don't actually run python

DeclareDesign · Feb 12, 2018 · 05c5d5c · 05c5d5c
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -15,14 +15,13 @@ _pkgdown.yml
 update_repo.R
 ^data-raw$
 ^_pkgdown\.yml$
+^estimatr_.*\.tar\.gz$
 ^tests/testthat/mtcars\.csv$
 ^tests/testthat/run-stata-models\.do$
 ^tests/testthat/test-broom\.R$
-^estimatr_.*\.tar\.gz$
 ^tests/testthat/test-texreg\.R$
 ^cran-comments\.md$
 ^vignettes/simulations-ols-variance\.Rmd$
 ^vignettes/simulations-debiasing-dim\.Rmd$
 ^vignettes/simulations.*\.rda$
 ^vignettes/stata-wls-hat\.Rmd$
-^vignettes/mtcars\.csv$
diff --git a/vignettes/mtcars.csv b/vignettes/mtcars.csv
diff --git a/vignettes/stata-wls-hat.Rmd b/vignettes/stata-wls-hat.Rmd
@@ -118,12 +118,15 @@ lm_robust(
 ```
 
 We can also see that Python's [`statsmodels`](http://www.statsmodels.org/stable/index.html) provides the same results as the methods in R (and in fact they note the difference in an [issue on GitHub](https://github.com/statsmodels/statsmodels/issues/1209)).
-```{python, python.reticulate=FALSE}
+```{python, python.reticulate=FALSE, eval = FALSE}
 import statsmodels.api as sm
 import pandas as pd
 dat = pd.read_csv('mtcars.csv')
 wls_mod = sm.WLS(dat['mpg'], sm.add_constant(dat['hp']), weights = dat['wt'])
 print(wls_mod.fit().HC2_se)
+#> const    2.162818
+#> hp       0.014457
+#> dtype: float64
 ```
 
 If we do the same in Stata 13, we get the following output: