Alright so in this lecture I'm going to talk about ggplot, but I'm going to talk about some of the more kind of, underlying fundamental aspects of ggplot. In the last lecture I talked mostly about the qplot function and how you could use the, the qplot function to kind of make your basic plots scatterplots, bar charts, things like that. This is in some sense a replacement for the base, for the plot function in R, which is, which we use the base plots to create the same kinds of things, scatter plots and histograms. So the qplot function's very handy, but it doesn't reveal a lot of the underlying infrastructure that kind of, is built into ggplot that allows you to make all kinds of custom plots. So I just wanted to highlight a couple of the basic ideas in this lecture. I don't really have time to go over every single little detail of the ggplot package, because it's so big. So I thought I would try to get you started on a couple of basic fundamentals. If you have a lot more interest in developing plots using the ggplot framework you can you can, there are a lot of references that I mentioned in the previous lecture in terms of the ggplot2 book and other types of books. So I encourage you to look at those resources if you want to explore this type of plotting framework further than what we discuss here in the course. So, let's get started. Just to review a little bit in terms of what is ggplot2. Remember it's an implementation of the grammar of graphics. Which was developed by Leland Wilkinson. And the grammar of graphics basically describes a re, an abstract representation for graphics. And so that the idea is that it's kind of the verb, noun, adjective for plots. So what are the basic elements that can be modified, and cannot be modified, and things like that? So that's a theory of graphics that you can kind of build on to develop new graphics using this kind of abstract language. So some of the basic components of a ggplot kind of graphic, are, you need data. Of course, the data is the most important thing, that's what you're going to put on to the page, and so you need to have a data frame that has all the data that you're going to plot in it. You need something that's called an aesthetic mapping, these are, these describe how the data are mapped to things like colors and sizes and points. Geoms are specific objects that you're going to put onto the page, like things like points or lines or tiles or shapes or whatever, you have it. Facets are, are used for conditional plots, so if you're going to have multi, typically you want to have multiple panels of plots, where it might show the relationship of something as something as a third variable changes that, that's a facet. And then stats are statistical transformations like smoothing, binning, quantiles, regression, things like that. Scales are to find how the different variables are coded, in terms of the plot. So, for example, you might have a binary variable that represents sex. So you let the males will be red, and the females blue, or something like that. And so that is a scaling for a particular type of dichotomous variable. And lastly, you need a coordinate system which we won't focu, focus on too much on this lecture. But you need a coordinate system that tells you how certain numerical representations get translated onto a plot. So when you build a plot with ggplot2, the basic idea if you're not going to be using the qplot function, you'll want to kind of build it piece by piece. You can kind of think of it as the artist's palette model. So this is the, similar to the model in the base plotting system but where you kind of start with something, and you kind of add things piece by piece. So unlike the lattice system, where you had to have the whole plot is encoded in a single function call, you can start with a single call to, you can start with a ggplot function. And then you kind of add things on piece by piece, maybe, as you think of them, or as you see things in the plot, you make little changes. You can kind of add things here as you go. So the idea is that plots are built up in layers. Okay. And so, the most fundamental layer of course is the data. So the first thing you usually start with, is you kind of tell ggplot about the data that you're working with, and some basic aesthetics. Like, if you're making a scatter plot, here are the x and y variables. Then you might overlay a summary. For example, you might overlay a statistical summary like a smoother or progression line. Something like that. And on top of that you might have some metadata or extra annotation for the plot. So the running example that I am going to use for this lecture I spoke about previously has come from the mouse allergen and asthma cohort study which was conducted here in Baltimore. Just very briefly, this is a study that included Baltimore children, age 5 to 17. They all had persistent athsma, with an exacerbation in the past year. And we're interested in a lot of the environmental triggers for asthma morbidity in this study. So some of those, so one of those triggers we're going to look at is indoor air pollution, as as specified by particulate matters, so fine particulate matters which is PM 2.5. And we'll also look at nitrogen dioxide, NO2. And I want to know whether BMI or