This lecture is about the base plotting system in R. Now previously we talked about the three different plotting systems that R has. The base plotting system, the lattice system, and the G G plots, the two system. So here I'm going to talk about the base plotting functions and the, which is the kind of most commonly used system in, for plotting in R. All of the core plotting and graphics engine in R is encapsulated in the graphics package and the G R devices Package. The GR devices package contains all the code for the graphics devices, including the screen devices, like X11 and Windows ports, as well as file devices like PDF and Post Script. The graphics package contains the plotting functions, so, so very basic functions like plot, hist, boxplot and, and many others. And so this will be core plotting system, what we call the base graphic system. The lattice system is, is encapsulated in the lattice package. And that uses another package called grid, which is a kind of a low-level, graphics system that's kind of an alternative to the, to the base graphing system. We usually, we don't ever, we usually don't call the grid package directly, only indirectly through packages like lattice or GE plot. And the last package we'll talk about in a separate lecture. So in the process of making a plot there are a lot of considerations that you have to make. In terms of where will the plot be made? Will it be made on the screen? Will it be sent to a file? How will the plot be used? Is it for viewing on the screen temporarily? Is it going to be in a web browser? Are you going to be printing it in a paper? Is it going to be publication quality, or you're going to use it in a slideshow or presentation? So, these are kind of questions that you need to think about when you're making a plot. Another thing, another question that you might want to think about is there, is there a large amount of data that's going to go into this plot? Or maybe just a few points? Or is it an image? Or is it a line drawing? Or is it something like that. Un, and then lastly one question that's worth ask, what, asking is whether you need to be able to dynamically resize the graphic, because you may want to use something like a vector format if you are sending it to a file rather than a bit map format. So those are a couple of questions that you want to make as you start making a plot. The other question of course is what graphic system you want to use? There's Base, lattice, and ggplot2. And the reason why you have to ask yourself this question is because you can't really mix the three different systems. And so the three systems, roughly speaking, are, you know, base graphics has this kind of piecemeal construction where you put things together one by one, through a series of function calls. Now, the lattice system creates entire plots out of a single function call, so everything kind of has to be conceptualized from the get go. And the ggplot2 system kind of combines concepts from both lattice and base. So in this lecture we'll be focusing only on the base plotting system, and, and we'll be talking about creating graphics on the screen device only. So in base graphics, you, you're essentially creating two dimensional graphics. and, and you have to do it in a two phase process. The first process initializes the new plot and the second par, phase I should say, annotates or adds to an existing plot. And so there are a number of functions that are useful for initializing a plot. And then there are ano-, separate set of functions that are useful for annotating a plot. So the plot function or the hist function are two examples of functions that are init-, used for initializing a plot. And what they do is that if they, if there's no graphics device open already, it will launch a graphics device. And typically, this will be, come in the form as a, of a window that pops up on your screen. And then it will draw the plot on that new device. The plot function is a generic function in R, and so, it has, it, it can behave differently, depending on what kinds of data you pass to it. If you don't pass it a special type, piece of data, so, if you just pass it, if X and Y are, for example, vectors, then, you use what's called the default method for plot. This default method has a lot of arguments. And you can look at the help page for plot and see what those arguments are. For example, you can set things like the title, the x-axis label and the y-axis label and many other types of details. In general, the Base graphics system has many parameters that you, can use to set that can set, and tweak to make your graphics look exactly the way you want them to. This is kind of a, it's a, it's a double-edged sword, because there are a lot of things that you can customize. But of course, then, there are a lot of things that you have to set that are not automatic. And so, most of these parameters are documented in the help page for par, so you just look at that help page, you'll see a lot of parameters there. It wouldn't help, hurt to try to memorize this help page, of course, but it's always there for you if you don't. So, here's a, the, a basic graph that, that is made using the base graphic system here. I'm using the hist function which creates a histogram. Which is a kind of a one-dimensional plot. And so here I'm loading the air quality data set from the data set's package and I'm making histogram of ozone. And so you can see that I'm not setting, I'm not using, there are other arguments to hist but I'm not setting all of them. And so I'm just using the defaults for, for whatever there is there for hist, and it makes a simple histogram. And so, this if my graphics device had not been open in R, it would have opened the graphics device and then put the histogram in there. Another fundamental type of plot is the scatter plot, of course. And this can be created using the plot function here. I'm using the same data set, but I'm making a scatter plot of ozone versus wind. And so you can see the default plotting is if I don't specify any other