 Okay, now that we have our file structure set up, we can go ahead and download the source data set for this exercise, which is located online. Because this next bit of code contains a long URL, and we don't want you guys to make any mistakes. I encourage you to just go ahead and copy that URL from the PDF that we provided and go ahead and paste that little block of code. The function that we're going to be using here is download file, and then in parentheses I'm going to paste that little code block from the PDF. Just to walk you through the contents of this function here, the first element, which is called an argument in R, is just the URL where the source data set is located. The second one, also in quotation marks, is the location where we're telling R to store our data set, and also the name that we want to get it. So in this case, we're storing it in the data folder, and we're calling that file SAPI Clean. SAPI is the name of the study where the data set's coming from, and it's clean because this has already been a cleaned version of the data from some previous exercise. And this just talks about the downloading mode. This is not super important for the purposes of this session here. It just talks about the writing mode when you're downloading an online file. Okay, and when you execute this, you're going to see that the console is telling you that it's downloaded 21 kilobytes, and now if we were to actually go over here and scroll into our folder and open up the data folder, you can actually see that that SAPI Clean data set has been stored there. Okay? And we can move back up to the parent folder just by clicking these two dots by the green arrow. And just to keep consistent with what we've been doing, we'll go ahead and leave some documentation of what we're doing here, just indicated we've been downloading the data. And the next thing that we're going to introduce you to is how to install packages. And the way that you think about packages in R is basically little toolboxes of functions that other people have created that basically simplify a lot of the processes that we're going to be running in our exercise. So what we're using now up to this point is what's called base R functions. These are the tools that come with the basically the base version of R. There's nothing super fancy there, but it allows you to do kind of a lot of the more simple functions. But when you're doing more kind of sophisticated statistical analysis, you're going to need the additional tools in your toolbox. And the way that you get those is by downloading these packages. These packages are created by the community of our users. These are people with a bit more advanced development skills. So they're saving us a lot of work by creating useful tools for us that we don't need to write from scratch. And we can just import those through these packages. So the one that we're going to use for the purposes of this exercise is called tidyverse. And tidyverse contains a lot of the statistical tools that we're going to be using for cleaning, reorganizing our data, and a lot of other useful things that we might get to later on. So we're first going to just give ourselves some documentation here that we're going to install packages. And the first function that we're going to use is the install packages function. And this goes in quotation marks. I'm going to type tidyverse. When you execute this, you'll see if it's the first time that you've installed this package, you might see some kind of script running down here in the console. That's okay. It can be scary at first because a lot of that's red, but don't worry, red is not me. I'm not going to tell you anything. It's breaking. It's just kind of telling you what the computer is doing in the background. A lot of those lines are also telling you other kinds of dependencies, other kinds of things that are being installed along with the tidyverse package. In my case, because I've done this before, it didn't take very long. And it's telling me now where those packages are stored. Now if you wanted to confirm that that install worked properly, you can use another function called installed packages and just run it without any arguments, so just with the parentheses there. And down here in your console, you'll see a list of different packages that you've already installed. Okay. And the details of this aren't super important. As I mentioned, a lot of these are kind of dependencies, things that are necessary for tidyverse to work properly, and we won't be using these. And it's also just a partial list. So it's only kind of helpful if you have a short list of packages there. Another way to look at this is actually using your packages tab in the bottom right window. And here you can both install packages by using this install button. And you can also just confirm which packages have already been installed by checking this list. Okay. If you want to search for a specific package, you can use this search bar. And if I type in tidyverse, you'll see that tidyverse is right here in that list indicating it's already been installed. Okay. Alternatively, you can do the exact same thing and actually specify the repository that you want to download this package from. Most of our packages are coming from the CRAN repository. This is the same place that if you did the R install using our setup instructions, this is the same place that that application was stored. And it also tells you where you can store it if you wanted to specify which library you wanted to install that package to. And you can see here that the box is checked for install dependencies. In some cases, like I mentioned, these packages will rely on other packages. And installing dependencies is just a good way to make sure that all the tools in that package run the first time that you install it. So I encourage you to go ahead and check that box. You can do the exact same thing with this code by adding an additional argument after tidyverse, which is just to type in dependencies equals true. Okay. And that would do the same thing. And you can see that as we've been doing this, R has some helpful shortcuts that I want to call your attention to. As you're typing a function, it will actually provide kind of this predictive text to suggest some possibilities that you might be looking for. And this just makes it a little bit easier when these names kind of slip your mind or whatever the case might be. You can take advantage of these. And there's also some helpful kind of documentation about what the function does, what the arguments that it takes are all here. And this will help to pop up. Okay. And actually, as this is coming up, if you type in tab, and that will basically choose the first recommendation on that list. So it's saying, do you want to choose dependencies? If that's the one that I'm looking for, just hitting the tab button will go ahead and insert that. Okay. And that wraps up our first lesson in this series. In the next one, we'll talk about creating variables, different objects in R. We'll also talk about data types and how to deal with missing values and a lot of other things. So go ahead and click on the next link to continue the series.