Thread by @EmilyRiederer

**#rstats BLACK FRIDAY "DEALS"** (thread)

100% OFF on these awesome, always free ebooks I've read and/or recommended this year

BOGO: in true R fashion, each thoughtfully covers both code and theory

Thankful to all these authors for openly sharing such great content🙏

(1/n)

I'm sure there is a ton that I am forgetting in the below, so please feel free to add on your own favorites!

@robjhyndman 's Forecasting: Principles and Practices

otexts.com/fpp2/

Fantastic intro to forecasting building from basic principles to complex models. Also gives context to appreciate a lot of exciting work happening in {tidyverts} tidyverts.org

@topepos 's Feature Engineering

feat.engineering

Extremely practical, realistic guide focused one of the most crucial and least documented parts of model building. Wide range of methods are illustrated with fun datasets and interesting problem statements

@javierluraschi @theotheredgar 's Mastering Spark with R

therinspark.com

Getting started with Spark in so easy w {sparklyr} but you get so much more value out when you really understand the framework. Ch9's tuning tips made me far more productive and ask better q's

@_MiguelHernan 's Causal Inference: What If book

hsph.harvard.edu/miguel-hernan/…

Have only read propensity score sections yet but this book provides a wonderful, consistent narrative to pull together the diverse causal inference lit

@ClausWilke 's Fundamentals of Data Visualization

serialmentor.com/dataviz/

Beautiful plots and brilliant advice on how to avoid "ugly", "bad", and "wrong" plots. Thoughtful analysis of what makes diff viz choices superior in diff contexts. Also goldmine repo for ggplot2 tricks

@bradleyboehmke 's Hands On Machine Learning with R

bradleyboehmke.github.io/HOML/

Haven't actually read, but hearing so many great things I can't help but include. First glance, I love the "Final Thoughts" sections ending each chapter highlighting cautions / shortcomings

@hadleywickham & @StatGarrett 's R for Data Science

r4ds.had.co.nz

Classic resource for learning {tidyverse}. Ch5 and Ch7 also taught me a lot about teaching syntax / specific pkgs; strong EDA narrative is *so* much more engaging than litany of commands

@rjs 's Shape Up: Stop Running in Circles and Ship Work that Matters

basecamp.com/shapeup

Not about R / ds, but this approach to prioritizing work w well-defined bets made a *ton* of sense to me. Shaping makes a lot more sense than agile for highly ambiguous analytics work