Hi, everyone, this lesson is going to be about ggplot2 or the ggplot2 package. And we're going to, I'm going to talk about how to do some basic plots using the ggplot2 package and what it's about. And in this, in the next lecture I'll talk a little bit in more detail about how it's designed and how you can make extensions to various ggplot2 plotting functions. So the first question very basic. You know, what is ggplot2? Basically it's a package in R that you can download from CRAN. And and it implements what's called the grammar of graphics, which is originally written by Leland Wilkinson and it is described in a, in a book called the Grammar of Graphics. Now the Grammar of Graphics is a description of how kind of graphics can be broken down into abstract concepts. You can, so think of the grammar of a language like English. You have things like verbs and nouns and adjectives. And so the question is, you know, what are the verbs, nouns, and adjectives of a data graphic? And the Grammar of Graphics kind of describes kind of those basic elements so that you can put them together to make new types of graphics. Just like you could take a verb and a noun and an adjective and make a new sentence that maybe no one's ever heard before. You could take the grammar of graphics and put together various aspects of plots and make a graphic that no one's ever seen before and so that's the basic idea. It's a very powerful concept to kind of organize all kinds of data graphics. And until recently there was no specific implementation for it in R, but Hadley Wickham who when he was a graduate student at Iowa State implement the Grammar of Graphics as an R package called ggplot and its current implementation is called ggplot2. So one could think of this as almost a third graphic system in R. Even though it's based, is built upon the grid graphic system which is, which comes with R. It's kind of a third mode of, of plotting that has become very popular. So if you think of the first mode as like the base plots using functions like plot, and hist, and boxplot, and then the second mode as the lattice plots so using XY plot and these kinds of trellis type functions. And then the third mode is ggplot. So you get the package from CRAN. You can, you can use install.packages. It installs on all almost all sys...I imagine on all systems. You can go to the ggplot website which is ggplot2.org. And so the nice thing about ggplot is that, is that, is that it is based on this grammar of graphics, and so, it, in a sense, there's a theory of the graphics. So you can take this theory and kind of reassemble the different pieces to make new types of plots. And as Hadley Whitcomb says in his book, you know, the basic idea is that you want to shorten the distance from the mind to the page. So if you have some data that you're looking at, And you want, and you thin of a way that you want to visualize that data. You want to be able to rapidly take those ideas and turn them into a picture on your screen. So, from the GG plot two book, this sentence kind of summarizes the basic idea. But the idea is that, the grammar tells us the statistical graphic is mapping from data to aesthetic attributes. So color, shape and size. - of geometric objects, so, points, lines, and bars, and the plot may also contain statistical transformations of the data and is drawn on a specific coordinate system. So, we have things are that, we have a mapping from data to aesthetics, geometric objects, we have statistics. Now we have a coordinate system. So in this lecture I just want to talk about the qplot function which is kind of the most basic function and it's probably the best place to start for someone who is transitioning from say the base plotting system. So in the base plotting system you know the work horse function is the plot function and so qplot which you can think of as standing for quick plot Is kind the work horse function for for GD plot and its analogous to the plot function and the base system. So one key difference that you have to get used to when you're using GD plot is that typically when you make a plot and you pass data to the q plot function you want to tell it where the data comes from and the data will always come from a data frame. So a data frame is going to be. So, your data have to be organized in a data frame. And then when you plot variables those variables are going to come from the data frame. Now, you don't have to specify a data frame. You can if you don't specify a data frame the the cue plot function or all the plotting functions will, will look for the data in your workspace. But it's generally a good idea to specify the data frame. That way when you read the code that generated the plot You know exactly where the data came from. So then so the data frame is very important to organize before you start plotting. Once you start plotting the plots are made up of aesthetics and geoms and so aesthetics are things like the size, shape, and color of things. Points and the geoms are sort of the objects that you're pointing, plotting I'm sorry. So are you plotting points Are you plotting lines, are you plotting bars, you know, whatnot. One aspect that's important for the qplot function, and also is similarly important when you're using lattice functions, is the idea of using factor variables. So factors are very important because they indicate subsets of your data. So if you imagine you have a data frame or you have a y variable and a x variable and then a factor variable the factor will indicate subsets of your data in the data frame. So for example you might have factor that indicates the gender. So you have a bunch of males and a bunch of females. So those are subsets of your data and you might want to plot a certain relationship divided by the various subsets or you might want to color certain points, depending on whether they're male of female. And so the categories that are indicated by various factor variables can be useful for annotating a plot. And so, one aspect, so one thing that's important about this feature is that, is that when you have factor variables in a data set, you want to make sure that they're properly labeled. So it's usually not useful to label a factor variable as one, two, and three, even if you have three categories. One, two, and three is not particularly informative. Usually you want to label them with the more informative labels so that you know what those factor variables are trying to encode. Now the qplot function is a fairly straight forward function to use. I think it's very easy to pick up. It hides a lot of the details of, of what ggplot is doing underneath which is fine for many cases. But the ggplot function is really kind of the core function of the system. It's very flexible and you can use it in combination with a lot of things that g, that qplot can't do.
