I will never say that I am an R expert because there are over 10,000 R packages and functions. There are many different reasons to use R as I outlined earlier and you must figure out what works best for your purposes in R. I have staple R packages and functions that I employ. I am not going to wrangle data without Dr. Hadley Wickham’s tidyverse R package. Any Data Scientist can attest to the hours, days, and weeks it takes to wrangle data in structured and unstructured formats to a tidy format before the data analysis, data modeling, and data visualization stages can begin. I employ a combination of tidytext R package and tm R Package for text mining (machine learning algorithm). RScripts and outputs should be reproducible like research should be reproducible. When I work on a project in R, I leave # notes documenting my decisions executed in case I need to share my code with someone to reproduce. Note: # is employed to let R know to not execute that line as code. I also leave # notes to remind myself months down the road why I made the decisions I made in a previous project.
I want to share snippets of RScripts and outputs that I have employed in R for different projects. Please note that the RScripts and outputs below were preceded by many steps that are not depicted below.