The ultimate standard for strengthening scientific evidence is replicatoon of findings and conducting studies with independent study investigators. Reproducible research makes analytic data and code available so that others may reproduce findings. R and RStudio provides a better Literate (Statistical) Programming platform by availability knitr and integration with pandoc , Latex Markdown, HTML etc.
[From Roger Peng Book] The idea of literate programming is to think of a report or a publication as a stream of text and code. The text is readable by people and the code is readable by computers. The analysis is described in a series of text and code chunks. Each kind of code chunk will do something like load some data or compute some results. Each text chunk will relay something in a human readable language. There might also be presentation code that formats tables and figures and there’s article text that explains what’s going on around all this code. This stream of text and code is a literate statistical program or a literate statistical analysis.
Reproducibility can be more or less easy to achieve depending on the context, the scientific area, the complexity of a data analysis, a variety of other factors.
- Good Science
- Don’t Do Things By Hand - let the code do the talking
- Don’t Point And Click
- Teach a Computer it listens
- Use Some Version Control - Github
- Keep Track of Your Software Environment
- Dont Save output
- Set Your Seed