This is a (non-comprehensive, very personally biased) guide to reproducibilty in scientific computing, software design and usage, data management and analysis. I focus primarily on programming and analysis practices in R, but the principles I've learned are extensible to your tool of choice. I also talk about general data management, and open-science platforms.
WIP
- Do not alter another machine's state
- Do not refer to things unique to your machine, or that are likely to differ between machines
- Record everything that can affect the analysis
- R-scripts (
.R-files) should be able to run from top to bottom hitch-free (as far as possible)
| Bad R | Good R |
|---|---|
setwd("my/path/that/no-one/else/has/) |
Using R-projects (i.e. a .Rproj-file) |
read.csv("an/absolute/path/") |
here::here("relative/path/") |
rm(list = ls() |
Don't use this in R-scripts |
install.package(...) |
Don't use this in R-script |
important_random_nos <- runif(...) |
set.seed(1234); reproducible_random_nos <- runif(...) |