 For the purposes of this lesson, we're going to encourage you just to go ahead and scroll to page 10 of the accompanying PDF. We offer two options for creating the data set that we're going to be using for today. There's a shortcut, which is the one that we're going to use, and there's also creating it from scratch using code that we introduced in the previous workshop. But because that material has already been covered in that previous workshop, we're going to go ahead and just take a shortcut. And as I mentioned earlier, because this one has a lot of URLs, we're downloading a file that we've already created and stored online. I encourage you to just copy and paste this code block from your PDF just to avoid any kind of unnecessary errors. So I've already done that. So I've gone ahead and pasted this here. And as we did before, I'm going to introduce with a hashtag a title for this section. Okay, and we're using the download.file function here. The first argument in this function is just the online location of the CSV file that we're using. Okay, so you can see that this is stored in the CMU library's GitHub account. You can navigate to this directly if you want to and download it from the source location. All that is open access. The second argument in this function is the location on our hard drive where we want to store it. So we're storing this in the data output folder, which is right here. And the name of that file is going to be interviews underscore plotting. Okay, so this is an edited version of our original data set that we're going to use for these plotting exercises and it's a CSV file. Again, the mode is just an additional argument that specifies the kind of download method that R is going to use to download that file. So go ahead and run that. Your console should say that the file has been downloaded. It's 36 kilobytes. And then if you look in your data output folder, it should appear here. Okay, and so the next thing that we're going to want to do is actually store this data set in a new data object within our environment. And this is going to be stored as a data frame, which if you remember is just kind of the default data type that's used for tabular storing tabular data within R. We're going to call this object data plotting. Follow that by the assignment operator. We're using the read CSV function from the tidyverse package and then in quotation marks. I'm going to specify first the location, the folder where that data set is stored, and then just the name here and I'll just go ahead and copy and paste that from above. Okay, when you run that, you should see in your global environment that that data frame object has been created. It's got 131 observations and 45 variables. Okay, again, if you want to preview this data, you can just click here and then a tab will open here and you can just scroll around to examine that data. So you can see all the additional field names that we've created in the previous exercise. These are going to become important for the plotting exercises that we're doing here. One quick note is that you may remember in the previous exercise that we had added an additional argument to this read CSV function to indicate what R should do with the missing values, the nulls or NAs. In this case, because this data set has already been cleaned, we don't need to specify that. These are optional arguments, so we can include them whenever they apply, but if they don't apply, it's perfectly fine to leave them out.