R should print out 5 right underneath where you typed 2+3, and then it should give you a new prompt ( > + blinking cursor) immediately below that. Where see you that prompt ( >), type the following statement: 2+3 That > symbol is a prompt it’s R’s way of saying, “Tell me what to do by typing statements right here.” Below I’ve circled it in red: More specifically, focus on the blinking cursor in the console, right next to the angle bracket ( >). 2 That’s why you need to install R in order for RStudio to work: if you try to drive a car without an engine, you’re not going to get very far.įor now, just focus on the left-hand panel (the console). Loosely speaking, if you imagine that analyzing data is like driving a car, then RStudio is the steering wheel and the pedals, while R is the engine. You’ll work entirely within RStudio meanwhile, RStudio will interface with R behind the scenes, without your ever needing to open the R program itself. Why two programs? Well, R is the program that does most of the raw computational work, while RStudio is a graphical front end for R (called an “integrated development environment,” or IDE) that offers a lot of creature comforts. If prompted to choose your version, you’ll want “RStudio Desktop,” which is free and, like R, available for Windows, Mac, and Linux. Note that if you have a newer MacBook with an Apple M1 chip, make sure to install the appropriate version of R (labeled Apple silicon arm64 on the download page), and not the version designed for older Intel-based MacBooks. Head to to download and install the version of R appropriate for your computer, whether Windows, Mac, or Linux.To begin using R, you’ll need to download two pieces of software: Application: modeling long-term asset returns.When is the normal distribution an appropriate model?.17.3 The normal distribution, revisited.One possible solution: stepwise selection.Example: predicting the price of a house.15.6 “What variables should I include?”.Statistical vs. practical significance, revisited.15.2 Interactions of numerical and grouping variables.Example 1: causal confusion in house prices.15.1 Numerical and grouping variables together.14.3 Models with multiple dummy variables.12.5 Example: labor market discrimination.The basic recipe of large-sample inference.10.2 The four steps of hypothesis testing.10.1 Example 1: did the Patriots cheat?.9.5 Bootstrapping usually, but not always, works.Bootstrap standard errors and confidence intervals.
What the sampling distribution tells us.
7.3 Using and interpreting regression models.2.6 Importing data from the command line.Data Science in R: A Gentle Introduction.
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