R programming is overwhelming to new data scientists. It was for me. I came from a SQL background. In SQL, a simple SELECT statement with a WHERE clause works in most situations. I didn't have to change things up based on the object type. Not true for R.
That meant I spent a lot of time trying to figure out how to wrangle data in R that would've been better spent on analysis.
In hindsight, a lot of these mistakes I made could've been prevented had someone given me a "list." Something to tell me the useful key principles, such as object types, that would've made the R programming language easier to learn and understand.
Well, I can't change the past, but I can hopefully save you (the new R user) some of those headaches.
Here's the list of R programming concepts you should learn ahead of time to make your R programming journey easier.
I adapted this blog post from a chapter in my upcoming book, R Programming in Plain English. You may download a PDF of all completed material for this book here.
As I said in a post a few weeks ago, R programming runs on objects. Most object types relate to the way data is stored and how it's handled. There's one object type, though, that's unique compared to the others.
That would be the function object type.
R functions allow you to script out various commands to transform and analyze data. This can be as simple as taking data from a vector and outputting a data frame. Or it could be something as complicated as a machine learning algorithm!