The lattice plotting system in R is a plotting system that's different from the base plotting system and it works very differently. And it's useful for plotting, often for plotting kind of high dimensional data and for making lot, many many plots at once. And if you recall in the base plotting system it was possible to put multiple plots on the same graphics device using the mfrow or mfcall arguments. And you can make panels and plots to look at many different feature. And the lattice plotting system is kind of designed to really make, to optimize that type of plot and to make very high density plots and so it can be useful in many different situations. And so I'll talk about some of the, the functions in the lattice system here. So, the lattice plotting system is, is implemented in the lattice package. So you have to load the package in order to use it. Underlying the lattice package is what's called the grid package which implements a lot of the kind of low level plumbing of the, of the lattice graphic system. And you, you typically won't have to call functions from the grid package directly but it's useful to know that underneath there are a lot of, there's a lot of code that is implemented in the grid package. And so the lat, lattice package builds on top of grid and gives you a number of kind of user functions to, to employ. the, the one thing about the lattice plotting system is, is that it does not have a kind of two phase aspect like the base plotting system did. In where you kind of in the base plotting system, if you recall, you kind of create the plot and then you annotate it with separate functions. Here in the lattice plotting system, you have to create the entire plot all at once in a single function call. And so you have to specify all the details within that function call and, because you won't have a chance really to annotate it afterwards. So some of the main functions in lattice are xyplot. This is probably the most important function. It's basically used for making scatter plots. There's bwplot for bock, for boxplots, histogram for histograms. Stripplot is kind of like a boxplot, but there's but it uses points. The dotplot plots points on where, where it will look like a violin strings. Splom is for scatter plot matrix. It's kind of like the pairs function in the base plotting system. And levelplot and contourplot are for plotting image data. And so, I'll just talk a little bit about the xyplot right function right now. Because I think it's the most important. The basic function call for xyplot is that you have a formula kind of notations. So you have y ~ x. So the y is going to be whatever's on your y-axis and x is going to be whatever's on your x-axis. And then you have this vertical bar which separates what are called, conditioning variables. And so f and g are going to be categorical variables that you condition on. So for example, and the idea is that you want to you can kind of, to interpret this function call in words, the idea is that I want to look at the scatter plot of y and x for every level of f and g, right? So, and you don't have to use two different categorical variables. I just wanted to show that you could. And so, and then the second argument is the data argument. And this is the data frame where the variables x, y, f, and g can be found. If there's no data frame there, then it will look in your workspace for the variables to plot. So here's just a very simple scatter plot. I load the lattice package and the data sets package, and I'm plotting the ozone and wind variables from the air quality data frame. So you can see I just did ozone ~ wind, so I got ozone on the y-axis and wind on the x-axis, and this is your basic scatter plot which looks very much like a scatter plot in the base plotting system. Notice that the defaults are a little bit different. It does use open circles, but they're colored blue by default. Now, here's a slightly more complicated lattice plot. So here I've, I've gotten the same data frame, but I've converted the month variable into a factor. And now I'm plotting ozone versus wind by month. So you can interpret the formula as, I want to look at the relationship between ozone and wind for each level of month and so month goes from five, six, seven, eight, and nine. So from the month of May to the month of September. And you can see the relationship between ozone and wind. It appears to change across the month. So there doesn't appear to be you know much relationship in May or June. But you can see in, in July and August, there's a relatively strong relationship. There is a negative one there, so more wind means less ozone. And you can see by September that relationship is starting to die down a little bit. So I, it's very easy, so that's one of the pow, the power of the xyplot function in lattice is that I can make these multidimensional panels very simply with just a single function call. And you recall in the base plotting system, I would, there would've been many, many function calls create this plot. And here I just used a single line. So now I'm, so now I'm looking at three different variables here, the month, the ozone, and wind. And, and they're all kind of arranged in one panel, which is very convenient. So the lattice functions behave a little bit differently, and it's worth noting the difference between lattice and the base graphics. Base graphics functions plot data directly to a graphics device. So if you recall there's the screen device, there's PDF files, there are all kinds of graphics devices. What lattice functions do is they, they don't actually, they don't specifically plot anything. Uh,hat they do is they return an object of class trellis. And then the object has to be printed in order for the data in the plot to kind of go to the graphics device. So there is a two-stage aspect there, but most of the time this aspect is invisible to the user. Because what happens is that the u-, when you call a function like xyplot, it returns an object of class trellis, but then it auto prints that object. And so, the auto-printing feature of R will make it so that it automatically sends the plot to the graphics device. So most of the time you don't have to worry about this two step process. But it's worth noting that you can, in theory, save the plot object to an R object, and then it'll be sitting in your work space. Now, you can theoretically store that object but it's probably not wise to do this. It's better to save the code that generates the plot and the data. So you can see this lattice behavior in just in this simple bit of code here. So here I'm calling the xyplot function. I'm saving to an object called p, and then I'm printing p. And that's when the plot appears. So when you run this code, after the first line of code, nothing will happen, but after the second line, the plot will appear. So if you just call xyplot, then it, then we use what's called the auto-printing feature of R.
