README.md
In alekrutkowski/RStataLink: Tools for calling Stata from R interactively
RStataLink -- R package for calling Stata from R interactively
Aleksander Rutkowski
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()
## Stata "server" started successfully.
i
## StataID object:
##
## Stata "server" id:
## oZV
## (you can see it in the top of the Stata window)
##
## Full path to the Stata "server" <--> R
## data exchange directory (folder):
## C:\Users\rutkoal\AppData\Local\Temp\1\RtmpekNB9k/oZV
##
## Should Stata close if this directory disappears:
## no
r1 <- doInStata(i, 'display 100 + 15.7', results = NULL)
r1 # results = NULL means no import of Stata e() or r() results
## $log
## . display 100 + 15.7
## 115.7
# Use in Stata a built-in demo dataset on cars
# and make a simple regression with robust standard errors
# (the 3 lines of code could be also done with 3 consecutive doInStata calls)
r2 <- doInStata(i,
'sysuse auto, clear
regress price weight trunk, robust
ereturn list')
r2$log
## . sysuse auto, clear
## (1978 Automobile Data)
##
## . regress price weight trunk, robust
##
## Linear regression Number of obs = 74
## F(2, 71) = 16.69
## Prob > F = 0.0000
## R-squared = 0.2943
## Root MSE = 2512.5
##
## ------------------------------------------------------------------------------
## | Robust
## price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## weight | 2.266182 .6227162 3.64 0.001 1.024521 3.507842
## trunk | -60.03885 88.23694 -0.68 0.498 -235.9783 115.9006
## _cons | 148.5533 947.5387 0.16 0.876 -1740.785 2037.892
## ------------------------------------------------------------------------------
##
## . ereturn list
##
## scalars:
## e(N) = 74
## e(df_m) = 2
## e(df_r) = 71
## e(F) = 16.69201624215745
## e(r2) = .2942577843486112
## e(rmse) = 2512.482807011177
## e(mss) = 186872936.3792214
## e(rss) = 448192459.7424002
## e(r2_a) = .2743777219358961
## e(ll) = -682.8181749520038
## e(ll_0) = -695.7128688987767
## e(rank) = 3
##
## macros:
## e(cmdline) : "regress price weight trunk, robust"
## e(title) : "Linear regression"
## e(marginsok) : "XB default"
## e(vce) : "robust"
## e(depvar) : "price"
## e(cmd) : "regress"
## e(properties) : "b V"
## e(predict) : "regres_p"
## e(model) : "ols"
## e(estat_cmd) : "regress_estat"
## e(vcetype) : "Robust"
##
## matrices:
## e(b) : 1 x 3
## e(V) : 3 x 3
## e(V_modelbased) : 3 x 3
##
## functions:
## e(sample)
r2$results # this (below) looks similar to "ereturn list" in Stata (above)
## $e_class
## List of 4
## $ scalars :List of 12
## ..$ N : num 74
## ..$ df_m: num 2
## ..$ df_r: num 71
## ..$ F : num 16.7
## ..$ r2 : num 0.294
## ..$ rmse: num 2512
## ..$ mss : num 1.87e+08
## ..$ rss : num 4.48e+08
## ..$ r2_a: num 0.274
## ..$ ll : num -683
## ..$ ll_0: num -696
## ..$ rank: num 3
## $ macros :List of 11
## ..$ cmdline : chr "regress price weight trunk, robust"
## ..$ title : chr "Linear regression"
## ..$ marginsok : chr "XB default"
## ..$ vce : chr "robust"
## ..$ depvar : chr "price"
## ..$ cmd : chr "regress"
## ..$ properties: chr "b V"
## ..$ predict : chr "regres_p"
## ..$ model : chr "ols"
## ..$ estat_cmd : chr "regress_estat"
## ..$ vcetype : chr "Robust"
## $ matrices:List of 3
## ..$ b :
## weight trunk _cons
## y1 2.27 -60 149
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V :
