Now, another layer that you can add to your plot is a smoother, and many times you want to do this just so you can get a sense of the overall trend in the data, so I want to, you might want to get a general sense of, as PM 2.5 increases do we get more nocturnal symptoms? So on the left hand side here, these, this is this is the code down here. The bottom that I use, I have my original GG plot object, G which just had the X and Y kind of Aesthetics. I added my points to make the scatter plot and I added a smooth a geom underscore smooth function. It adds a little smoother which is the blue line and the grey confidence bands and so I used the default for the geom smooth which is a low s smoother. And which is generally fine. But you can see there's a lot of noise on the boundaries, so on the left and right side of the smoother the confidence intervals get very wide because it's flexible smoother, and the noise, there's a lot of noise on the boundaries because there's not much data there. And so I'd rather something a little bit simpler than [INAUDIBLE] I just want a linear regression line. And, and so, that's less flexible, but it still gives me a sense of the trend. So, here on the, on the right hand side, you see I've re-, I've re-made the plot, and I've added the geome smooth, but I give it, in option, I said method equals LM, and this will do a linear model instead of a low S smoother. And you can see that on the edges of the linear model, the, the uncertainty in the compass band's a lot smaller than it is for low S. And you see that generally speaking there seems to be an increasing trend with pm 2.5 and nocturnal symptoms. So, another layer we want to add is the facet. So, this is the final layer to kind of reproduce that original simple plot. That I made with Q Plot. So here the facets are going to be, are going to be determined by the BMI category. So I want to compare children who are normal weight and children who are overweight. And so that's going to be two separate plots, but then I want to plot the same relationship in each one of those windows. So that's what creates the facet. And so, I can add my facet with the the facet underscore grid variable. And this variable uses special notation which is separate. Which has kind of a variable on the left-hand side and a variable on the right-hand side. And, and they're separated by this tilde. And so, the variables that you specified in the facet function determine how the grid, how the separate plots. Are laid out on the screen. And so, the number of plots will be, will be equal to the number of levels of your factor. And so, I have two levels in my factor. I've got normal weight and overweight. Then that will create two plots for me. If I, notice I only have a single Set a factor variable that I want to separate on. So, I don't want, I'm not going to have a matrix of plots. I'm just going to have a single row of plots. And so, I'll, if I, so I'm only going to have two plots because there's two categories in this BMI variable. If I had two variables, which I, and I'll give you an example of that later. So a variable on the left-hand side and the variable on the right-hand side, the in the product of the number of factor levels is going to be the number of plots that you generate with the facets. So here I've added the facet grid function and I also added the smoother with my lm. Notice that it doesn't, it doesn't really matter what kind of order you put these things in. You can add them in Any order you want. And notice I stuck the facet grid function in between the smooth and the point and that was fine it still generated the plot. So facets are useful for, for kind of conditioning on a categorical variable. And you can see it here that the normal weight children have don't seem to have any relationship in pm 2.5 and nocturnal symptoms. Whereas the overweight children might appear to have a stringer more positive relationship. The last thing that I'll mention is you notice the labels in each of the panels I didn't, I didn't actually label them by hand, the labels come from the levels the factor variable that you condition So it's important that you use the factor variables in an intelligent way and have intelligent labels on them because the GEGE plot will take that information and automatically apply it to the labeling of your plots. And so in GEGE plot the substance is easier to modify your data and make sure the metadata is then specified appropriately rather than to kind of Plot things and then kind of modify the plot later. It's better to make sure all your data is kind of organized and labeled appropriately before you make the plot, because then that makes the plotting code much simpler. Okay, so once you've kind of made your plot often you want to modify things like the axis labels and the titles and, and other kinds of kind of plot metadata. And so there are a number of, of functions for doing that. For, for the labels there's the xlab function, the ylab function and the ggtitle function. Those are all st-, I use to specify the x label, the y label and the title. You could also use the labs function, which is a more general function that can be used to specify x, y labels or titles. It doesn't really matter what you use. Each of the geom functions, like the geom points and the geom, smooth function have options that you can use to change things, so you can explore some of those options too if you don't like the default. There are some things that only make sense kind of at a global thing, globally meaning for the whole plot, and so that the theme function is vail, is available for that. So this might specify kind of the big basic colors, the background colors are here for