 OK, now we're going to move on to sub-setting. So in lesson two, you might remember that we did this with vectors. So vectors only have that one dimension. They have the length dimension. And now we have a data frame that has both length and width. We have rows and columns that we can inspect here. So it works similar. It's going to have some additional features that allow us to kind of dig into both of those dimensions that we have in data frames. And just in our previous case, we're going to use square brackets to sub-set. Except whereas in the previous case, we only specified kind of one coordinate, so to speak, one number to indicate basically to pick the object up from the length dimension. In this case, we're going to specify both the column and row. So essentially two coordinates to extract these elements. And these are organized basically with rows coming first, followed by columns, separated by a column. And if I was to just leave both of these blank and run this, then essentially it's just going to return everything. Leaving a blank basically tells R that we want to use the default. And the default is to include all the rows and all the columns. On the other hand, if I was to specify that I was just interested in the third row and let's say the fourth column, well now it's going to return a table with just one dimension, so one by one, so one row, one column. Essentially it's just picking out a specific cell. And the value in that cell is 10. So this is the value that's stored in the third row and the fourth column, which is the number of members. So more often, I'm interested in picking out an entire range. The way that I do that is with the colon, which basically says that I want rows 1, 2, 3. So basically everything between those first and second values, and then maybe I just want the number of members column returned. So in this case, I have a table with three rows by one column. And what's returned here are just the elements from those first three rows, but just for that number of members column. Likewise, if I was to use a colon here, I could pick out a range and return columns 4 through 6. And that's going to give me a 3 by 3 tipple or data frame. Or again, I can leave these blank and return everything. That's all the columns with just the first three rows. Or alternatively, I can return all the rows just those three columns. So a lot of options here for subsetting. And I just encourage you to continue to play with it. For more practice with this skill, you can check out pages 29 and 30 of the PDF. We've got some exercises there that will run through some additional examples.