Reproducible Research with R Markdown
Meetup Description
Research is considered to be reproducible when the exact results can be reproduced if given access to the original data, software, or code. Reproducible research is sometimes known as reproducibility, reproducible statistical analysis, reproducible data analysis, reproducible reporting, and literate programming.
Reproducible Research with R Markdown
According to Harris er al. (2018) analyses are reproducible when analyzing the same data with the same methods produces the same results.
This is different from:
Repeatability, which is the ability to conduct the same analysis on the same data (regardless of results), and
Replicability, which is the ability collect new data, use the same methods, and get the same results
How can I make my research reproducible?
Two things are needed to reproduce research:
The data
The statistical code or very detailed methods instructions
Renv package
The renv package helps you create create reproducible environments for your R projects. The renv package vignette introduces you to the basic nouns and verbs of renv, like the user and project libraries, and key functions like,
renv::init(), renv::snapshot() and renv::restore().
You’ll also learn about some of the infrastructure that makes renv tick, some problems that renv doesn’t help with, and how to uninstall it if you no longer want to use it.
More documentation on Renv found here;
Read all about Posits’ blog on the release of the renv v.1.0.0 here