I just attended an online presentation on tips for increasing the reproducibility of your research run by AIMOS - Association for Interdisciplinary Meta-Research and Open Science. I asked the community there for some pointers to good resources to help people newish to coding to learn about increasing the reproducibility of their research. Here are few of the ones I though were useful, they are mostly focused on coding in R. I hope others find this list useful too.
This book provides an overview of skills needed for reproducible and open research using the statistical programming language R and tidyverse packages. It covers reproducible workflows, data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations. Note that there are short video lectures for each chapter and of course code to reproduce the examples provided.
Packaging Data Analytical Work Reproducibly Using R
Useful journal article, here is the full citation: Marwick B, Boettiger C, Mullen L, (2018) Packaging Data Analytical Work Reproducibly Using R (and Friends). The American Statistician 72, 80–88. https://doi.org/10.1080/00031305.2017.1375986
FORRT is for advancing research transparency, reproducibility, rigor, and ethics through pedagogical reform and meta-scientific research.
This is the website for the 2nd edition of “R for Data Science”. This book will teach you how to do data science with R. You’ll learn how to get your data into R, get it into the most useful structure, transform it and visualize it.
An R package that transforms the preprocessing code in your R, R Markdown, or Python script into a Smallset Timeline, a simple visualisation of preprocessing decisions.
A way for research teams to standardize research workflow, plus track and reproduce all computations and discoveries. You can get free access for academic research.
We believe every researcher should know how to write short programs that clean and analyze data in a reproducible way and how to use version control to keep track of what they have done. But just as some astronomers spend their careers designing telescopes, some researchers focus on building the software that makes research possible. People who do this are called research software engineers; the aim of this book is to get you ready for this role by helping you go from writing code for yourself to creating tools that help your entire field advance.