## weight trunk _cons
## weight 0.388 -46.5 -435
## trunk -46.457 7785.8 24214
## _cons -435.200 24213.6 897830
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V_modelbased:
## weight trunk _cons
## weight 4.14e-08 -5.05e-06 -5.54e-05
## trunk -5.05e-06 1.37e-03 -3.53e-03
## _cons -5.54e-05 -3.53e-03 2.29e-01
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## $ modeldf :Classes 'Stata_b_se' and 'data.frame': 3 obs. of 2 variables:
## coef stderr
## weight 2.27 0.623
## trunk -60.04 88.237
## _cons 148.55 947.539
##
## - attr(*, "class")= chr "StataResults"
##
## $r_class
## List of 3
## $ scalars :List of 1
## ..$ level: num 95
## $ macros :List of 1
## ..$ citype: chr "normal"
## $ matrices:List of 1
## ..$ table:
## weight trunk _cons
## b 2.27e+00 -60.039 148.553
## se 6.23e-01 88.237 947.539
## t 3.64e+00 -0.680 0.157
## pvalue 5.15e-04 0.498 0.876
## ll 1.02e+00 -235.978 -1740.785
## ul 3.51e+00 115.901 2037.892
## df 7.10e+01 71.000 71.000
## crit 1.99e+00 1.994 1.994
## eform 0.00e+00 0.000 0.000
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## - attr(*, "class")= chr "StataResults"
# 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$log
##
## . describe
##
## Contains data
## obs: 150
## vars: 5
## size: 3,900
## ------------------------------------------------------------------------------
## storage display value
## variable name type format label variable label
## ------------------------------------------------------------------------------
## sepallength float %9.0g Sepal.Length
## sepalwidth float %9.0g Sepal.Width
## petallength float %9.0g Petal.Length
## petalwidth float %9.0g Petal.Width
## species str10 %10s Species
## ------------------------------------------------------------------------------
## Sorted by:
## Note: Dataset has changed since last saved.
##
## . gen ln_sepallength = log(sepallength)
##
## . reg ln_sepallength sepalwidth petallength petalwidth
##
## Source | SS df MS Number of obs = 150
## -------------+---------------------------------- F(3, 146) = 304.61
## Model | 2.56104326 3 .853681087 Prob > F = 0.0000
## Residual | .409175062 146 .002802569 R-squared = 0.8622
## -------------+---------------------------------- Adj R-squared = 0.8594
## Total | 2.97021832 149 .019934351 Root MSE = .05294
##
## ------------------------------------------------------------------------------
## ln_sepalle~h | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## sepalwidth | .1070129 .0112169 9.54 0.000 .0848444 .1291813
## petallength | .1166281 .009546 12.22 0.000 .097762 .1354943
## petalwidth | -.0842665 .0214666 -3.93 0.000 -.1266919 -.0418411
## _cons | 1.090994 .0422063 25.85 0.000 1.00758 1.174408
## ------------------------------------------------------------------------------
# If data is exported to Stata with df = ...
# a (possibly modified) Stata dataset is imported back
# into R unless argument import_df = FALSE is
# specified in doInStata()
str(r3$df)
## 'data.frame': 150 obs. of 6 variables:
## $ sepallength : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ sepalwidth : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ petallength : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ petalwidth : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ species : chr "setosa" "setosa" "setosa" "setosa" ...
