I live right outside of Washington D.C. I can walk to NIH, and I pass three hospitals on the way to the grocery store. Not surprisingly, I have met a few biologist that needed programming help. After helping a few biologists in completely independent situations, I began to notice a pattern. Researchers have data, can write R, but can’t organize all of the packages and dependencies they need to execute their analysis.

Click the topics to find a list of past threads and to suggest your own for the future! Planeshift.

Every time, I install R using Anaconda and hook into the Bioconda channel, and all is well. After the second encounter, I wrote in a guide using GitHub Markdown. I have given it out three or four times since then!

The latest version of Mac OS X, High Sierra, comes with Python 2.7 out of the box. $ brew install python@2 Because python@2 is a “keg”. This opinionated guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a daily basis. Will be to install Python 2.7 with the Python 2 Miniconda and to install Python 3.7 with the Python 3 Miniconda. You can override the default by explicitly setting python=2 or python=3. It also determines the default value of CONDA_PY when using conda build.

I figure it is about time to make it public. See the guide below. Installing R and RStudio via Anaconda for Biologists This guide will take users through installation of R in a new Anaconda virtual environment. I always recommend biologists use Anaconda for managing R and its dependencies, because it gives us access to the Bioconda channel.

The Bioconda channel is an incredibly powerful hub for many of the most important bioinformatic software. Not only does it consolidate the packages into a single channel, it manages version dependencies between them. Any software stack that can be built with Anaconda can be replicated on any other similar system with ease. Reproduce-ability of environments and results is a must for biological research in the computer age.

We will make frequent mention of Python in this guide. This is because, at its core, Anaconda is a virtualizer for Python. There is a close relationship between Python and R in the biology community, so they are managed simultaneously by Anaconda. The principles in this guide can be similarly applied to creating Python environments. See the relevant section of the related guide in this repository on installing Python.