jcblum
October 4, 2018, 9:18pm
3
Confusion between R and RStudio is common! We have a disambiguation FAQ: Differentiating R from RStudio
Thanks to R's vibrant ecosystem of contributed add-on packages, it can actually be a little overwhelming to navigate all the stuff you can do with R. One starting place for browsing is the "Task Views" on CRAN: CRAN Task Views . There's also a search page that lets you do a keyword search of CRAN package documentation and task views, e.g. http://finzi.psych.upenn.edu/cgi-bin/namazu.cgi?query="natural+breaks"&idxname=functions&idxname=views
There are many textbooks about statistical methods using R. A keyword search of WorldCat is another starting point. And we have a few general threads here calling out people's favorite stats references, many of them written with R in mind:
Hi guys! I recently decided to refresh a bit some of my university learnings on statistics and have been looking for good books. Unfortunately those those that I was learning from weren't especially practice orientated - I'm looking for ones that talk about stats from a more data science, practical point of view and blend nicely some of the statistical concepts with machine learning. Would you have anything good to recommend? Thank!
Hi
I'm new to R (and statistics), and I love reading books when I learn new stuff. I've read Hands on Programming with R and I'm halfway through R for Data Science. I think they're both great and would absolutely recommended them to someone beginning with R, but the main reason I'm learning R at the moment (except for needing an excuse to learn programming) is statistics for my phd. Long-term I'd like to use it for machine learning in medical imaging.
What I've learned this far has already been tremendously helpful, however, I can't seem to find a book that actually covers statistics from A-Z using tidyverse-based methods. I have a feeling ModernDive at some point will evolve into what I'm…
If you want specific advice on R packages, it will help if you can provide more details about what you're trying to do. Taken at face value, "regression analysis" (for example) is a pretty big universe of topics!
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