Forget about Python being the prime data analysis platform: there are plenty of alternatives and R has been one of them. With CRAN, rOpenSci, Bioconductor (doi:10.1186/gb-2004-5-10-r80) the platform has three efforts where you can publish your R work. I think of them as scholarly journals: the peer review is strong with them. Anyways, over the years I did my share of R coding (a good bit of my PhD is written in R) and contributed to a few R packages. Nowadays I don’t do a lot of R coding anymore. (Sorry, genalg users: I know this package needs some serious love, and a huge thank you to those (like Michel Ballings) who have picked up the package!!)
But regarding the packaging, I still contribute my bits. For example, with rWikiPathways and BridgeDbR. So, I happily accepted the invitation to contribute to a paper that was published this week and outlines a ton of R packages that are used in the data analysis of metabolomics data: The metaRbolomics Toolbox in Bioconductor and beyond (doi:10.3390/metabo9100200), led by Jan Stanstrup and Steffen Neumann. And many R packages it discusses indeed! The paper is like an atlas, showing you around in a adventurous world of metabolomics, as clear from this dependency graph of Figure 2:
|CC-BY. Figure 2 from the article.|
But there is more ongoing. The article, being CC-BY is being rewritten as a book, and I have some work left to do to add BioSchemas to Bioconductor R package web pages, get more packages to use BioSchemas in their package vignettes (so the ELIXIR TeSS can automatically pick them up), and there is some more awesomeness being discussed. Well, that’s not there yet, but you can start reading this metaRbolomics bible.
Thanks to everyone involved!