atrisovic / dataverse-r-study Goto Github PK

Overleaf:

https://www.overleaf.com/3579224233hcjdwrssrwbq

Excellent work 😄

Comments and questions, I hope you don't mind me misusing the repo for that. Happy to take the conversation elsewhere if you prefer (email?).

1. The installation of missing packages could also fail when you use an old R version today but CRAN does not have the package for that version, or you use a new R version and R only has it working for an older release. Did you consider using MRAN checkpoints matching the R version release window?
2. Have you considered using R package that can parse R files for loaded packages? automagic and similar stuff?
3. Why Python code to analyse R code - personal preference, or did you find that useful somehow?

(Might add more questions as I digest... again, cool work!)

Because they do not stop on errors, that is why they are "best performing". You are basically ignoring errors:

{ require("foobar") ; install.packages(tempfile()); message("\nNOT GOOD!!!\n") }

See how "NOT GOOD" is printed here:

Loading required package: foobar
Installing package into ‘/Users/gaborcsardi/Library/R/arm64/4.2/library’
(as ‘lib’ is unspecified)

NOT GOOD!!!

Warning messages:
1: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘foobar’
2: package ‘/var/folders/ph/fpcmzfd16rgbbk8mxvy9m2_h0000gn/T//Rtmph0Pd7A/file12c3c78c562f5’ is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages

OTOH you could argue that if the script finishes anyway, then those packages were not really needed in the first place....

Replication package = dataset = data & code

code-stats

• Lines of code per comment
• Lines of code per dependency
• Lines of code per function (modularity)
• Lines of code per 'test' (testing)
• File encoding

overview

• Histogram of dataset sizes
• Histogram of number of files per dataset
• Histogram of file name lenghts
• Pie chart - file name contains space?
• Pie chart - dataset contains documentation?
• Pie chart - dataset contains other code?
• Pie chart - dataset contains testing script?
• Pie chart - dataset contains R markdown?

exe-rates

• exe rate before cleaning
• exe rate after cleaning
• exe rate per R version
• aggrigated results per package

exe-stats

• exe rate per year of publishing
• exe rate per field of study
• exe rate per publisher
• exe rate per dependency count