 If we're going to talk about coding and data science and the languages that are used, the first and foremost is R. The reason for that is, according to many standards, R is the language of data and data science. For example, take a look at this chart, this is a ranking based on a survey of data mining experts of the software that they use in doing their work. And R is right there at the top, R is first. And in fact, that's important because there's Python, which is usually taken hand in hand with R for data science. But R sees 50% more use than Python does, at least in this particular list. Now there's a few reasons for that popularity. Number one, R is free and it's open source, both of which make things very easy. Second, R is specially developed for vector operations. That means it's able to go through an entire list of data without having to write four loops to go through it. If you've ever had to write four loops, then you know that that would be kind of disastrous having to do that with data analysis. Next, R has a fabulous community behind it. It's very easy to get help on things with R. You Google it, you're going to end up in a place where you're going to be able to find good examples of what you need. And probably most importantly, R is very capable on its own, but there are 7000 packages actually as many more than that 7000 packages that add capabilities to R. Essentially, it can do anything. Now, when you're working with R, you actually have a choice of interfaces. That is, how do you actually do the coding and how do you get your results? R comes with its own IDE or interactive development environment. You can do that. Or if you're on a Mac or Linux, you can actually do R through the terminal through command line. If you've installed R, you just type R and it starts up. There's also a very popular development environment called R studio. And that's actually the one that I use and I'll be using for all my examples. But another new competitor is Jupiter, which very commonly used for Python. It's what I use for examples there. It works in a browser window, even though it's locally installed. And R studio and Jupiter, there's pluses and minuses to each one of them. I'll mention them as we get to them. But no matter what interface you use, ours command line, you are typing lines of code in order to get the commands. Some people get really scared about that. But really, there are some advantages to that in terms of the replicability and really the accessibility, the transparency of your commands. So for instance, here's a short example of so commands in R, you can enter them into what's called the console. And that's just like one line at a time, that's called an interactive way. Or you can save scripts and run bits and pieces of them selectively. That makes your life a lot easier. No matter how you do it, if you're familiar with programming other languages, then you can find that ours a little weird, it has an idiosyncratic model. It makes sense once you get used to it. But it is a different approach. And so it does take some adaptation if you're accustomed to programming in other languages. Now, once you do your programming to get your output, what you're going to get is graphs in a separate window, you're going to get text in numbers, numerical output in the console. And no matter what you get, you can save the output to files. So that makes it portable, you can do it in other environments. But most importantly, I like to think of this, here's our box of chocolates, where you never know what you're going to get. The beauty of our is in the packages that are available to expand its capabilities. Now there are two sources of packages for R. One goes by the name of CRAN and that stands for the comprehensive R archive network. And that's at cran.rstudio.com. And what that does is it takes the 7000 or so different packages that are available, and it organizes them in topics that they call task views. And for each one, if they've done their homework, you have data sets that come along with that package, you have a manual in PDF format, and you can even have vignettes where they run through examples of how to do it. Another interface is called crantastic. And the exclamation point is part of the title. That's at crantastic.org. And what this is is an alternative interface that links to CRAN. So if you find something like in crantastic, and you click on the link, it's going to open in CRAN. But the nice thing about crantastic is it shows the popularity of packages. And it also shows how recently they were updated. And that can be a nice way of knowing that you're getting getting sort of the latest and greatest. Now, from this very abstract presentation, we can say a few things about R. Number one, according to many, R is the language of data science. And it's a command line interface, you're typing lines of code. So that gives it both a strength and a challenge for some people. But the beautiful thing is the thousands and thousands of packages of additional code and capability that are available for her that make it possible to do nearly anything in this statistical programming language.