  With the current emphasis on reproducibility and replicability, there is an
increasing need to examine how data analyses are conducted. In order to analyze
the between researcher variability in data analysis choices as well as the
aspects within the data analysis pipeline that contribute to the variability in
results, we have created two R packages: matahari and tidycode. These packages
build on methods created for natural language processing; rather than allowing
for the processing of natural language, we focus on R code as the substrate of
interest. The matahari package facilitates the logging of everything that is
typed in the R console or in an R script in a tidy data frame. The tidycode
package contains tools to allow for analyzing R calls in a tidy manner. We
demonstrate the utility of these packages as well as walk through two examples.
