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 initial set up 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.
```{stata auto}
sysuse auto
regress mpg weight
```
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
------------------------------------------------------------------------------
```{r cars}
summary(lm(mpg ~ wt, data=mtcars))
```
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