arguments to the plot function it's, I'm using all the defaults here. And the default plotting symbol is a, is an open circle with kind of a white background. And then finally, this is another function that initializes the plot. It's the box plot function. here, and you can see I'm making a box plot of the distribution of ozone by month. So you see the box, the box plot function uses a kind of formula notation, where I'm saying on the Y axis I want ozone, which is the left hand side of the tilde. And on the right hand side I specify what I want on the X axis, which in this case is the month. And so like, the, the month is to kind of divide it into a categorical vector. cat, sorry, car, categorical variable so if for the months of May, June, July, August and September. And here you see, I specified what I want the x lab, the x label to, x axis label to be through x lab and the y axis label through y lab. [BLANK_AUDIO] So there are a couple of key base graphic parameters that are useful to kind of keep in the, and, at the front of your brain, or at the top of your head, whatever you want to call it because they are, you know, you tend to use them quite often. The first one is the pch or the plotting character or plotting symbol. And you notice that the default in the, in the previous plots tends to be, is the open circle. But you can change this to be something else, and, and so the pch can be, take two types of arguments. It can take a number which refers to a table of kind of built-in symbols. Or, it can take a character. So we can take character element which would be plot that character on the screen. So if you give the letter A for example, then it will use as, the letter A the plotting symbol for all your data points. So sometimes this is useful for kind of self-labeling a plot, so you can see that all the As represent one group, and all the Bs represent another group. The lty is the line type, and so if you're plotting things that have, plotting data that are lines this will specify whether it's a solid line, a dashed line or a dotted line or a dot-dash line, or etc. The lwd is the line width. So some, a lot of times you want to control the line width depending on how, you know, how the plot's going to be used. For example, if the plot's going to be in a presentation where lots of people are going to be sitting in the back row, then you want to, usually you want to have very thick line width so they can see the lines. But if it's going to be in a publication where it's going to be in a mag, in a journal or printed and people are going to be looking at it much closer, then you don't need such thick lines. The col is the col-, the plotting colors. This is the color that the the plotting symbols will appear on the screen. Sometimes you like to cu-, customize that. You can specify colors using either a number or a string which, which is like a word or you can use a hexcode that's written in, in character format. And the colors function will give you a list of colors that you can specify by name. So you can see there are a lot of colors there. And then xlab and ylab will let you specify the x axis and y axis labels. And that's very useful for annotating plots. And a couple other base graphics parameters that can be useful that you can set in the par function. Because the par function is used to specify global graphics parameters. So these, these will be the same in every single plot that you make. Of course you could always, always, you can usually, I should say, override the global graphics parameters with, by specifying them in each function call. So if you don't like, for example the background color, you can usually, you can change that within a given function. However, other parameters like the margin size, or the mfrow or the mfcol, these cannot be spec, changed within the plotting function. They have to be changed using the par function only. And so the las, the las, is, is the orientation of the axis on the labels. So if you'd like all the labels on the the, the axis labels to be kind of horizontal or, or perpendicular to the graph, you can change this. BG is the background color for the plot. The margin size, sometimes it needs to be modified if you have a very complex x, y axis or x axis label, you might, might want to make the margin a little bit bigger to accommodate that. The outer margin is important if you have multiple plots per page and you want to have a mar-, a kind of a label that's in the outer margin that kind of covers the entire plot. And the mfrow and the mfcol control how many, if you having multiple plots on the dev, on the screen device. And for example you have a matrix of plots. And it controls how many plots will appear per row, and per column. So you can see what the default values for some of these global graphics parameters are, by just calling the par function with the name of the parameter and no other arguments. So you can see the lty, the default is a solid line. For color, the default is black and for pch the plotting symbol is 1, which corresponds to n open circle. For BG, the, the background is transparent, so there's really no background color. And then for the margin you can see that there's four numbers here and each one of these numbers refers to one of the margins of a plot. And if you think of the bottom of the plot as the first side and then the left hand side is the second side, and then the top is the third side and the right hand side is the fourth side. So you can think of, starting at the bottom going in a clockwise direction. Those are the four sides of a plot. And you can see the bottom margin gets a spa-, a, a size of 5.1. And you can roughly think of this as 5.1 lines of text that could be in the bottom margin. And then the s-,, the left hand side is 4.1, the top is 4.1 and the right hand side is 2.1. So you can adjust those margin sizes if you need to accommodate your plot to make more or less space in those margins. And then the mfrow defaults to 1,1, which means one row and one column. So that's just a single plot.
