In alekrutkowski/RStataLink: Tools for calling Stata from R interactively

Features

Execute smaller or larger bits and pieces of Stata code interactively from R in a Stata "server" i.e. not in a batch mode:

avoid repeated costly operations, e.g. open Stata and load data in Stata only once and do subsequent operations requiring R<->Stata interaction without re-opening and re-loading
easy R<->Stata data exchange (import/export)
easy Stata r() and e() "results" extraction (macros, scalars, matrices, coefficients and standard errors), shaped for convenient consumption in R
concurrency "primitive" (future-like) and parallel apply()-style functions for executing Stata code in a concurrent/parallel manner in a "cluster" of multiple Stata instances (regular and load-balancing versions)
should work cross-platform (Windows, Linux, possibly Mac too) -- pure R and pure Stata code

Make your work with Stata more functional!

Similar but unrelated project: https://github.com/lbraglia/RStata. It seems to offer only batch mode i.e. not interactive.

How it works

Stata "server" is a Stata instance running an infinite loop and waiting for new jobs showing up in a specific directory/folder. Thus, the R<->Stata communication is disk-based so it can take place only locally (within a single computer) or through a shared network drive (across computers).

In the latter case (the shared drive approach), Stata would still need to be opened by R with startStata() or startStataCluster() on the "server" computer (e.g. via SSH). The generated StataID object(s) (see i or cl below) need(s) to be serialised (e.g. with saveRDS()) and transmitted in some way to a remote "client" computer to be deserialised (e.g. with readRDS()). The entire path to the shared network drive directory (see argument compath in startStata()) should be the same on both computers ("server" and "client").

Installation

if ('remotes' %in% installed.packages()[,"Package"])
    remotes::install_github('alekrutkowski/RStataLink', INSTALL_opts="--no-staged-install") else
    stop('You need package "remotes"!')

Usage examples

Tell R where Stata is (can be also per-call -- using argument start_cmd in startStata() -- but that would be less convenient):

# A virtualised app in my case, therefore such a complicated path, should be simpler normally:
options(statapath='C:/ProgramData/Microsoft/AppV/Client/Integration/C8737350-E2E4-4B3E-A45D-5D2C0B8150FC/Root/StataMP-64.exe')

A single Stata instance functionality demo

For the Stata code pieces use a string with a newline character (\n), or multi-line string, or a character vector (that will be converted to a multi-line string):

library(RStataLink)
i <- startStata()
i
r1 <- doInStata(i, 'display 100 + 15.7', results = NULL)
r1  # results = NULL means no import of Stata e() or r() results
# Use in Stata a built-in demo dataset on cars
# and make a simple regression with robust standard errors
# (the 3 lines of code could be also done with 3 consecutive doInStata calls)
r2 <- doInStata(i,
                'sysuse auto, clear
                regress price weight trunk, robust
                ereturn list')
r2$log
r2$results  # this (below) looks similar to "ereturn list" in Stata (above)
# Use in Stata an R demo dataset on flowers
# modify it (temporarily in Stata with preserve...restore,
# see http://www.stata.com/help.cgi?preserve)
# and do a simple regression:
data(iris)
r3 <- doInStata(i, code = 
                'describe
                gen ln_sepallength = log(sepallength)
                reg ln_sepallength sepalwidth petallength petalwidth',
                df = iris,
                preserve_restore = TRUE)
r3$log
# If data is exported to Stata with df = ...
# a (possibly modified) Stata dataset is imported back
# into R unless argument import_df = FALSE is
# specified in doInStata()
str(r3$df)
r3$results$e_class  # again, the estimated results from e() are available:
# Since we did preserve...restore in Stata
# while operating on the iris data,
# the data on cars is still there
doInStata(i, 'describe', results=NULL)
# Also r-class Stata results can be collected:
r4 <- doInStata(i, 'summarize price \n return list',
                results = 'r')  # Stata r-class results only
r4
# A non-blocking call to Stata -- perform some long-running job
system.time({f1 <- doInStata(i, 'sleep 6000 // in milliseconds in Stata
                            display "hello"',
                            future = TRUE,
                            results = NULL)})
# do some work in R in the meantime:
Sys.sleep(2)
# collect the results from Stata if ready
# (if not ready, you must wait -- it's blocking this time,
# note the approximate time difference: 6 - 2 = 4):
system.time({r5 <- getStataFuture(f1)})
r5
# You can avoid being blocked by an undelivered future by
# preceding the extraction attempt with an isStataReady() check:
f2 <- doInStata(i, 'sleep 2000 // in milliseconds in Stata
                            display "hello2"',
                            future = TRUE,
                            results = NULL)
system.time(if (isStataReady(i, timeout = 0.5)) # default timeout here = 1 sec.
    getStataFuture(f2) else message("Not yet ready!"))
# Say good-bye to Stata:
stopStata(i)

A multiple Stata instance functionality demo

library(RStataLink)
# The length of cl (the number of Stata instances) will depend
# on the number of cores detected by parallel::detectCores()
# (but you can override it)
cl <- startStataCluster()
# It's just a simple list overall:
class(cl)
# Have a look at the first element:
cl[[1]]
# A trivial example - a series of different regressions with
# only one explanatory variable each:
m <- paste('regress sepallength',
                 c('sepalwidth',
                   'petallength',
                   'petalwidth'))
cat(m, sep = '\n')
c1 <- doInStataCluster(cl,
                       # X = a vector (atomic or list) of tasks/jobs
                       # expressed as a character vector
                       # or a list of character vectors (which will be
                       # collapsed into string each)
                       X = m,
                       nolog = TRUE,
                       df = iris,
                       import_df = FALSE)
# A load-balancing version -- use if some jobs/tasks
# are much longer than others (iris data already loaded
# so no need for df = iris):
c2 <- doInStataClusterLB(cl,
                       X = m,
                       nolog = TRUE)
identical(c1, c2)
# Collect the results:
library(magrittr) # for the super cool pipe operator %>%
c1 %>%
    lapply(function(x) x$results$e_class$modeldf) %>%
    do.call(rbind, .)
# Say good-bye to all the Statas opened by R
# (clear = TRUE so that each Stata allows you
# to discard the imported iris data):
stopStataCluster(cl, clear = TRUE)