Speaker 1: So why is it that R is becoming such a popular and useful tool in data analysis and statistical analysis? I'm going to tell you why, stay tuned. Now the short answer is, this is one of the rare occasions when something that is free and open source is in fact better, and this is in my opinion, better than the expensive commercially available alternatives that are out there. And if you don't believe me, just look at the trends. There are masses of people moving from SPSS to R, from Stata to R, from SAS to R. I don't see anybody moving the other direction. Now R is essentially a programming language, and you might find that fact a little bit intimidating or scary, but don't. And you'll see when I do the little demonstration at the end of the video, that it's not difficult to use, that it's relatively intuitive, and you can learn it, and there's loads and loads of support out there if you need it. The importance of using code when you do data analysis, is that your analysis is reproducible. Somebody else can see exactly how it is that you got to the answers that you got to. Added to that, you've got the ability to collaborate with other people, and they can look at what you've done and make suggestions or changes or identify mistakes in your analysis. And you can't do that with a point-and-click system. And the next reason why using code to do your analysis is important, is that not only is your analysis reproducible, but it's also repeatable. In other words, if a year from now, you've got additional data, let's say you had data for 2018 and in 2019, you've got double the data set, you want to rerun that analysis, you just run your code and everything, your data cleaning, your data manipulation, and your analysis, all gets repeated right there and then at the push of a button. Now, one of the most exciting things about R is because it's open source, you've got people all over the world writing packages, and packages are things that you can install and use that deal with very specific data analytic problems. And these are free, and they are literally, thousands of them. Another big advantage of using R is that it has incredible data visualization and graphics capabilities. In fact, in that sense, it beats any other package hands down. It's a slam dunk, nothing comes close. Right, now I'm going to do a short demonstration just to show you that using a programming language to do analysis is not difficult, it's not scary, it's relatively simple. Okay, so watch this. So in this particular example, I have got a little data frame called friends, I click on that, and we can see it over here, we've got some variables, and some observations, I'm looking at age and height, let's see what we can do with those. Basically, the way the coding works is you apply a function to an object. So in this case, the function might be the mean, we want to know the mean of age, which is the object, which is 23, we might want to know the median of the height, we can plot a histogram of the age, or plot age against height. And we might want to know if there is a statistically significant correlation between age and height. And as we can see, in this particular case, there isn't. Right, so clearly, writing code is not that scary at all. I mean, I haven't broken out into a sweat, you'll notice I don't have a tremor, I don't have a heart palpitation, I haven't fallen over dead, I've actually survived. It's not difficult, it's not scary, you can do it. Now, if you're interested in learning R, then why don't you subscribe to this channel, follow the videos, I'm going to start off by just talking you through how to install R, how to install RStudio, which is where you're going to write the code, and then we're going to go through everything, data analysis, data manipulation, data visualization, all the way up to machine learning and AI. Thanks for watching, leave your comments and questions in the discussion section below. See you soon. Bye again.