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!

It all depends on your own R programming goals.

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.

Packages are one of the most important concepts in R programming. It's almost hard to conceive R programming without them.

An R package stores various functions and data sets for other users to access. It allows R to move beyond its roots in statistical programming and achieve more complex goals.

For example, you might be writing a research paper. You want to clearly show the results of your regression analysis in this report, along with various tables and charts to illustrate key points. You can use a combination of Rmarkdown, ggplot2, xtable, and various other packages to accomplish this goal.

That way you don't have to copy and paste your work to a word document as you analyze the results. You merely write it and program it in R and then export it to Microsoft Word when you're done. This saves you a lot of time in the long run and makes your code far more re-producible.

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.

The Census Bureau collects useful demographic data on the US population and makes it available through API connections. Anyone can access this data for free using the R programming language. 

This post will teach you how to do that. I’ll show you how to search metadata to find what you’re looking for and then pull the specific variables you need.
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(Side note: If you’re looking for a quick overview of available data from the Census Bureau, you can read my other post here or review the Census Bureau’s API page here.)