 And so you're starting to work with our and you're getting basic statistics, you may find you want a little more information than the base summary function gives you. In that case, you can use something called describe. And his purpose is really easy. It gets more detail. Now this is not included in ours base functionality. Instead, this comes from a contributed package, it comes from the psych package. And when you run describe from psych, this is what you're going to get, you'll get n, that's the sample size, the mean, the standard deviation, the median, the 10% trimmed mean, the median absolute deviation, the minimum and maximum values, the range, skewness and kurtosis and standard errors. Now, don't forget, you still want to do this after you do your graphical summaries pictures first, numbers later. But let's see how this works in our simply open up this script, and we'll run through it step by step. When you open up our, the first thing we're going to need to do is we're going to need to install the package. Now I'm actually going to go through my default installation of packages, because I'm going to use one of these Pacman and this just makes things a little bit easier. So we're going to load all these packages. And this assumes, of course, that you have Pacman installed already. We're going to get the data sets. And then we'll load our iris data, we've done that lots of times before, sepal and pedal length and width and the species. But now we're going to do something a little different. We're going to load a package I'm using p load from the Pacman package. That's why I loaded it already. And this will download it if you don't have it already might take a moment and it downloads a few dependencies, generally other packages that need to come along with it. Now if you want to get some help on it, you can do P, anytime you have P and underscore that's something from Pacman, P help psych. Now when you do that, it's going to open up a web browser and it's going to get the PDF help. I've got it open already because it's really big. In fact, it's 367 pages here of documentation about the functions and psych. Obviously, we're not going to do the whole thing here. What we are going to do is we can look at some of it in the R viewer. If you simply add this argument here, web equals F for false, you can spell out the word false as long as you do it in all caps. Then it opens up here on the right. And here is actually this is a web browser, this is a web page we're looking at. And each of these you can click on and get information about the individual bits and pieces. Now let's use describe that comes from this package, it's for quantitative variables only so you don't you want to use it for categories. What we're going to do here is we're going to pick one quantitative variable right now. And that is iris and then sepal length. When we run that one. Here's what we get. Now we get a list here, a line, the first number, the one simply indicates the row number, we only have one row. So that's what we have anyhow. And it gives us the N of 150, the mean of 5.84, the standard deviation, the median, so on and so forth, out to the standard error there at the end. Now that's for one quantitative variable. If you want to do more than that, or especially if you want to do an entire data frame, just give the name of the data frame in describe. So here we go describe iris. And I'm going to zoom in on that one because now we have a lot of stuff. Now it lists all the variables down the side, sepal length, and it gives the variables numbers 12345. And it gives us the information for each one of them. Please note, it's given us numerical information for species, but it shouldn't be doing that because that's a categorical variable. So you can ignore that last line. That's why it put an asterisk right there. But otherwise, this gives you more detailed information, including things like the standard deviation in this unit that you might need to get a more complete picture of what you have in your data. I use describe a lot, it's a great way to complement histograms and other charts like box spots to give you a more precise image of your data and prepare you for your other analyses.