Basic Statistical Graphics in R. Stem and leaf plots 100,100,100,99,98,97,96,94,94,87,83,82,77,75,75,73,71,66,63,55,55,55,51,19

Transcription

1 Basic Statistical Graphics in R. Stem and leaf plots Example. Create a vector of data titled exam containing the following scores: 100,100,100,99,98,97,96,94,94,87,83,82,77,75,75,73,71,66,63,55,55,55,51,19 > exam = c(100,100,100,99,98,97,96,94,94,87,83,82,77,75,75,73,71,66,63,55,55,55,51,19) > stem(exam) 1 2: represents 12 leaf unit: 1 n:

2 Histograms To create a histogram of the exam scores, not surprisingly the hist command would be used. You could create a very basic histogram would by typing hist(exam), at the command line, but often you d like to add some stuff to the plot. Try typing > hist(exam, main = "Exam Results", col="purple") This yields the following output. Col is the color of the plot. R recognizes many colors by name, but if you are hard core and you know the exact hex code of the color you want, you can input that as well. For example, if indigo is your preferred shade of purple, try > hist(exam, main = "Exam Results", col="#4b0082") at the command line for a lovely indigo plot. If you right click on the plot and select copy as bitmap, the plot can then be pasted into other applications, like a Word file. If you type main = "some title" inside the hist() command, a title with the quoted text will appear.

3 There are some optional commands which can also be used. freq is either TRUE or FALSE and determines the units on the y axis. If true, the y axis will show frequency of the data, and if false it will show relative frequencies. breaks determines how many categories or bins of data you want to use. breaks = Sturges is the default which refers to using the so called Sturges rule to determine the optimal number of bins. breaks=5 for instance, would force the data into five bins. xlab will put a label of choice on the x axis of the plot. For example, > hist(exam, breaks = 5, freq=false, main = "Exam Results", col="red", xlab = "Exam score") produces the plot below. Note the label on the x axis, the 5 bars are the 5 bins of data, and the y axis shows what percentage of the data is in each bin (relative frequencies as opposed to frequencies).

4 Boxplots The syntax is pretty simple. >boxplot(exam, ylab = "Exam Grade", main = "Exam Results") produces It shouldn t be a surprise that the ylab command puts the indicated text on the y axis.

5 Multiple plots Sometimes you d like to make a boxplot of lots of data all at once. For example, at there is some (fictional) data about the starting salaries of some graduates of a degree program at several different universities (Harvard, Yale, and Miskatonic State). First, get the data into R by entering Now, if you type > plot(salarydata$salary~salarydata$school, xlab = "School", ylab ="Salary", main = "Salary Survey") the output is The syntax salarydata$salary~salarydata$school indicates that we want to plot the salary variable on the y axis, and see what the salaries look like among the various levels in the variable school.

6 Scatterplots Scatterplots are useful when trying to find patterns between two variables. Let s create some vectors of data and a data frame. The simplest scatter plot would be produced by either of the commands > plot(class$exam2 ~ class$exam1, main = "Exam 2 vs. Exam 1", xlab = "exam 1 grade", ylab = "exam2 grade") > plot(class$exam2, class$exam1, main = "Exam 2 vs. Exam 1", xlab = "exam 1 grade", ylab = "exam2 grade") which yields the output

7 If you want to do more fancy things with the plot, you have to work a little harder. To access some of the more sophisticated plotting tools in R, you will first need to load a package of additional functions. Packages contain useful functions developed by R users. Here s how to do this. First, select the Packages tab, and select Install package(s).

8 You ll be prompted to select a mirror location from which to download some additional files. Of course, you must be connected to the internet here.

9 The car package contains some advanced plotting tools, so select it from the menu that pops up, and select OK. You ll see a progress window while the files are downloaded. Finally, at the command line, type >library(car) and you now have some fancy plotting tools to use. The simplest scatterplot would be > scatterplot(exam2~exam1) The first argument is the variable that is the dependent/response (Y) variable, and the second is the independent/predictor (X) variable.

10 However, there are several arguments you can add to customize the plot. The code > scatterplot(exam2~exam1, reg.line=lm, xlab="first exam", ylab="second exam", data=class, smooth=false) produces the plot below. As before, the col parameter can be added to change the color of the plot. Setting reg.line=lm adds the best fit regression line to the plot (more on that in the next section) xlab and ylab are the labels that will be displayed on the x and y axes respectively data is the data frame that will be used smooth = FALSE indicates we re not interested in seeing a smooth curve that tries to fit the data.

11 And, you can use combinations of the other commands to make the plot look however you like. For instance, > scatterplot(exam2~exam1, reg.line=lm, xlab="first exam", ylab="second exam", data=class, main="exam 2 versus Exam 1", col = "red", smooth=false) yields the plot shown below.