![]() What is particularly interesting is that this can become a pie chart simply by changing its coordinate system to polar. The x-axis comes out labeled as “factor(“”)” but we can over-write that with a title for the x-axis. ggplot(mydata100, aes(x = factor(""), fill = workshop) ) + geom_bar() ![]() The colors are a bit garish, but they are chosen so that colorblind people (10% of males) can still read them. There is only one type of geometric object on the plot, which I add with geom_bar. I then fill the single bar in using the fill argument. On the x-axis, there really is no variable, so I plugged in a call to the factor() function that creates an empty one on the fly. It is not a very popular plot, but it helps demonstrate how different the grammar of graphics perspective is. Let us start our use of the ggplot() function with a single stacked bar plot. For the remainder of this page, I will use only ggplot() because it is the more flexible function, and by focusing on it, I hope to make it easier to learn. In R for SAS and SPSS Users and R for Stata Users, I showed how to create almost all the graphs using both qplot() and ggplot(). Faceting by gender would cause the graph to repeat for the two genders. Facets – these are the groups in your data.Scales – these are legends that show things like circular symbols represent females while circles represent males.Statistics – these are the functions like linear regression you might need to draw a line.Geoms – these are the geometric objects.A variable may control where points appear, the color or shape of a point, the height of a bar and so on. Aesthetics – these are the roles that the variables play in each graph.To understand ggplot, you need to ask yourself, what are the fundamental parts of every data graph? They are: While qplot() is easy to use for simple graphs, it does not use the powerful grammar of graphics. A plot like that of two factors simply shows the combinations of the factors that exist which is certainly not worth doing a graph to discover. Most of them are useful except for middle one in the left column of qplot(workshop, gender). The command that created each plot is shown in the title of each graph. Below is an example of the default plots that qplot() makes. It is particularly easy to use for simple plots. The quickplot() function – also known as qplot() – mimics R’s traditional plot() function in many ways. To make it easy to get started, the ggplot2 package offers two main functions: quickplot() and ggplot(). The programs and the data they use are also available for download here. Here I will review the basic examples presented in my books. ![]() Wickham’s book, ggplot2: Elegant Graphics for Data Analysis, provides a detailed presentation of the ggplot2 package. It is simplified only in that he uses R for data transformation and restructuring, rather than implementing that in his syntax. The ggplot2 package is a simplified implementation of the grammar of graphics written by Hadley Wickham for R. It is not a language you can use to recreate his graphs! ![]() However, it is not a light read, and it presents an abstract graphical syntax that is meant to clarify his concepts. Wilkinson’s book is perhaps the most important one on graphics ever written. From this perspective, a pie chart is just a bar chart with a circular (polar) coordinate system replacing the rectangular Cartesian coordinate system. In his book, The Grammar of Graphics, Wilkinson showed how you could describe plots, not as discrete types like bar plots or pie charts, but using a “grammar” that would work not only for plots we commonly use but for almost any conceivable graphic. The “gg” in ggplot2 stands for the Grammar of Graphics, a comprehensive theory of graphics by Leland Wilkinson, which he described in his book by the same name. The ggplot2 package is extremely flexible, and repeating plots for groups is quite easy. Viewing the same plot for different groups in your data is particularly difficult. While R’s traditional graphics offers a nice set of plots, some of them require a lot of work. The User Guide for that free software is here. Graphs are quick to create that way, and it will write the ggplot2 code for you. An easy way to study how ggplot2 works is to use the point-and-click user interface to R called BlueSky Statistics. Below are examples of graphs made using the powerful ggplot2 package. ![]()
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