CMPSC 390 Visual Computing Spring 2014 Bob Roos Notes on R Graphs, Part 2

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1 Notes on R Graphs, Part 2 1 CMPSC 390 Visual Computing Spring 2014 Bob Roos Notes on R Graphs, Part 2 Bar Graphs in R So far we have looked at basic (x, y) plots consisting of points or lines representing mathematical relations or functions. But what about data that contain a mix of numeric and non-numeric data? For instance, the data set VADeaths that comes with R consists of numbers representing the average percentage of deaths in Virginia per 1000 people for the year 1940, classified into several age and population categories: > VADeaths Rural Male Rural Female Urban Male Urban Female While it could possibly be argued that the leftmost column is numeric, it is really just a column of labels indicating an age group. A better way to view this data might be as a bar chart (see Figure 1). R s barplot function automatically plots each column as a stack of smaller columns, one for each of the row categories. We can add a legend to clarify the meanings. > barplot(vadeaths,xlab= "Population Group",ylab= "1940 Death Rate per 1000", col=c("red","green", "magenta","cyan","blue"), legend=labels(vadeaths)[[1]]) Figure 1: A stacked bar graph

2 2 Notes on R Graphs, Part 2 Figure 1 differs a bit from earlier graphing examples. First of all, we have specified just one data parameter, the name of the table itself, rather than a pair of x and y vectors. By default, barplot treats the column labels as the x-axis labels and treats the row labels as the stacked bar plot elements. Another difference is that we have placed the legend into the barplot command (we could have issued a separate legend function call instead). The advantage to doing it as a parameter in barplot is that the colors are already known. Finally, we have made use of a new function called labels. This identifies the row and column labels of a table. The row labels are the elements of labels(vadeaths)[[1]], the column labels are the elemtns of labels(vadeaths)[[2]]. For our legend, we want the row labels. It is also possible to obtain side-by-side bars in the graph by adding the parameter beside=true as shown in Figure 2. > barplot(vadeaths,xlab= "Population Group",ylab= "1940 Death Rate per 1000", beside=true) Figure 2: A side-by-side bar graph Pie Charts in R Another common way of illustrating categorical data is by means of a pie chart. For instance, the data set WorldPhones contains information about the number of telephones (in thousands) in various parts of the world for several years between 1951 and 1961: > data(worldphones) > WorldPhones N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer Handout 27 Handed out on 4 April 2014

3 Notes on R Graphs, Part 2 3 Pie charts give us a way to envision any single column of data from this table. The notation is straightforward, but there is one small glitch in the example I selected. To visualize the data for Europe, we might think of using a program such as this one: data(worldphones) pie(worldphones$europe) This doesn t work it produces the error $ operator is invalid for atomic vectors. Why didn t the $ operator work? It always has before! The answer is that we did not read in the WorldPhones table using the read.table command; instead, we loaded it from R s library of data sets. It so happens that this particular data set is not saved in the form of an R data frame, so the $ designator for columns doesn t work properly. It is easy to fix this, however all we need to do is convert the data into a data frame using the as.data.frame conversion function. Figure 3 shows this, plus the resulting graph. We must provide labels for the different sectors; as before, we use the labels function. > data(worldphones) > wp = as.data.frame(worldphones) > pie(wp$europe,labels=labels(wp)[[1]], main="phone Usage Growth in Europe") Figure 3: A pie chart It is worth mentioning that pie charts are generally considered poor conveyors of information. The following quotation is from the R documentation itself! (just type help(pie) on the R command line and see for yourself!): Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. A bar chart or dot chart is a preferable way of displaying this type of data. Box-and-Whisker Plots Sometimes we want a fast way to get an idea of not just the range of values in some set of data, but also how those data are distributed. One way to do this is through a box-and-whisker plot, also called just a box plot. We illustrate this with a few examples.

4 4 Notes on R Graphs, Part 2 Consider the sorted list of values 1, 2, 10, 11, 11, 12, 12, 14. The median value in this list is 11: half the values in the list are 11 and half the values in the list are 11. (Don t confuse the median with another value called the mean, or average that s just the sum of the values divided by the number of values. In this case, the mean is ( )/8 = ) The first quartile is the median of the lower half of the data, the third quartile is the median of the upper half of the data. In our example, the first quartile is a point somewhere between 2 and 10 and the third quartile is 12. The distance between the first and third quartile (in other words, the range of values of the middle half of the elements) is sometimes called the interquartile range. Now let s look at a box plot of this data. Figure 4 shows the original boxplot on the left and an annotated one on the right. (R chose the value 6 as the first quartile it is midway between 2 and 10.) Figure 4: Result of the command boxplot(c(1,2,10,11,11,12,12,14)) The whiskers represent the range of all the data lying within a distance of 1.5 times the interquartile range from the lower and upper quartiles. Since the interquartile range in this case is 12 6 = 6, all values lie inside the whiskers since they are within = 9 of the nearest quartile. Suppose the data were -7, 2, 10, 11, 11, 12, 12, 14. The quartiles, median, and interquartile range don t change, but -7 lies at a distance more than 9 away. Likewise, if the last value is changed from 14 to 22, it lies more than 9 away from the 3rd quartile 12. These appear as isolated points outside the whiskers in Figure 5. Finally, let s see the R command for boxplots for one of the data sets in R. The USJudgeRatings file contains ratings for a number of state judges in a number of criteria: > data(usjudgeratings) > USJudgeRatings CONT INTG DMNR DILG CFMG DECI PREP... etc.... AARONSON,L.H etc.... ALEXANDER,J.M etc.... Handout 27 Handed out on 4 April 2014

5 Notes on R Graphs, Part 2 5 Figure 5: Result of the command boxplot(c(-7,2,10,11,11,12,12,22)) ARMENTANO,A.J etc.... BERDON,R.I etc etc.... where the criteria are things like number of contacts, integrity, demeanor, diligence, case flow management, etc. Since all the rankings are on the same scale, we can have side-by-side box plots. R is smart enough to figure out that it should attempt to plot only the columns with numeric data. Figure 6 shows the result. We use the las=2 parameter to turn the axis scales perpendicular to the axes for better readability. > data(usjudgeratings) > boxplot(usjudgeratings,las=2, xlab="attributes",ylab="rating", main="ratings of State Judges") Figure 6: Box plots for judge rating data