example the le, the position of the legend. And you can use the theme function to specify these options. Then finally there are, there are some general themes that are built in that kind of control a lot of different elements of the plot. So you can specify to kind of set many options at once and, and basically The theme_gray is kind of the default theme. It gives you the gray background with the white line, the white grid lines. The theme_bw for black and white is a kind of more stark, a plainer theme if you don't like the gray background. So, I'll talk a little bit about kind of modifying some of the features of the aesthetics. So for example, sometimes there, you might want to change the colors of the points if you don't like black points or if you don't like them to be solid. You like them to be transparent. You can, on the left-hand side, I've modified, modified my, my basic scatter plot of pm2.5 and nocturnal symptoms by doing two things. One is I've made the color steel blue. I've increased the size of the points to be 4 the default is 1. And I've, and I've changed the alpha transparency to be a half rather than solid. So now you can see that the blue, there's blue points that are a steel blue, they're somewhat transparent and they're a lot bigger than they were before. The important thing to realize that I, I've placed these options in the geo-point function and each one of these options the color, the size, the alpha, have all been set to be a constant. And so the constants Basically are, are for the are not actually aesthetics they're just options for the, for putting the points on the page. On the right hand side here you can see that the plot looks similar but I I've, I've got, there's two colors now and each of the colors corresponds to if the child is over weight or normal weight. And so now you can see what I call, what is called geom_point. Rather than say just color equals bmicat, which would assign the, the category of bmi to be a different color. I had to wrap it in this aes function which stands for aesthetics. So now, rather than specify color as a constant like steel blue The color has been assigned to a data variable. And so, it's very important that you know the distinction between, giving a, assigning a color to be a constant, like black or white or blue, and assigning color to be the value of a variable. So, when you assign color to be the value of a variable - What happens is that all the points and each set of points is assigned a different color. Then they are put back on the page along with this legend on the right hand side. Then outside of the A.E.S. function I still have my size equals 4 and my alpha equals 1/2. So those are unchanged from the left hand plot. So that the points are still bigger, and the transparency is still half. And so, so the import-, so, so the key to remember here is that there's a difference between assigning a aesthetic value to be a constant, and assigning aesthetic to be, to be the, the correspondent, or variable, data variable. If you want it to be a constant, you can just specify in the geom point function directly. If you want it to be assigned to a data variable, then you have to wrap it in this aes function. You can modify the labels with the labs function. So the labs function here I've called a few different times. I've added on to my basic plot. So the g object, remember, it's just the data frame plus the x and y coordinates. Here I've added geome point function, and I've, I've signed the color variable, the BMI category, so now the different, the two different BMI categories in, in two different colors. The labels I set the title to be max cohort, because that's where the data come from. This is a max study. I've set the x label to be this expression, which is log of pm 2.5, and I used the mathematical annotation that I talked, that we talked about previously to get that pm 2.5 that 2.5 subscript on there. And then I took the y label to just be nocturnal signal. So now it's a little bit cleaner. The plot has kind of more appropriate labels on it. I can also customize the smoother if I want to. So now, I'm going to modify the options to the geom's smooth function. So here all I've done to, is I add an option to say it's size equals four. So I made this line a little bit thicker. And I mad the, the line type to be equal three. So the three that, that just means a dotted line instead of a solid line. I still use method equals LM, so I want to regression line. And I turned off the confidence intervals, so I, so that it's a little bit more clear to see. So I see SC equals false. So these are all functions that you can add to the GM Smooth function if you don't like the defaults. So another thing you can do is just change the overall theme. So the default theme, you look in the previous slide here. You can see that there's a gray background, with those kind of white grid lines. There's this solid kind of thicker white lines for kind of larger kind of intervals. And then there's kind of thinner white lines for smaller intervals. If you don't like that background, you can change it to the kind of a more basic black and white background. So this is the theme_bw theme. And another thing, as you can see, I've changed here is, I've changed the font. So the default font is going to be something like Helvetica, but I've changed it to be a Times font. So now you can see that it's a serif font as opposed to sans serif. And it looks like, and it uses the Times font to do the labels, to do the legend, and also the axis ticks. So you can see, the numbers on the axis are all in the Times font. So the theme function, the various theme functions can be used to modify these elements of the plot.
