R is the Open-Source All-rounder with a Difficult Learning Curve
Approximately three years ago, I switched from a commercial statistics solution (that was similar to SPSS) to R. Â I can now say with conviction that I don't need another tool for advanced analytics. Especially in combination with IDE "R-Studio", the software has now reached a level of maturity that allows it to be used in big data science projects without any concerns.
There is, however, no need to delude oneself that one can install R easily and get started immediately. The learning curve is comparatively steep because there are multiple ways to do things due to the variety of packages, amongst other reasons. Â Frequently, I was annoyed during my evaluation when I was suddenly tripped up by a trivial step and this meant I had to research how to solve the problem in R before continuing. Therefore, in this introduction (hopefully with many more parts to follow), I would like to present some tips and tricks that I would have appreciated knowing when I started.