x) list(mean=mean(x),               sd=sd(x))))), by=id]  #   id obs_1.mean  obs_1.sd obs_2.mean  obs_2.sd obs_3.mean  obs_3.sd  #1:  1  0.4854187 1.1108687 -0.3238542 0.2885969  0.7410611 0.1067961  #2:  2  0.4171586 0.2875411 -0.2397030 1.8732682  0.2041125 0.3438338  #3:  3 -0.3601052 0.8105370  0.8195368 0.3829833 -0.4087233 1.4705692

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