statable is an R interface to Stata that allows you to combine the strengths of both R and Stata into one seamless workflow. You can prepare and manipulate your data in R, then quickly switch to Stata to perform specialised analyses, all without leaving your R environment. This integration streamlines your work, saves time and reduces the need to manually transfer data between software.
statable provides the following functionality:
- Run Stata commands from R
- Transfer datasets between R and Stata
- Run Stata “chunks” in knitr or R Markdown documents
- Work with multiple Stata sessions at the same time
Installation
statable is not currently on CRAN. You can install the development version from GitHub with:
# install.packages("pak")
pak::pak("james-atkins/statable")Usage
For more information, please read the documentation.
# First, load the package. All statable commands start with `stata_`.
library(statable)
# statable should find Stata automatically but, if necessary, you can
# manually set the path to the Stata executable
# stata_path("C:/Program Files/Stata18/StataSE-64.exe")
# `stata_run()` runs Stata commands in the same session.
# All your data and results stay in one place, allowing you to easily build on
# your previous commands.
stata_run("sysuse auto")
#> . sysuse auto
#> (1978 Automobile Data)
stata_run(c("summarize", "regress price length weight"))
#> . summarize
#>
#> Variable | Obs Mean Std. Dev. Min Max
#> -------------+---------------------------------------------------------
#> make | 0
#> price | 74 6165.257 2949.496 3291 15906
#> mpg | 74 21.2973 5.785503 12 41
#> rep78 | 69 3.405797 .9899323 1 5
#> headroom | 74 2.993243 .8459948 1.5 5
#> -------------+---------------------------------------------------------
#> trunk | 74 13.75676 4.277404 5 23
#> weight | 74 3019.459 777.1936 1760 4840
#> length | 74 187.9324 22.26634 142 233
#> turn | 74 39.64865 4.399354 31 51
#> displacement | 74 197.2973 91.83722 79 425
#> -------------+---------------------------------------------------------
#> gear_ratio | 74 3.014865 .4562871 2.19 3.89
#> foreign | 74 .2972973 .4601885 0 1
#> . regress price length weight
#>
#> Source | SS df MS Number of obs = 74
#> -------------+---------------------------------- F(2, 71) = 18.91
#> Model | 220725280 2 110362640 Prob > F = 0.0000
#> Residual | 414340116 71 5835776.28 R-squared = 0.3476
#> -------------+---------------------------------- Adj R-squared = 0.3292
#> Total | 635065396 73 8699525.97 Root MSE = 2415.7
#>
#> ------------------------------------------------------------------------------
#> price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
#> -------------+----------------------------------------------------------------
#> length | -97.96031 39.1746 -2.50 0.015 -176.0722 -19.84838
#> weight | 4.699065 1.122339 4.19 0.000 2.461184 6.936946
#> _cons | 10386.54 4308.159 2.41 0.019 1796.316 18976.76
#> ------------------------------------------------------------------------------
# To pass data from R to Stata, you can use the function `stata_data_in()`
data("mtcars")
stata_data_in(mtcars, clear = TRUE)
#> . use "$R_data_frame", clear
stata_run("describe")
#> . describe
#>
#> Contains data from /tmp/RtmpbSZnLo/statableab3f8474947a6/data_frameab3f833a5346
#> > 9.dta
#> obs: 32
#> vars: 11 24 Aug 2024 12:48
#> -------------------------------------------------------------------------------
#> storage display value
#> variable name type format label variable label
#> -------------------------------------------------------------------------------
#> mpg double %10.0g
#> cyl double %10.0g
#> disp double %10.0g
#> hp double %10.0g
#> drat double %10.0g
#> wt double %10.0g
#> qsec double %10.0g
#> vs double %10.0g
#> am double %10.0g
#> gear double %10.0g
#> carb double %10.0g
#> -------------------------------------------------------------------------------
#> Sorted by:
# Likewise, `stata_data_out()` returns the current Stata dataset into R
stata_run("rename mpg miles_per_gallon")
#> . rename mpg miles_per_gallon
mtcars_stata <- stata_data_out()
#> . save "/tmp/RtmpbSZnLo/statableab3f8474947a6/stataab3f87a311d3a.dta"
#> file /tmp/RtmpbSZnLo/statableab3f8474947a6/stataab3f87a311d3a.dta saved
mean(mtcars_stata$miles_per_gallon)
#> [1] 20.09062
# You can also reference R data frames using Stata globals of the form
# `$R_dataframe_name`.
# This allows you to use data in R with a variety of Stata commands, such as
# `merge`, `joinby` or `append`.
stata_run("use $R_mtcars, clear")
#> . use $R_mtcars, clear
stata_run("summarize")
#> . summarize
#>
#> Variable | Obs Mean Std. Dev. Min Max
#> -------------+---------------------------------------------------------
#> mpg | 32 20.09062 6.026948 10.4 33.9
#> cyl | 32 6.1875 1.785922 4 8
#> disp | 32 230.7219 123.9387 71.1 472
#> hp | 32 146.6875 68.56287 52 335
#> drat | 32 3.596563 .5346787 2.76 4.93
#> -------------+---------------------------------------------------------
#> wt | 32 3.21725 .9784574 1.513 5.424
#> qsec | 32 17.84875 1.786943 14.5 22.9
#> vs | 32 .4375 .5040161 0 1
#> am | 32 .40625 .4989909 0 1
#> gear | 32 3.6875 .7378041 3 5
#> -------------+---------------------------------------------------------
#> carb | 32 2.8125 1.6152 1 8
stata_run("append using $R_mtcars")
#> . append using $R_mtcars
stata_run("display _N")
#> . display _N
#> 64knitr
statable integrates with knitr allowing you to run Stata code chunks in knitr or rmarkdown documents. To do so, you must first import the package with library(statable) so knitr knows what to do with stata chunks. Then add a chunk like the following.