body mass index modifies the relationship between PM 2.5 and asthma. So do children with different BMIs have a, respond differently in terms of their asthma symptoms to exposure to PM 2.5. So that's the kind of basic question that we want to answer here. So here's a, a basic plot. I made this using the qplot function. You can see the code down there at the bottom. And and the basic idea is I plotted the relationship between, I've taken the kind of scientific question in the previous slide, and translated that into a plot. So I plotted the relationship between nocturnal symptoms here. So these are the number of days in the past two weeks where there were symptoms at night. So, symptoms while sleeping for example. And so, so the, I plotted the relationship between nocturnal symptoms and the in the log of the PM2.5 indoor value. And you can see that, you know, roughly speaking, on the left side we have all the normal weight children and there doesn't seem to be a very strong relationship between log PM2.5 and nocturnal symptoms. Now the right side we have the overweight or obese children and there seems to be an increasing relationship between PM 2.5 and symptoms. And so you can see that there's perhaps some evidence of an interaction between the BMI and PM 2.5, because the relationship between PM 2.5 and symptoms appears to differ by BMI status. So, of course, in order to formalize this, you'd probably want to fit some sort of model, do a little bit more thorough analysis, of course, and adjust for various things. But we're going to stick, stick here with this plot for the moment. So you can make this plot very easily with the qplot function and by adding things like facets and geoms, to the plot. But I'm going to recreate this plot using the kind of more, the, the lower level ggplot framework. And so how do we build this up layer by layer? First, so first we start with the data, right? So here I've got a little excerpt of the data frame. And you can see that there are three variables here. There's logpm2.5, there's BMI category, and that's two levels, there's normal weight and overweight. And then I have the number of days with nocturnal symptoms. So that's going to be a number between 0 and 14, because it's over the last two weeks. And so you can see, so the data are very important in any ggplot graphic, because almost everything that you put on the plot is a, can be thought of as some sort of data. And then in the first, in the next line here I've got my initial call to ggplot. You can see I just call ggplot and I give it the data frame. I give it a pair of aesthetics. I give it the x and the y variables, which are logpm2.5 and NocturnalSympt. And then, and then I save this to a plot, to an object that I call g. And if I call summary on this object, you can see, I get, it shows you what the data are, shows you what the the mapping, the aesthetic mapping is and it shows you some, something about faceting, which, in which case there's no faceting. So, the, that's null. So, you can always call summary on a ggplot object and it'll give you a quick kind of list of what's in this plot and what are the data. Now, the important thing to realize is that I, I haven't made a plot yet. There's no pic, if you run this code, there will be no picture on your screen yet. So, for example, if here I, I called ggplot, and if I, if I say print g, I immediately get an error, because it says there's no layers on this plot. And so, what does that mean? It basically just says it doesn't know how to, how to draw the data yet. In the sense that it doesn't know if you want points, it doesn't know if you want lines or tiles or other kinds of polygons or shapes, and so it doesn't have enough information yet to draw the plot on the screen. So the next line here, I add, so notice I use the plus operator here, so you can literally add things to your plot by using the plus operator. And so when I add the geom_point function, this and I save it to a new object called p. So my new, this p object is also a ggplot graphic, but it's got this geom_point thing added to it. Now when I print that out and print that out, I get a plot, I get a picture on the screen. So notice that the geom_point function was called with no arguments. I didn't have to tell it about the data, I didn't have to tell it where the points were, I didn't have to tell it anything. So, there are a bunch of defaults that it will, that will get used, but the point is that the original ggplot object, which is encoded in this g object, has all the information that, that, that, that the geom_point function will need. And so I don't have to separately specify it again. So, I don't, in this first example here I saved the modified object to something called p, but you, you, don't have to do that. I, I, could have just said g plus geom point and then the plot will have auto printed to the screen. So you can use auto printing, or if you want to save the object, you can. So here's the plot, and I, and with the point layer added on to it. So I reproduced the code down here at the bottom, just for your reference, and you can see that the default for the points, are a solid circle, filled circle. And and so I used the default because I didn't specify anything different. So here, here are the data.
