basetheme

- R package "matrixTests": https://github.com/karoliskoncevicius/matrixTests I often have to perform statistical tests on genomic data. Which in practice means running the same test on every gene (row of a matrix). Running it separately on each row is slow, specially in R. Hence to speed it up I started an R package which is a lot faster and deals nicely with edge case scenarios (missing values, infinities, empty matrices, etc). - R package "annmatrix": https://github.com/karoliskoncevicius/annmatrix An S3 object for matrices with persistent metadata. This again is mostly relevant in genomic contexts where a matrix of, say, gene expression has to also keep information about rows (gene names, positions, chromosomes), as well as samples (disease status, age, sex). - R package "basetheme": https://github.com/karoliskoncevicius/basetheme On o

Base R plot() can also be pretty powerful[1] and look great.[2] As an alternative to ggplot2 I would also propose that Vega Lite[3] could be a contender, with an excellent cross language ecosystem. [1] http://karolis.koncevicius.lt/posts/r_base_plotting_without_... [2] https://github.com/KKPMW/basetheme [3] https://vegawidget.github.io/vlbuildr/

I don't want to say that the Tidyverse is inferior, because it's not and it works for a lot of people. Hadley will also know much more about R and always be a better programmer than me. However, there are other tools which predate Tidyverse, which I think so a stellar job at their core competency. ggplot2 is great, but it was also around long before the Tidyverse concept. But at the same time base R plot() can also be pretty powerful[1] and look great.[2] As an alternative to ggplot2 I would also propose that Vega Lite[3] could be a contender, with an excellent cross language ecosystem. There are also libraries available for applying SQL on dataframes from directly within R if that's what you want to do. sqldf[4] has been around for a long time, and now there is also the new duckdf[5], which is a bit quicker. Or one can use the DBI[6] lib

I'm really pleased to see you're using Pander[1] in your tool chain. I think this is one of the most useful literate programming tools in the R ecosystem. Although, having read Part 6 (briefly), it looks like you might only be using it for tables and plots. I think Pander's literate programming functionality, in conjunction with Brew, is worth further exploration[2]. Also, if you're using R-base plots, there's an excellent tutorial[3] and theme library[4] which can help make those plots look really excellent. [1] http://rapporter.github.io/pander/ [2] https://github.com/Rapporter/pander/tree/master/inst/example... [3] http://karolis.koncevicius.lt/posts/r_base_plotting_without_... [4] https://github.com/KKPMW/basetheme