 You may be familiar with the statistical programming language are a free open source language that is specifically developed for working with data. And it's a favorite tool of statisticians, data analysts, data scientists everywhere. One of the great things about R is how powerful it is and that really you can do anything in it. One thing you can't do, however, is use drop down menus. You have to type out lines of code and that makes it intimidating for a lot of people. One of the great things about Jamovie, which is based on R, is that it helps provide a bridge between people used to menu driven applications like SPSS and command line driven applications like R. To show the connection between the two, I have the same analysis in both a Jamovie file that's jmvpackage.omv and as an R script jmvpackage.r. Now, you're only going to be able to open that one if you have R installed on your computer and hopefully R studio too. Let me show you how Jamovie connects these two applications. Here's the analysis open in Jamovie. It's the iris data. And what I have here on the right is I have some descriptive statistics. And if you click on that, you get the commands. I have the menu. It looks a lot like an SPSS menu. I put the variables in there. I asked for certain statistics. I asked for certain plots. And we scroll down and here is the basic analysis. I have density plots and box plots for each of the variables. And it's really easy to do. If I want to do the same thing in R, it's really easy to do using the Jamovie package. But to get there, you first need to enable something called syntax mode. If you come up to the top right and click on these three dots, you'll open up a menu. And right down here under results is you have syntax mode. If you click on that, then the output here looks a little bit different. We now have a monospace font. We have some headers here. And this right here is an R command. And what you can do is you can do an option click on that or two finger click and do syntax and do copy. And then from there, you go over to R. R version of this file open. I've got some header information that I put on all of my files. You need to install the JMV, which is short for Jamovie package. And once you've got it installed, you need to load it by using library. I'm going to run that command down at the bottom. It just tells us that Jamovie now has precedence on the ANOVA command over the stats package. And we also need to load the data sets because that's where the iris data is. So I do those two things. And now I load the data. Now a funny thing here is the Jamovie command needs to know where the data is. And in R you say data is equal to and then you give the name of the data where the name of the object in the memory. And in this case, it's called data. I know it looks redundant, but it makes it very flexible as long as you come back here and you save your data set into an object called data, which I'm going to do right here. And then you can see that it's loaded over here, 150 observations with five variables. I'm going to take a look at the first few lines with the head command just to make sure it's entered right. That looks exactly like it should. And then we come here to this command from Jamovie. I've already pasted in here. It's a single sentence. This tells us that we're using the JMV package, the descriptives function. Here's the arguments that go into it. And then a few extra options. This is a single command. And when I run that command, what I get are the same tables and charts that I had in the Jamovie output. Now let me make this a little bigger over here so you can see the tables better. This is the exact same chart that Jamovie produced. It's now a monospace font, but it's set up exactly the same way. And over here on the right under plots, we have the same plots, but now it's showing them to us one at a time. And this is actually the last one and you can scroll through them and see the other charts that Jamovie produced. It's exactly the same as we had in the application. And so the purpose of this package is number one, maybe you're more comfortable with R. You can reproduce all these commands that you did in Jamovie in R. Number two, it provides a bridge. If you're familiar with SPSS and other menu driven applications, Jamovie makes it possible to set up your analyses with menus, then copy the syntax and paste it into R. And that way you can become more accustomed and learn to use R. And in fact, because the Jamovie package really contains everything you need to get, for instance, through an introductory statistics course, you only have to learn one package. And that makes it extremely efficient and extremely user friendly when it comes to learning a powerful language like R.