The following is a basic (and crude) crash course in Data Analytics with R for common tasks needed for data and statistical analysis - it is by no means exhaustive and (in all honesty) will have some parts that may be outdated (or soon to be), in part, due to the rapidly, evolving nature of R and associated packages. I have worked with R for the better part of 20 years, as such, some of the things written here are partly me just set in my own ways and not bothering to find the newest tools (if it ain’t broke, I’m not going to fix it). It is assumed that you have a basic understanding of the theory/assumptions of most of these tests and, therefore, the primary purpose is to expose you to the the specifics steps necessary to perform these steps (and that you will do your due diligence when it comes to verifying the veracity of your results).
Note: There will be some inaccuracies in how I describe some components in R, so no need to be pedantic; this is just a quick and dirty manual; however, if there are errors, please feel free to contact me so that I may correct them.
Update(s):
blockrand package.Archive:
Version 0.0.2: Incomplete document, updated Statistical Analysis > Advanced Methods with Statistical process chart focusing on the qcc and qicharts packages.
Version 0.0.1: Incomplete document missing Statistical Analysis > Advanced Methods.