Lecture 3: Introduction to R and RStudio

Nick Huntington-Klein

January 17, 2019

Welcome to R

The first half of this class will be dedicated to getting familiar with R.

R language for working with data. RStudio is an environment for working with that language.

By the time we’re done, you should be comfortable manipulating and examining data.

Why R?

This is going to accomplish a few things for us.

  • As we mentioned last week: Excel/Sheets is a great tool for accountants, not for working with data.
  • Learning to program is a highly valuable skill
  • R is basically a big cool calculator, and we want to do big cool calculations

A few notes

  • These slides are written in R; you can look up the code I’m using on Titanium if you want to see how I did something
  • The assigned videos are there to help
  • Other resources on the Econometrics Help list, available on Titanium
  • Don’t be afraid of programming! It’s a language like any other, just a language that requires you to be very precise. Tell the computer what you want!
  • If you’re already a programmer, you may get a little bored. If you’d like to learn something more advanced, let me know.
  • Ask lots of questions!!! (!)


  • Get used to working in RStudio
  • Figure out how R conceptualizes working with stuff
  • Figure out the Help system <- Important!

The RStudio Panes

  • Console
  • Environment Pane
  • Browser Pane
  • Source Editor
  • Li’l tip: Ctrl/Cmd + Shift + number will maximize one of these panes


  • Typically bottom-left
  • This is where you can type in code and have it run immediately
  • Or, when you run code from the Source Editor, it will show up here
  • It will also show any output or errors

Console Example

  • Let’s copy/paste some code in there to run
#Generate 500 heads and tails
data <- sample(c("Heads","Tails"),500,replace=TRUE)
#Calculate the proportion of heads
#This line should give an error - it didn't work!
data <- sample(c("Heads","Tails"),500,replace=BLUE)
#This line should give a warning
#It did SOMETHING but maybe not what you want
#This line won't give an error or a warning
#But it's not what we want!

What We Get Back

  • We can see the code that we’ve run
  • We can see the output of that code, if any
  • We can see any errors or warnings (in red). Remember - errors mean it didn’t work. Warnings mean it maybe didn’t work.
  • Just because there’s no error or warning doesn’t mean it DID work! Always think carefully

Environment Pane

  • The output of what we’ve done can also be seen in the Environment pane
  • This is on the top-right
  • Two important tabs: Environment and History
  • Mostly, Environment

History Tab

  • History shows us the commands that we’ve run
  • To save yourself some typing, you can re-run commands by double-clicking them or hitting Enter
  • Send to console with double-click/enter, send to source pane with Shift+double-click/Enter
  • Or use “To Console” or “To Source” buttons

Environment Tab

  • Environment tab shows us all the objects we have in memory
  • For example, we created the data object, so we can see that in Environment
  • It shows us lots of handy information about that object too
  • (we’ll get to that later)
  • You can erase everything with that little broom button (technically this does rm(list=ls()))

Browser Pane

  • Bottom-right
  • Lots of handy stuff here!
  • Mostly, the outcome of what you do will be seen here

Files Tab

  • Basic file browser
  • Handy for opening up files
  • Can also help you set the working directory:
    • Go to folder
    • In menu bar, Session
    • Set Working Directory
    • To Files Pane Location

Plots and Viewer tabs

  • When you create something that must be viewed, like a plot, it will show up here.
plot(density(LifeCycleSavings$pop75),main='Percent of Population over 75')
  • Note the “Export” button here - this is an easy way to save plots you’ve created

Packages Tab

  • This is one way to install new packages and load them in
  • We’ll be talking more about packages later
  • I generally avoid this tab; better to do this via code
  • Why? Replicability! A VERY important reason to use code and not the GUI or, say, Excel
  • You always want to make sure your future self (or someone else) knows how to use your code
  • One thing I do use this for is checking for package updates, note handy “Update” button

Help Tab

  • This is where help files appear when you ask for them
  • You can use the search bar here, or help() in the console
  • We’ll be going over this today a bit later

Source Pane

  • You should be working with code FROM THIS PANE, not the console!
  • Why? Replicability!
  • Also, COMMENTS! USE THEM! PLEASE! # lets you write a comment.
  • Switch between tabs like a browser
  • Set working directory:
    • Select a source file tab
    • In menu bar: Session
    • Set Working Directory
    • To Source File Location

Running Code from the Source Pane

  • Select a chunk of code and hit the “Run” button
  • Click on a line of code and do Ctrl/Cmd-Enter to run just that line and advance to the next <- Super handy!
  • Going one line at a time lets you check for errors more easily
  • Let’s try some!


  • RStudio comes with autocomplete!
  • Typing in the Source Pane or the Console, it will try to fill in things for you
    • Command names (shows the syntax of the function too!)
    • Object names from your environment
    • Variable names in your data
  • Let’s try redoing the code we just did, typing it out


  • Autocomplete is one way that RStudio tries to help you out
  • The way that R helps you out is with the documentation
  • When you start doing anything serious with a computer, like programming, the most important skills are:
    • Knowing to read documentation
    • Knowing to search the internet for help


  • You can get the documentation on most R objects using the help() function
  • help(mean), for example, will show you:
    • What the function is
    • The “syntax” for the function
    • The available options for the function
    • Other, related functions, like weighted.mean
    • Ideally, some examples of proper use
  • Not just for functions/commands - some data sets will work too! Try help(mtcars)

Searching the Internet

  • Even professional programmers will tell you - they spend a huge amount of time just looking up how to do stuff online
  • Why not?
  • Granted, this won’t come in handy for a midterm, but for actually doing work it’s essential

Searching the Internet

  • Just Google (or whatever) what you need! There will usually be a resource.
  • R-bloggers, Quick-R, StackOverflow
  • Or just Google. Try including “R” and the name of what you want in the search
  • If “R” isn’t turning up anything, try Rstats
  • Ask on Twitter with #rstats
  • Ask on StackExchange or StackOverflow <- tread lightly!


  • We’ve picked up some data sets to use like LifeCycleSavings and mtcars with the data() function
  • What data sets can we get this way?
  • Let’s try help(data)
  • Let’s try searching the internet

That’s it!

See you next time!