The R Journal: article published in 2020, volume 12:1

Tools for Analyzing R Code the Tidy Way PDF download
Lucy D’Agostino McGowan, Sean Kross and Jeffrey Leek , The R Journal (2020) 12:1, pages 226-242.

Abstract 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.

Received: 2019-06-14; online 2020-09-10
CRAN packages: matahari, tidycode, tidyverse, tidytext, dplyr, purrr, wordcloud, data.table, gh
CRAN Task Views implied by cited CRAN packages: NaturalLanguageProcessing, Databases, Finance, HighPerformanceComputing, ModelDeployment, TimeSeries, WebTechnologies

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Lucy D’Agostino McGowan and Sean Kross and Jeffrey Leek},
  title = {{Tools for Analyzing R Code the Tidy Way}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-011},
  url = {},
  pages = {226--242},
  volume = {12},
  number = {1}