npm-badges
Appendices, data sets, and R scripts for:
"Adding Sparkle to Social Coding: An Empirical Study of Repository Badges in the npm Ecosystem."
Asher Trockman, Shurui Zhou, Christian Kästner, and Bogdan Vasilescu.
In Proceedings of the International Conference on Software Engineering, ICSE, ACM (2018).
R scripts
You can run the R scripts on the CSV files in the repository to estimate the different regression models we report on in the paper.
Before you run
Make sure you have R installed. We tested this on R 3.3.2.
We require the following dependencies: lme4, lmerTest, MuMIn, car, MASS, effects, effsize, sqldf, MASS, pROC, plyr, beanplot, RColorBrewer, ggplot2.
It may be necessary to change the Pandoc environment variable in make.R: the required path can be found by running Sys.getenv("RSTUDIO_PANDOC") in RStudio. If you are using OS X with an existing RStudio installation, this step is probably not needed.
Run using make
You can run make from the root folder to generate appendix.pdf.
Run from R
You can also run make.R directly from R by setting the working directory to this repository with setwd('/path') and running source('make.R').
If you want to change something and rebuild the appendix, it is most efficient to run source('make.R') from an existing R session. The script will detect that the data has already been loaded into memory and processed, preventing this time-consuming step from happening again.
Notes
Some data sets have been truncated to to meet GitHub's file size limit.
data/timeseries_freshness.csv: reduced from 3,294,725 to 47,400 rows, since we only considered projects with non-zero freshness scores in our longitudinal analysis.data/master_info.csv: removed unnecessary columns such as license and author information, which were not used in our study.