## $ ln_sepallength: num 1.63 1.59 1.55 1.53 1.61 ...
r3$results$e_class # again, the estimated results from e() are available:
## List of 4
## $ scalars :List of 12
## ..$ N : num 150
## ..$ df_m: num 3
## ..$ df_r: num 146
## ..$ F : num 305
## ..$ r2 : num 0.862
## ..$ rmse: num 0.0529
## ..$ mss : num 2.56
## ..$ rss : num 0.409
## ..$ r2_a: num 0.859
## ..$ ll : num 230
## ..$ ll_0: num 81.3
## ..$ rank: num 4
## $ macros :List of 10
## ..$ cmdline : chr "regress ln_sepallength sepalwidth petallength petalwidth"
## ..$ title : chr "Linear regression"
## ..$ marginsok : chr "XB default"
## ..$ vce : chr "ols"
## ..$ depvar : chr "ln_sepallength"
## ..$ cmd : chr "regress"
## ..$ properties: chr "b V"
## ..$ predict : chr "regres_p"
## ..$ model : chr "ols"
## ..$ estat_cmd : chr "regress_estat"
## $ matrices:List of 2
## ..$ b:
## sepalwidth petallength petalwidth _cons
## y1 0.107 0.117 -0.0843 1.09
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V:
## sepalwidth petallength petalwidth _cons
## sepalwidth 1.26e-04 3.24e-05 -4.58e-05 -0.000451
## petallength 3.24e-05 9.11e-05 -1.96e-04 -0.000206
## petalwidth -4.58e-05 -1.96e-04 4.61e-04 0.000326
## _cons -4.51e-04 -2.06e-04 3.26e-04 0.001781
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## $ modeldf :Classes 'Stata_b_se' and 'data.frame': 4 obs. of 2 variables:
## coef stderr
## sepalwidth 0.1070 0.01122
## petallength 0.1166 0.00955
## petalwidth -0.0843 0.02147
## _cons 1.0910 0.04221
##
## - attr(*, "class")= chr "StataResults"
# 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)
## $log
## . describe
##
## Contains data from Q:\Stata140.001\ado\base/a/auto.dta
## obs: 74 1978 Automobile Data
## vars: 12 13 Apr 2014 17:45
## size: 3,182 (_dta has notes)
## ------------------------------------------------------------------------------
## storage display value
## variable name type format label variable label
## ------------------------------------------------------------------------------
## make str18 %-18s Make and Model
## price int %8.0gc Price
## mpg int %8.0g Mileage (mpg)
## rep78 int %8.0g Repair Record 1978
## headroom float %6.1f Headroom (in.)
## trunk int %8.0g Trunk space (cu. ft.)
## weight int %8.0gc Weight (lbs.)
## length int %8.0g Length (in.)
## turn int %8.0g Turn Circle (ft.)
## displacement int %8.0g Displacement (cu. in.)
## gear_ratio float %6.2f Gear Ratio
## foreign byte %8.0g origin Car type
## ------------------------------------------------------------------------------
## Sorted by: foreign
# Also r-class Stata results can be collected:
r4 <- doInStata(i, 'summarize price \n return list',
results = 'r') # Stata r-class results only
r4
## $log
## . summarize price
##
## Variable | Obs Mean Std. Dev. Min Max
## -------------+---------------------------------------------------------
## price | 74 6165.257 2949.496 3291 15906
##
## . return list
##
## scalars:
## r(N) = 74
## r(sum_w) = 74
## r(mean) = 6165.256756756757
## r(Var) = 8699525.974268789
## r(sd) = 2949.495884768919
## r(min) = 3291
## r(max) = 15906
## r(sum) = 456229
##
##
## $results
## $results$r_class
## List of 1
## $ scalars:List of 8
## ..$ N : num 74
## ..$ sum_w: num 74
## ..$ mean : num 6165
## ..$ Var : num 8699526
## ..$ sd : num 2949
## ..$ min : num 3291
## ..$ max : num 15906
## ..$ sum : num 456229
## - attr(*, "class")= chr "StataResults"
# 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)})
## user system elapsed
## 0.00 0.02 0.01
# 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)})
## user system elapsed
## 0.29 0.08 3.99
r5
## $log
## . sleep 6000 // in milliseconds in Stata
##
## . display "hello"
## hello
# 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!"))
## Not yet ready!
## user system elapsed
## 0.01 0.01 0.53
# 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()
## Stata "server" started successfully.
## Stata "server" started successfully.
# It's just a simple list overall:
class(cl)
## [1] "list"
# Have a look at the first element:
cl[[1]]
## StataID object:
##
## Stata "server" id:
## YDd
## (you can see it in the top of the Stata window)
##
## Full path to the Stata "server" <--> R
## data exchange directory (folder):
## C:\Users\rutkoal\AppData\Local\Temp\1\RtmpekNB9k/YDd
##
## Should Stata close if this directory disappears:
## no
# 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')
## regress sepallength sepalwidth
## regress sepallength petallength
## regress sepallength petalwidth
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)
## [1] TRUE
# Collect the results:
library(magrittr) # for the super cool pipe operator %>%
c1 %>%
lapply(function(x) x$results$e_class$modeldf) %>%
do.call(rbind, .)
## coef stderr
## sepalwidth -0.2233611 0.15508093
## _cons 6.5262227 0.47889633
## petallength 0.4089223 0.01889134
## _cons1 4.3066034 0.07838896
## petalwidth 0.8885802 0.05137355
## _cons2 4.7776294 0.07293476
# 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)
alekrutkowski/RStataLink documentation built on March 22, 2023, 2:18 a.m.
