10 Combining Stata and R
10.1 Introduction
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.
10.2 Switching Languages
You tell knitr which language to use for each code chunk. This is specified
in the code chunk header.
10.2.1 Using Stata
A regression in Stata.
The code chunk is
```{stata}
sysuse auto
regress mpg weight
```The result of including it in your document is
(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
------------------------------------------------------------------------------10.2.2 Using R
A regression in R, using very similar data!
The code chunk is
```{r cars}
summary(lm(mpg ~ wt, data=mtcars))
```The result of including it in your document is
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(This page was written using Statamarkdown version 0.9.2.)