Replication Package for the paper "Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study" (MSR '21). # Scripts: Data Collection and Processing These are the scripts used to extract the data from _rOpenSci_. The following steps indicate how to use them. 1. Add all attached R files into an R project. 2. Install the following R packages. Moreover, the process also requires to have a working GitHub account, in order to obtain the corresponding token. ```{r} library(dplyr) library(stringr) library(stringi) library(jsonlite) library(httpuv) library(httr) library(ggplot2) library(tidyr) ```' 3. All the individual functions on the following files should be sourced into the R Environment: `getToken.R`, `comments.R`, `issues.R`, and `tagging.R`. 4. Run the script located on the file `process.R`. This will run all the previous functions in the corresponding order. # Datasets The following files are included: -Dataset_1-100_Author1.xlsx contains the randomly selected 100 comments that were classified according to TD types by Author 1. -Dataset_1-100_Author2.xlsx contains the randomly selected 100 comments that were classified according to TD types by Author 2 and the combined classification (in blue) after discussion. -Dataset_Phrases_Both.xlsx contains the randomly selected 358 comments (resulting in 602 phrases) that were classified according to TD types by both authors 1 and 2. Their classification was incorporated into a single spreadsheet side by side for easy comparison. Disagreement was discussed and final classification is in the “Agreement” field. -UserRoles.csv contains the user roles associated with the 600 phrases. The “comment_id” is the unique identifier for the comment from which the phrase is extracted. The phrase is represented in the “statement” field. The “agreement” field shows the final technical debt label after the analysis by two of the authors. The user roles are shown in the “user_role” column.