 Okay, ggplot. I do wanna say that what I'm doing now with ggplot is a very basic introduction to the concepts, to the sort of order in which you do things with ggplot. All of tomorrow, pretty much, and some of Friday will be working in a lot of depth with ggplot because ggplot is an excellent and very adaptive tool. But you are more likely to get good value out of tomorrow and Friday, the time we spend working with ggplot if it does not hit you cold. So today, I'm just sort of briefly introducing ggplot, a little bit about how you structure commands to it. So don't worry too much if you don't get this, there will be much more to do to work with it tomorrow. But in general, when you use ggplot, you create a plot name, I use the assignation command in R, you pass it the name of the data frame, the tibble that you want to create your plot from and you pipe the command ggplot. So then you add required aesthetic mappings. What aesthetic mappings are is you have to tell it which variable from this data set you're interested in plotting. Now, some kinds of plots only need one variable, some kinds of plots need two. So according to the plot that you want to create, the required aesthetic mappings may be slightly different. So for example, a histogram, I think only needs one variable to plot, whereas a scatter plot needs two. So they each have different required aesthetic mappings. You will then add optional aesthetic mappings, things like color, line size or dot size, if you want to change the way labels are printed, things like that. These are all optional, if you do not put them in, ggplot will make the decision for you and will give you the basic output. These are all the different kinds of optional aesthetic mappings that you can choose. And there's probably quite a lot more. I just wanted to show you briefly what kind of optional aesthetic mappings you can get, things like transparency, line type, point size, scale, break points, font, all kinds of things. Really, ggplot is very powerful, gives you a lot of options. This is the key to using ggplot is you then add geometries with a plus. So you add this plus here after your ggplot has required aesthetics. gompoint, for example, will give you a scatter plot, gomhist, will give you a histogram, gombar, will give you a bar chart, all of these different things and we'll cover it. Well, we've got hist, bar, polygon, line, there's a lot of options. You can keep adding, but you should do so sensibly. So for example, if you have a scatter plot, it might make sense to add a line that shows the line of best fit, for example, the regression value that can be derived from those points. You should add sensibly, of course, there's no sense in adding a linear regression line to a histogram. That's just creating confusion for everyone. You should note that you can add aesthetics to individual geometries as well as to the plot as a whole. And whether or not you do that depends on how specific you want those aesthetics to apply. If you only want them to apply to the line, then put them, you know, there are required aesthetics, things like intercept and slope that you need for an AB line, but you could also put optionals here like color or line thickness. Yes, the aesthetics can go on the GD plot and the geometries, both of which may have required, all of which will have optional. So now let's work through some GD plot exercises. Again, at the end of the tidy verse worksheet, this is the last set of exercises for today. There is an answer sheet, a separate answer sheet in the .rmd file. And this time you just stop when you get to the end. You can, of course, work back through all of the exercises, doing all of the optional or added challenge ones that I mentioned if you see fit.