Here are some further resources for learning R, statistics and statistics with R. All of these resources are free and open source. This is by no means a comprehensive list so feel free to add your own by issuing a pull request on GitHub or contact me directly.
You can also find additional resources on the R-Project website and also this GitHub repository.
R-Project: User contributed documentation
The R Journal: Journal of the R project for statistical computing
Swirl: An R package that teaches you R from within R
Rseek: A custom Google search for R-related sites
Learning statistics with R: A fantastic book written by Danielle Navarro which comprehensively takes you through the basics up to more complicated statistical approaches.
Exploratory Data Analysis with R: Covers the essential exploratory techniques for summarising data. Written by Roger D. Peng.
Modern Statistics for Modern Biology: A really nice book with an interesting take on learning and using statistics by Susan Holmes and Wolfgang Huber.
Answering questions with data: Aimed at Psychology students but a good read for other students. Mostly lays out statistics in a more traditional way but pretty comprehensive. By Matthew J. C. Crump.
Applied Statistics with R: A comprehensive statistics book which also contains specific chapters covering a range of statistical concepts by David Dalpiaz.
Beyond Multiple Linear Regression: Covering generalised linear models and multilevel models in R but also has a good introduction to linear models. Written by Paul Roback and Julie Legler.
Mixed Models with R: A short introductory book on using and fitting mixed models in R by Michael Clark.
R for Data Science: A tidyverse centric book for learning R and data science written by Hadley Wickham.
Statistical Inference via Data Science: Takes you from data wrangling through to inferential statistics using the tidyverse by Chester Ismay and Albert Y. Kim.