11–20 of 138 posts
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
I do genetic epidemiology (which is considerably more compute intensive than regular epidemiology), and R is still the most common language, with the most libraries and packages being used for it, compared to python for example.
I think maybe you should consider being less forthcoming with your opinions on topics which you are not well informed on.
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
What is a good book on statistics that one can use for self-learning?
What is a good book on statistics that one can use for self-learning?
Can second Statistical Rethinking though if you have the basics of stats and want to learn it again from a very different, more causal/bayesian point of view.
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
By contrast, from first downloading R to running my first R script took about 1 hour (the most difficult part was opening the 'script' pane in RStudio IDE, which doesn't open by default on new installations, for some reason).
There's huge demand out there for statistical software that's accessible to people whose primary pursuit is not programming/cs, but genetics, bioinformatics, economics, ecology and other disciplines that necessitate tooling much more powerful than excel, but with barriers to entry not much greater than excel. R is a fairly amazing fit for those folks.
Using it truly feels like a 'fresh way' to do statistics. Its main website provides ample use cases, guides and tutorials, and I often return to the blog for the well documented deepdives into how traditional frequentist methods and their bayesian counterparts compare (the animated explainers are especially helpful, and I appreciate the devs reflecting on each release and future directions).