best way to transpose data.table

Question

[UPDATE: there is now a native transpose() function in data.table package]

I often need to transpose a data.table, every time it takes several lines of code and I am wondering if there's any better solution than mine.

if we take sample table

library(data.table) mydata <- data.table(col0=c("row1","row2","row3"),                      col1=c(11,21,31),                      col2=c(12,22,32),                      col3=c(13,23,33))  mydata # col0 col1 col2 col3 # row1   11   12   13 # row2   21   22   23 # row3   31   32   33 

and just transpose it with t(), it will be transposed to the matrix with conversion to character type, while applying data.table to such matrix will lose row.names:

t(mydata) # [,1]   [,2]   [,3]   # col0 "row1" "row2" "row3" # col1 "11"   "21"   "31"   # col2 "12"   "22"   "32"   # col3 "13"   "23"   "33"    data.table(t(mydata)) #   V1   V2   V3 # row1 row2 row3 #   11   21   31 #   12   22   32 #   13   23   33 

so I had to write a function for this:

tdt <- function(inpdt){   transposed <- t(inpdt[,-1,with=F]);   colnames(transposed) <- inpdt[[1]];   transposed <- data.table(transposed, keep.rownames=T);   setnames(transposed, 1, names(inpdt)[1]);   return(transposed); }   tdt(mydata) # col0 row1 row2 row3 # col1   11   21   31 # col2   12   22   32 # col3   13   23   33 

is there anything I could optimize here or do it in "nicer" way?

Answer 1

Why not just melt and dcast the data.table?

require(data.table)  dcast(melt(mydata, id.vars = "col0"), variable ~ col0) #    variable row1 row2 row3 # 1:     col1   11   21   31 # 2:     col2   12   22   32 # 3:     col3   13   23   33 

Answer 2

The current docs show a builtin transpose method.

Specifically, you can do:

transpose(mydata, keep.names = "col", make.names = "col0") ##     col row1 row2 row3 ## 1: col1   11   21   31 ## 2: col2   12   22   32 ## 3: col3   13   23   33