This page compares the speed of R and Stata for typical data analysis. Instructions are runned on randomly generated datasets of 500 Mo (which corresponds to 1e7 observations). For each data operation, I use the fatest command available in each language. In particular, I use ftools and fastxtile in Stata. I use data.table and fst in R.
All the code below can be downloaded in the code folder in the repository. The dataset is generated in R using the file 1-generate-datasets.r. The R code in the file 2-benchmark-r.r: The Stata code in the file 3-benchmark-stata.do:
The machine used for this benchmark has a 2.9GHz i5 processor (4 cores) and a SSD disk.
The Stata version is Stata 13 MP. The R session info is
R version 3.3.0 (2016-05-03)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.12.5 (unknown)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] fst_0.7.2 statar_0.6.4 readr_1.1.1 lfe_2.5-1998
[5] Matrix_1.2-10 stringr_1.2.0 ggplot2_2.2.1 devtools_1.13.2
[9] lazyeval_0.2.0 tidyr_0.6.3 dplyr_0.7.1 data.table_1.10.4
[13] lubridate_1.6.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.11 plyr_1.8.4 bindr_0.1 tools_3.3.0
[5] digest_0.6.12 memoise_1.1.0 tibble_1.3.3 gtable_0.2.0
[9] lattice_0.20-35 pkgconfig_2.0.1 rlang_0.1.1 parallel_3.3.0
[13] bindrcpp_0.2 withr_1.0.2 hms_0.3 grid_3.3.0
[17] glue_1.1.1 R6_2.2.2 Formula_1.2-1 magrittr_1.5
[21] scales_0.4.1 matrixStats_0.52.2 assertthat_0.2.0 colorspace_1.3-2
[25] xtable_1.8-2 sandwich_2.3-4 stringi_1.1.5 munsell_0.4.3
[29] zoo_1.8-0