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. The PDF also includes practice problems and solutions over these concepts.

In a previous post, I explained the various object types in R. Now we want to learn how to filter and transform those objects. Notice how I didn't say filter and transform the data? That's because filtering and transforming data in R heavily depend on the object type.

That's what we'll cover in this post.

Before I explain those methods though, we need to quickly cover operators.

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.

RStudio is an IDE (integrated development environment) that allows more interactivity and for you to visually keep track of what you’re doing.

In simpler words, it’s a handy user interface for programming in R.

It’s far easier to both get started and understand the R programming language by using RStudio. It makes importing data and packages easier. It also makes it easier to manage and visually review what data and packages you have already loaded. And finally, it’s just plain nicer looking.

This post was adapted from material in my current book project, R Programming in Plain English. You may download a PDF of all completed material for this book here.

Before you can download RStudio, which is the far better way to use the R programming language, you'll need to first download R base. I'll show you how to do that in this post.

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.

R uses objects to store and interact with data and there are various object types. That probably means little to you now, but understanding these differences will make R programming easier – whatever your R programming goals.

In fact, I say it’s better to understand how these objects interact with one another over memorizing every base function and package out there.

That’s different from the approach I took to learning R. When I learned R, I went straight to learning the base functions. You know, the cool stuff that does the regression analysis and confidence intervals and whatnot.

That didn’t work out well for me. I was coming from a SQL background and thought data worked in a similar way with R.

Had I started by learning the objects first, I would’ve saved a lot more time. I would’ve done less data manipulation in SQL or Excel ahead of time and made simpler, more scalable R code.