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RStataLink -- R package for calling Stata from R interactively
Aleksander Rutkowski
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()
## Stata "server" started successfully.
i
## StataID object:
##
## Stata "server" id:
## oZV
## (you can see it in the top of the Stata window)
##
## Full path to the Stata "server" <--> R
## data exchange directory (folder):
## C:\Users\rutkoal\AppData\Local\Temp\1\RtmpekNB9k/oZV
##
## Should Stata close if this directory disappears:
## no
r1 <- doInStata(i, 'display 100 + 15.7', results = NULL)
r1 # results = NULL means no import of Stata e() or r() results
## $log
## . display 100 + 15.7
## 115.7
# Use in Stata a built-in demo dataset on cars
# and make a simple regression with robust standard errors
# (the 3 lines of code could be also done with 3 consecutive doInStata calls)
r2 <- doInStata(i,
'sysuse auto, clear
regress price weight trunk, robust
ereturn list')
r2$log
## . sysuse auto, clear
## (1978 Automobile Data)
##
## . regress price weight trunk, robust
##
## Linear regression Number of obs = 74
## F(2, 71) = 16.69
## Prob > F = 0.0000
## R-squared = 0.2943
## Root MSE = 2512.5
##
## ------------------------------------------------------------------------------
## | Robust
## price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## weight | 2.266182 .6227162 3.64 0.001 1.024521 3.507842
## trunk | -60.03885 88.23694 -0.68 0.498 -235.9783 115.9006
## _cons | 148.5533 947.5387 0.16 0.876 -1740.785 2037.892
## ------------------------------------------------------------------------------
##
## . ereturn list
##
## scalars:
## e(N) = 74
## e(df_m) = 2
## e(df_r) = 71
## e(F) = 16.69201624215745
## e(r2) = .2942577843486112
## e(rmse) = 2512.482807011177
## e(mss) = 186872936.3792214
## e(rss) = 448192459.7424002
## e(r2_a) = .2743777219358961
## e(ll) = -682.8181749520038
## e(ll_0) = -695.7128688987767
## e(rank) = 3
##
## macros:
## e(cmdline) : "regress price weight trunk, robust"
## e(title) : "Linear regression"
## e(marginsok) : "XB default"
## e(vce) : "robust"
## e(depvar) : "price"
## e(cmd) : "regress"
## e(properties) : "b V"
## e(predict) : "regres_p"
## e(model) : "ols"
## e(estat_cmd) : "regress_estat"
## e(vcetype) : "Robust"
##
## matrices:
## e(b) : 1 x 3
## e(V) : 3 x 3
## e(V_modelbased) : 3 x 3
##
## functions:
## e(sample)
r2$results # this (below) looks similar to "ereturn list" in Stata (above)
## $e_class
## List of 4
## $ scalars :List of 12
## ..$ N : num 74
## ..$ df_m: num 2
## ..$ df_r: num 71
## ..$ F : num 16.7
## ..$ r2 : num 0.294
## ..$ rmse: num 2512
## ..$ mss : num 1.87e+08
## ..$ rss : num 4.48e+08
## ..$ r2_a: num 0.274
## ..$ ll : num -683
## ..$ ll_0: num -696
## ..$ rank: num 3
## $ macros :List of 11
## ..$ cmdline : chr "regress price weight trunk, robust"
## ..$ title : chr "Linear regression"
## ..$ marginsok : chr "XB default"
## ..$ vce : chr "robust"
## ..$ depvar : chr "price"
## ..$ cmd : chr "regress"
## ..$ properties: chr "b V"
## ..$ predict : chr "regres_p"
## ..$ model : chr "ols"
## ..$ estat_cmd : chr "regress_estat"
## ..$ vcetype : chr "Robust"
## $ matrices:List of 3
## ..$ b :
## weight trunk _cons
## y1 2.27 -60 149
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V :
