Some Resources

R Community

R Consortium: https://www.r-consortium.org/
R Weekly: https://rweekly.org/

Blogs

RStudio Blog: https://blog.rstudio.org/
BLOGR: https://drsimonj.svbtle.com/
Mad (Data) Scientist: https://matloff.wordpress.com/
R Bloggers: https://www.r-bloggers.com/
R Consortium Blog: https://www.r-consortium.org/news/blog
Revolutions Blog: http://blog.revolutionanalytics.com
rOpenSci Blog: https://ropensci.org/blog/
Simply Statistics: http://simplystatistics.org/
Statistical Modeling, Causal Inference, and Social Science: http://andrewgelman.com/
StatsBlogs: http://www.statsblogs.com/
Win-Vector Blog: http://www.win-vector.com/blog/

Data Science

Apache Hadoop: http://hadoop.apache.org/
KDnuggets: http://www.kdnuggets.com/
R for Data Science: http://r4ds.had.co.nz/
sparklyr: http://spark.rstudio.com/
SparkR: https://spark.apache.org/docs/latest/sparkr.html
Tessera: http://tessera.io/

Statistics

Journal of Statistical Software: https://www.jstatsoft.org/index
Forecasting: principles and practice: https://www.otexts.org/fpp
From Algorithms to Z-Scores: http://heather.cs.ucdavis.edu/probstatbook
Statistical Foundations of Machine Learning: https://www.otexts.org/book/sfml
The Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf