These are books and other resources that I myself consult a lot (yes, I do consult my own books; can’t keep it all in my head :-) ), plus others I recommend.
Nonprogramming Coverage of R
Andrie de Vries and Joris Meys, R For Dummies (second edition), For Dummies
Jaren Lander, R for Everyone: Advanced Analytics and Graphics (second ed.), Addison-Wesley
R Programming and Language
John Chambers, Software for Data Analysis: Programming with R, Springer
Dirk Eddelbuettel, Seamless R and C++ Integration with Rcpp, Springer
Colin Gillespie and Robin Lovelace, Efficient R Programming: A Practical Guide to Smarter Programming
Norm Matloff, The Art of R Programming, NSP
Norm Matloff, Parallel Computation for Data Science, CRC
Hadley Wickham, Advanced R (second edition), CRC
Data Science with R
Nina Zumel and John Mount, Practical Data Science with R, Manning (2nd ed.)
Hadley Wickham and Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O’Reilly
Graphics in R
Winston Chang, R Graphics Cookbook: Practical Recipes for Visualizing Data, O’Reilly
Paul Murrell, R Graphics (third edition), CRC
Deepayan Sarkar, Lattice: Multivariate Data Visualization with R, Springer
Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer
Regression Analysis and Machine Learning, Using R
Francis Chollet and JJ Allaire, Deep Learning in R, Manning
Julian Faraway, Linear Models with R, CRC
Julian Faraway, Extending the Linear Model with R, CRC
John Fox and Sanford Weisberg, An R Companion to Applied Regression, SAGE
Frank Harrell, Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, Springer
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Introduction to Statistical Learning: with Applications in R, Springer, 2nd ed.
Max Kuhn, Applied Predictive Modeling, Springer
Max Kuhn and Kjell Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models, CRC
Norm Matloff, Statistical Regression and Classification: from Linear Models to Machine Learning, CRC
Norm Matloff, The Art of Machine Learning: Algorithms+Data+R, NSP, coming soon
Other
Rob Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, OTexts
Ted Kwartler, Text Mining in Practice with R
Norm Matloff, Probability and Statistics for Data Science: Math + R + Data, CRC
Julia Silge and David Robinson, Text Mining with R: A Tidy Approach, O’Reilly
Yihui Xie et al, R Markdown: The Definitive Guide, CRC
Web
I also would recommend various Web tutorials:
Szilard Palka, CEU Business Analytics program: Use Case Seminar 2 with Szilard Pafka (2019- 05-08)
Hadley Wickham, the Tidyverse