## weight trunk _cons
## weight 0.388 -46.5 -435
## trunk -46.457 7785.8 24214
## _cons -435.200 24213.6 897830
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V_modelbased:
## weight trunk _cons
## weight 4.14e-08 -5.05e-06 -5.54e-05
## trunk -5.05e-06 1.37e-03 -3.53e-03
## _cons -5.54e-05 -3.53e-03 2.29e-01
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## $ modeldf :Classes 'Stata_b_se' and 'data.frame': 3 obs. of 2 variables:
## coef stderr
## weight 2.27 0.623
## trunk -60.04 88.237
## _cons 148.55 947.539
##
## - attr(*, "class")= chr "StataResults"
##
## $r_class
## List of 3
## $ scalars :List of 1
## ..$ level: num 95
## $ macros :List of 1
## ..$ citype: chr "normal"
## $ matrices:List of 1
## ..$ table:
## weight trunk _cons
## b 2.27e+00 -60.039 148.553
## se 6.23e-01 88.237 947.539
## t 3.64e+00 -0.680 0.157
## pvalue 5.15e-04 0.498 0.876
## ll 1.02e+00 -235.978 -1740.785
## ul 3.51e+00 115.901 2037.892
## df 7.10e+01 71.000 71.000
## crit 1.99e+00 1.994 1.994
## eform 0.00e+00 0.000 0.000
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## - attr(*, "class")= chr "StataResults"
# 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$log
##
## . describe
##
## Contains data
## obs: 150
## vars: 5
## size: 3,900
## ------------------------------------------------------------------------------
## storage display value
## variable name type format label variable label
## ------------------------------------------------------------------------------
## sepallength float %9.0g Sepal.Length
## sepalwidth float %9.0g Sepal.Width
## petallength float %9.0g Petal.Length
## petalwidth float %9.0g Petal.Width
## species str10 %10s Species
## ------------------------------------------------------------------------------
## Sorted by:
## Note: Dataset has changed since last saved.
##
## . gen ln_sepallength = log(sepallength)
##
## . reg ln_sepallength sepalwidth petallength petalwidth
##
## Source | SS df MS Number of obs = 150
## -------------+---------------------------------- F(3, 146) = 304.61
## Model | 2.56104326 3 .853681087 Prob > F = 0.0000
## Residual | .409175062 146 .002802569 R-squared = 0.8622
## -------------+---------------------------------- Adj R-squared = 0.8594
## Total | 2.97021832 149 .019934351 Root MSE = .05294
##
## ------------------------------------------------------------------------------
## ln_sepalle~h | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## sepalwidth | .1070129 .0112169 9.54 0.000 .0848444 .1291813
## petallength | .1166281 .009546 12.22 0.000 .097762 .1354943
## petalwidth | -.0842665 .0214666 -3.93 0.000 -.1266919 -.0418411
## _cons | 1.090994 .0422063 25.85 0.000 1.00758 1.174408
## ------------------------------------------------------------------------------
# If data is exported to Stata with df = ...
# a (possibly modified) Stata dataset is imported back
# into R unless argument import_df = FALSE is
# specified in doInStata()
str(r3$df)
## 'data.frame': 150 obs. of 6 variables:
## $ sepallength : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ sepalwidth : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ petallength : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ petalwidth : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ species : chr "setosa" "setosa" "setosa" "setosa" ...
