 The first thing that we need to do for our an introduction is to get set up. More specifically, we need to talk about installing R. The way you do this is you can download it. You just need to go to the homepage for the R project for statistical computing. And that's at r dash project.org. When you get there, you can click on this link in the first paragraph that says download R. And then I'll bring you to this page that lists all the places that you can download it. Now, I find the easiest is to simply go to this top one. This is cloud, because that'll automatically direct you to whichever of the below mirrors is best for your location. When you click on that, you'll end up at this page, the comprehensive R archive network or CRAN, which we'll see again in this course. You need to come here and click on your operating system. If you're on a Mac, it'll take you to this page. And the version you're going to want to click on is just right here. It's a package file. That's a zipped application installation file. Click on that, download it and follow the standard installation directions. If you're on a Windows PC, then you're probably going to want this one base. Again, click on it, download it and go through the standard installation procedure. And if you're on a Linux computer, you're probably already familiar with what you need to do. So I'm not going to run through that. Now, before we get a look at what it's actually like when you open it, there's one other thing you need to do. And that is to get the files that we're going to be using in this course. On the page that you found this video, there's a link that says download files. If you click on that, then you'll download a zipped folder called R01 underscore intro underscore files, download that, unzip it. And if you want to put it on your desktop. When you open it, you're going to see something like this, a single folder that's on your desktop. And if you click on it, then it opens up a collection of scripts. The dot R extension is for an R source or a script file. I also have a folder with a few data files that we'll be using in one of these videos. If you simply double click on this first file, whose full name is this, that'll open up in our and let me show you what that looks like. When you open up the application are you will probably get a setup of windows that look like this. On the left is the source window or the script window where you actually do your programming. On the right is the console window that shows you the output. And right now it's got a bunch of boilerplate text. Now coming over here again on the left, any line that begins with a pound sign or hashtag or octa Thorpe is a commented line that's not run. But these other lines are code that can be run. By the way, you may notice a red warning just popped up on the right side. That's just telling us about something that has to do with changes in our and it doesn't affect us. What I'm going to do right here is I'm going to put the cursor in this line and then I'm going to hit command or control and then enter, which will run that line. And you can see now that it's opened up over here. And what I've done is I've made available to the program a collection of data sets. Now I'm going to pick one of those data sets. It's the iris data sets very well known as a measurements of three species of the iris flower. And we're going to do head to see the first six lines. And there we have the sepal length, sepal width, petal length and petal width of in this case, it's all Satosa. But if you want to see a summary of the variables, get some quick descriptive statistics, we can run this next line over here. And now I get the quartiles, the mean, as well as the frequency of the three different species of iris. On the other hand, it's really nice to get things visually. So I'm going to run this basic plot command for the entire data set. And it opens up a small window, I'm going to make it bigger. And it's a scatter plot of the measurements for the three kinds of viruses, as well as a funny one where it's included in the three different categories there. I'm going to close that window. And so that is basically what our looks like and how our works in its simplest possible version. Now before we leave, I'm actually going to take a moment to clean up the application and the memory I'm going to detach or remove the data sets package that I added. I already closed the plot so I don't need to do this one separately. But what I can do is come over here to clear the console. I'm actually going to come up to edit and come down to clear console. And that cleans it out. And this is a very quick run through of what our looks like in its native environment. But in the next movie, I'm going to show you another application we can install called RStudio that lays on top of this and makes interacting with our a lot easier and a lot more organized and really a lot more fun to work with.