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How to avoid the same column names with multiple conversions in data.table?

I tried to do some conversions for the same columns in data.table and found this answer . However, if I follow the steps, I get the same 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)), by = 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 a way to avoid this behavior and get different column names for different transformations? I am using the latest (1.9.4) stable version of data.table .

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2 answers

You can 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 the option suggested by @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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If the data is not that big and focus on readability, using dplyr also be a good idea.

 library(dplyr) dt %>% group_by(ID) %>% summarise_each(funs(mean, sd)) # ID Obs_1_mean Obs_2_mean Obs_3_mean Obs_1_sd Obs_2_sd Obs_3_sd #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

(As @akrun pointed out, this will not work if you use only one function in funs() .)

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