 It's LinkedIn Learning author Monica Wahee with today's data science makeover. Watch while Monica Wahee demonstrates how to count the number of rows in a data frame in R. It's Monica Wahee here with our line items dataset. Remember, it's my singularly favored fake dataset. See, I already read in the line items dataset with the read RDS command. If you want to know more about that command, just look for the link to my other video in the description. Then after that, I ran the object line items and that printed it to the console so you can see here. See, not a very exciting dataset. Let's look at how long it is. See that it is 95 rows. Now let's say we wanted to know that by running a command. We could just run the N row command on the data frame line items, like that code here. N row stands for number of rows, which is nice. Not all R commands are that logical. The N row command only needs one argument in the parentheses, the data frame. Okay, so let's run this and see if R gets the answer right. See that? R did get the right answer, 95. Now I tend to run N rows on the before and after dataset when I am removing rows by criteria. That way I can keep my finger on the pulse of how much data are getting removed. I can make sure my data frame is not getting gutted. What can be powerful and can really open doors for you in automated processing is storing the result of an N row into a variable. Then you can compare those variables or even put them in arithmetical equations. See here, this is an arrow with a variable to the left of it called line items underscore num rows. So that's me shoving some value into that variable, line items underscore num rows with an arrow. And you know what that is? That is our N row command that comes out to 95. See what I'm doing? I'm saving the value of the N row of line items in the line items underscore num rows variable. Let me run this code and demonstrate. Then when I run this new object I made, line items underscore num rows, it should say 95. And it does. Look at that. So nice. Very nice. Looking good. And that's today's Data Science Makeover. Thank you for watching this Data Science Makeover with LinkedIn Learning author Monica Wahee. Remember to check out Monica's Data Science courses on LinkedIn Learning. Click on the link in the description.