 One more important virtue of Factors is it allows you to get a quick overview of your data, and it does this by using these levels to essentially work as bins to collect all of your records. So I mentioned earlier with respondent floor type that even though each house has a different value in that field, you can essentially group them all into either cement earth or one of the other levels of that factor. So if we use this in conjunction with the plot function, then you can essentially see this represented as a bar graph. So let's actually to do this, we're going to create a new variable called member association. This is another field from our data set. And in order to extract that field, we're going to use interviews, followed by the dollar sign. And like subsetting the dollar sign is basically a way of just referencing a specific variable or column name from your data. Right here member association. Okay, when we do this, it basically pulls out that entire column and stores it in a new vector variable here. Now the next thing that we need to do is convert this to a factor. I mentioned earlier that this is stored just as a vector of the character data type. So we can see as factor function here and throw in member association. Again, I'm just rewriting this variable reassigning the assignment operator. Okay. So let's go ahead and take a look at the contents of that variable. When we do that, you can see that we have basically a collection of null values and yeses and noes. I'm going to use the plot function to view this as a bar graph. And when I do this, you should now see your plot being created in the plots tab in that bottom right window. Okay, and this will happen anytime you create a visualization a graph in your code here. It'll use this plots tab to display that. So we can see here now that our yeses are about 30, whereas our noes looks like it's about 65 or so. Okay. So again, a way to just kind of quickly view how these records are falling into those two bits. Now what if we also wanted to display the NA values. We can do this by creating our variable. So we're going to create the vector again. So now member association, rather than having that factor being a factor variable, it's now back to being a vector of the character data type. And now we're going to use the square brackets to extract all of the null values. And we're going to do that using the is NA function. So this is going to be embedded now in our square brackets. And then we're going to reassign all of those values to a new basically store a new value in those locations. So what this is doing is this writing this new character value here to all the records that are in a or all the cells that are in a in that member association vector. And once you do that you can check now. Remember association that instead of those in a's now everything is a character data type or yeses knows and undetermined are all actual values. They're not just nulls. The last thing that we need to do is again, for this to a factor. And now you can see here that knows yeses. And we have this new category of undetermined in the middle here. All right, that wraps up the content for this lesson. There are some additional exercises at the end on page 34 and the PDF. We encourage you to check those out to get some additional practice and then when you're all set, go ahead and click on that next video to continue with the series.