Thomas Pedersen is one of the less well known contributors to the R ecosystem. His work on fundamentals like graphics rendering is the kind of thing that can be pretty thankless and I think he deserves a ton of credit for his combination of technical skills and focus on user experience. The gganimate package used for animating plots is also by him.
Is tidyverse still fundamentally license incompatible with everything? Last I checked, it included both gpl2-only, and gplv3+, modules. That prevented my company from releasing code that we wanted to.
(this is really a pervasive problem across the r ecosystem, but tidyverse really seals the deal because it's so popular)
The good news is I don't think there is a feature in the tidyverse you couldn't do with base R, albeit in an "R way." I think the main sell of tidyverse is the sexier plots from ggplot2 and some quality of life functions. Maybe others can comment if there is more there. The annoyance of 85% of stack overflow R comments starting off with sourcing the tidyverse library was enough to push me away and develop a base R solution whenever I had an issue.
I think the appeal to the tidyverse "universe" is the readability of the code. Especially to novel users of R (and there are a lot of people in science that have to generate a plot once a month or so). ggplot2 works fine if you only use that package without the rest of tidyverse. I personally use the data.table package a lot and than generate plots using ggplot2. Stackoverflow is indeed very much focused on tidyverse packages which is a pity as indeed base R can solve so many problems posted too without the need of additional packages.
Right. I'm not sure there's ever been definitive rulings on this but the viewpoint in the R community has been that it is the licensing is at the package level in terms of what a user would care about. As an example, in the early days there was an intentional decision by the Bioconductor group to use more permissive licenses in order to enable corporate use and thus help with adoption.
This is definitely a great intro to the parts of R that are relevant for all types of analysis. After you get comfortable with this and want to do some domain-specific analysis, check out the R Task Views[0] on the official site. You can read brief summaries of what packages are available for different analysis tasks. Really useful, considering how many packages are on the official repository (CRAN).
To see more of what a package does and how to use it, look for vignettes on the package description page. For example, the `forecast` package[1] includes a journal article.
You could also look on bookdown.org, which showcases some online books made using the titular package. The are a lot of "Doing X with R" guides among them.
There's a Johns Hopkins certificate program in data science on Coursera, it's all taught in R. If you have a background in data science a lot of the course will be review but I learned R taking those classes and found it to be really well designed.
Very cool that you can register font names that have styles attached. And all of the ligature support seems great for adding math expressions to your plots.
