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Reminder: there are hundreds of great, FREE learning resources for #rstats out there.

There's no need to sign up to take courses with a disgusting, ethically bankrupt company with sniveling, feckless leadership.
I'm completely self-taught in R. Here's a list of the FREE, OPEN materials I've used on my journey:

For data wrangling and visualization, nothing beats Hadley's "R for Data Science"

Link: r4ds.had.co.nz
Want to learn about data visualization? Check out @ClausWilke's "Fundamentals of Data Visualization." While not a book about R specifically, it's a great resource for learning what makes a good, interpretable viz. Plus, the book's code is available!

Link: serialmentor.com/dataviz/
If you want to learn about ggplot2 plotting in particular, it's best to get it straight from the source: read Hadley's ggplot2: Elegent Graphics for Data Analysis. I can't imagine why you'd want to learn it from anyone else.

Link: ggplot2-book.org
For diving into the internals of R, Hadley's "Advanced R" is a wonderful resource. I've gone back to it several times over the years and found new nuggets of info. Confused about environments or closures, or how to debug C code or NSE? Check it out.

Link: adv-r.had.co.nz
Want to learn about maps, GIS, and spatial analysis and visualization? Check out @robinlovelace & @jakub_nowosad's "Geocomputation with R", a great free resource on geographic data analysis, visualization and modeling.

Link: geocompr.robinlovelace.net
Want to learn how to spin up your own data analysis cluster and do work on "big data" in R? Check out @javierluraschi's "Mastering Spark with R." He even shows you how to fire up a rayrender render farm for complex 3D visualizations!

Link: therinspark.com
If you like the "in person" learning experience video provides and you're interested in learning more about making maps in R with rayshader, check out my open and free masterclass I taught with the @PennMUSA program:

Video: penniur.upenn.edu/events/musamas…

Link: github.com/tylermorganwal…
Finally, the R community is the best—if you ever run into seemingly intractable problems on your journey, tweet your issue with the #rstats hashtag & almost certainly you'll get help. And if that fails, try the community forums hosted by RStudio:

Link: community.rstudio.com
Learn about solve text mining problems with "Text Mining with R"! Great introduction to the tidytext package and working with text data by @juliasilge & @drob

Link: tidytextmining.com
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