Resources

Places for learning R:

• Swirl: Learn R in R. It sounds hard but it really isn’t.
• DataCamp: A little heavy with hand-holding but it is great for beginners.
• Coursera R programming course
• Udemy R programming course
• BookDown: Collection of free open source books written by some of the top people. Especially check out these ones:
  • R for data science
  • Hands-on programming with R
  • R programming for data science
  • R Markdown definitive guide
  • R Markdown cookbook
• Gaston Sanchez, UC Berkeley: So many R tutorials and vignettes that will blow your mind.
• Statistical tools for high-throughput data analysis (STHDA): Maintained by Alboukadel Kassambara (PhD in Bioinformatics and Cancer Biology) who authored several helpful R packages including ggpubr, survminer, ggcorplot, and factoextra.
• useR! Machine Learning Tutorial: Tutorial from the R user conference 2016 focusing on using machine learning algorithms in R.

Fantastic datasets and where to find them:

• Kaggle: Community curated datasets from all sorts of disciplines
• Harvard Dataverse: Harvard-managed database containing ~100K datasets from various sources.
• Our World in Data: Numbers of the World

Websites that give you a helping hand when you are stuck:

• Stack Overflow: For coding problems
• Cross Validated: For questions about statistics and whatnot
• Biostars:Bioinformatics forum contributed by many across the globe
• R-bloggers: A repository of blogs focusing on R across the globe.

Places for understanding statistics and machine learning better

• StatQuest: A great way of learning statistics and machine learning concepts without getting into heavy mathematics.
• Introduction to Statistical Learning: Perfect for understanding how statistics and machine learning works, and it involves minimal maths.
  • Video Lectures
  • Book
• Elements of Statistical Learning: The big brother of the Introduction to Statistical Learning course above. For a more detailed dive into the concepts.