## $ ln_sepallength: num 1.63 1.59 1.55 1.53 1.61 ...
r3$results$e_class # again, the estimated results from e() are available:
## List of 4
## $ scalars :List of 12
## ..$ N : num 150
## ..$ df_m: num 3
## ..$ df_r: num 146
## ..$ F : num 305
## ..$ r2 : num 0.862
## ..$ rmse: num 0.0529
## ..$ mss : num 2.56
## ..$ rss : num 0.409
## ..$ r2_a: num 0.859
## ..$ ll : num 230
## ..$ ll_0: num 81.3
## ..$ rank: num 4
## $ macros :List of 10
## ..$ cmdline : chr "regress ln_sepallength sepalwidth petallength petalwidth"
## ..$ title : chr "Linear regression"
## ..$ marginsok : chr "XB default"
## ..$ vce : chr "ols"
## ..$ depvar : chr "ln_sepallength"
## ..$ cmd : chr "regress"
## ..$ properties: chr "b V"
## ..$ predict : chr "regres_p"
## ..$ model : chr "ols"
## ..$ estat_cmd : chr "regress_estat"
## $ matrices:List of 2
## ..$ b:
## sepalwidth petallength petalwidth _cons
## y1 0.107 0.117 -0.0843 1.09
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## ..$ V:
## sepalwidth petallength petalwidth _cons
## sepalwidth 1.26e-04 3.24e-05 -4.58e-05 -0.000451
## petallength 3.24e-05 9.11e-05 -1.96e-04 -0.000206
## petalwidth -4.58e-05 -1.96e-04 4.61e-04 0.000326
## _cons -4.51e-04 -2.06e-04 3.26e-04 0.001781
## attr(,"class")
## [1] "matrix" "StataMatrix"
##
## $ modeldf :Classes 'Stata_b_se' and 'data.frame': 4 obs. of 2 variables:
## coef stderr
## sepalwidth 0.1070 0.01122
## petallength 0.1166 0.00955
## petalwidth -0.0843 0.02147
## _cons 1.0910 0.04221
##
## - attr(*, "class")= chr "StataResults"
# 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)
## $log
## . describe
##
## Contains data from Q:\Stata140.001\ado\base/a/auto.dta
## obs: 74 1978 Automobile Data
## vars: 12 13 Apr 2014 17:45
## size: 3,182 (_dta has notes)
## ------------------------------------------------------------------------------
## storage display value
## variable name type format label variable label
## ------------------------------------------------------------------------------
## make str18 %-18s Make and Model
## price int %8.0gc Price
## mpg int %8.0g Mileage (mpg)
## rep78 int %8.0g Repair Record 1978
## headroom float %6.1f Headroom (in.)
## trunk int %8.0g Trunk space (cu. ft.)
## weight int %8.0gc Weight (lbs.)
## length int %8.0g Length (in.)
## turn int %8.0g Turn Circle (ft.)
## displacement int %8.0g Displacement (cu. in.)
## gear_ratio float %6.2f Gear Ratio
## foreign byte %8.0g origin Car type
## ------------------------------------------------------------------------------
## Sorted by: foreign
# Also r-class Stata results can be collected:
r4 <- doInStata(i, 'summarize price \n return list',
results = 'r') # Stata r-class results only
r4
## $log
## . summarize price
##
## Variable | Obs Mean Std. Dev. Min Max
## -------------+---------------------------------------------------------
## price | 74 6165.257 2949.496 3291 15906
##
## . return list
##
## scalars:
## r(N) = 74
## r(sum_w) = 74
## r(mean) = 6165.256756756757
## r(Var) = 8699525.974268789
## r(sd) = 2949.495884768919
## r(min) = 3291
## r(max) = 15906
## r(sum) = 456229
##
##
## $results
## $results$r_class
## List of 1
## $ scalars:List of 8
## ..$ N : num 74
## ..$ sum_w: num 74
## ..$ mean : num 6165
## ..$ Var : num 8699526
## ..$ sd : num 2949
## ..$ min : num 3291
## ..$ max : num 15906
## ..$ sum : num 456229
## - attr(*, "class")= chr "StataResults"
# 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)})
## user system elapsed
## 0.00 0.02 0.01
# 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)})
## user system elapsed
## 0.29 0.08 3.99
r5
## $log
## . sleep 6000 // in milliseconds in Stata
##
## . display "hello"
## hello
# 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!"))
## Not yet ready!
## user system elapsed
## 0.01 0.01 0.53
# 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()
## Stata "server" started successfully.
## Stata "server" started successfully.
# It's just a simple list overall:
class(cl)
## [1] "list"
# Have a look at the first element:
cl[[1]]
## StataID object:
##
## Stata "server" id:
## YDd
## (you can see it in the top of the Stata window)
##
## Full path to the Stata "server" <--> R
## data exchange directory (folder):
## C:\Users\rutkoal\AppData\Local\Temp\1\RtmpekNB9k/YDd
##
## Should Stata close if this directory disappears:
## no
# 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')
## regress sepallength sepalwidth
## regress sepallength petallength
## regress sepallength petalwidth
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)
## [1] TRUE
# Collect the results:
library(magrittr) # for the super cool pipe operator %>%
c1 %>%
lapply(function(x) x$results$e_class$modeldf) %>%
do.call(rbind, .)
## coef stderr
## sepalwidth -0.2233611 0.15508093
## _cons 6.5262227 0.47889633
## petallength 0.4089223 0.01889134
## _cons1 4.3066034 0.07838896
## petalwidth 0.8885802 0.05137355
## _cons2 4.7776294 0.07293476
# 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)
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