For this example (1 column data), you get much closer results using R's scan function rather than read.table

  awk 'END{for(i=0;i<1000000;i++){ print int(rand() * 1000000) } }' </dev/null > data.txt
   
  time datamash sstdev 1 < data.txt
  288619.72189328
  0.72s user 0.01s system 99% cpu 0.736 total

  time R --vanilla --slave -e 'sd(scan("data.txt"))'
  Read 1000000 items
  [1] 288619.7
  1.09s user 0.04s system 99% cpu 1.134 total

R read.table read performance is fairly slow by default because it has to infer the types of columns and check for inline comments, quotes ect.

This seems like a better replacement for awk and bash one-liners to me than tasks I would use R for.

For instance counting unique elements.

  #naive approach
  time (sort data.txt | uniq | wc -l)
  632209
  13.09s user 0.04s system 101% cpu 12.984 total

  #using hashing
  time (awk '!a[$0]++' data.txt | wc -l)
  632209
  1.34s user 0.03s system 100% cpu 1.360 total

  #R
  time R --vanilla --slave -e 'length(unique(scan("data.txt")))'
  Read 1000000 items
  [1] 632209
  1.20s user 0.04s system 99% cpu 1.244 total

  #datamash
  time datamash countunique 1 <data.txt
  632209
  0.83s user 0.01s system 99% cpu 0.840 total

Quite good performance in that case, although R surprised me here as well.