Adding a Docker image to your R bioinformatics project to improve reproducibility
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlVtv0Th5_8Aal4NvGv4JqIQK2qRpnLzKkXLUGrv4A_XtnlW8szSZ9nd3ELCcPh8_RmdYN6ASnKKQ8G1sVb-0doItxqKi0PZXLh-lDi8dFT2xT-B2V_AEzirSTYNMkWRiBK_elZS7aUHUSTwHqb6j_mML4HVpWb8yzvLiY3cY7VGi5fUTO73ZvjY4HjEPT/w320-h180/mqdefault.jpg)
I have gotten into the habit of making Docker images for each of my projects, which is helpful if these projects are large, and span over multiple years. Long-running projects are a headache for bioinformaticians because software like R has significant updates each year and we want to keep our machines running the most up to date software with recent bug fixes, features and new packages. Docker images also help in making the research more reproducible for others who are interested in taking a deeper dive into the research materials. This helps auditability and could even help in improving research quality through better transparency. That said, still it is a bit tricky to achieve, so I thought I would write walk through on how I do it with an R workflow. Step 0: install docker on a linux system On Ubuntu you can use: sudo apt install docker.io Other OSs will vary. Step 1: Write your R Markdown script You probably have a workflow that you've been working on which works for your cur