---
title: "Combining Stata and R"
author: "Doug Hemken"
date: "Jul 2026"
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---

One of the virtues of processing your dynamic documents through R
is that you can use more than one programming language in a single
document.  Many of us are multi-lingual, and it is often quicker
and easier to execute part of a project in one language, while
completing your work in another.  This is especially common when
you are in the process of learning a new language, or if part of
your work involves a specialized language with limited capabilities.

## Some Setup for Stata
Some initial setup is required to use Stata to process commands.
You would include an initial fenced code block (\"code chunk\")
to do this.  Use the `include=FALSE`
chunk option to hide this from your readers.

```{{r Statasetup}}
library(Statamarkdown)
```



Then, to switch languages, you just indicate the language in
the code fence.

## Using Stata

```{{stata auto}}
sysuse auto
regress mpg weight
```


``` stata
sysuse auto
regress mpg weight
```

```
(1978 automobile data)

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(1, 72)        =    134.62
       Model |   1591.9902         1   1591.9902   Prob > F        =    0.0000
    Residual |  851.469256        72  11.8259619   R-squared       =    0.6515
-------------+----------------------------------   Adj R-squared   =    0.6467
       Total |  2443.45946        73  33.4720474   Root MSE        =    3.4389

------------------------------------------------------------------------------
         mpg | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      weight |  -.0060087   .0005179   -11.60   0.000    -.0070411   -.0049763
       _cons |   39.44028   1.614003    24.44   0.000     36.22283    42.65774
------------------------------------------------------------------------------
```

## Using R

```{{r cars}}
summary(lm(mpg ~ wt, data=mtcars))
```


``` r
summary(lm(mpg ~ wt, data=mtcars))
```

```

Call:
lm(formula = mpg ~ wt, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5432 -2.3647 -0.1252  1.4096  6.8727 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
wt           -5.3445     0.5591  -9.559 1.29e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.046 on 30 degrees of freedom
Multiple R-squared:  0.7528,	Adjusted R-squared:  0.7446 
F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10
```
