Statistical Analysis with R For Dummies
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Many times the dependent variable is a data point rather than a frequency. The table shows the data for commercial space revenues for the early 1990s. (The data, by the way, are from the U.S. Department of Commerce, via the Statistical Abstract of the U.S.)

U.S. Commercial Space Revenues 1990–1994 (In Millions of Dollars)

Industry 1990 1991 1992 1993 1994
Commercial Satellites Delivered 1,000 1,300 1,300 1,100 1,400
Satellite Services 800 1,200 1,500 1,850 2,330
Satellite Ground Equipment 860 1,300 1,400 1,600 1,970
Commercial Launches 570 380 450 465 580
Remote Sensing Data 155 190 210 250 300
The data are the numbers in the cells, which represent revenue in thousands of dollars. A base R bar plot of the data in this table appears in the figure.

Bar plot of the data.

If you had to make a presentation about these data, you'd agree that your audience would prefer the graph to the table. Although the table is informative, it doesn't hold people's attention. It's easier to see trends in the graph — Satellite Services rose fastest while Commercial Launches stayed fairly level, for example.

This graph is called a grouped bar plot. How do you create a plot like this one in base R?

The first thing to do is create a vector of the values in the cells:

rev.values <- c(1000,1300,1300,1100,1400,800,1200,1500,1850, 2330,860,1300,1400,1600,1970,570,380,450,465,580, 155,190,210,250,300)

Although commas appear in the values in the table (for values greater than a thousand), you can't have commas in the values in the vector! (For the obvious reason: Commas separate consecutive values in the vector.)

Next, you turn this vector into a matrix. You have to let R know how many rows (or columns) will be in the matrix, and that the values load into the matrix row-by-row:

space.rev <- matrix(rev.values,nrow=5,byrow = T)

Finally, you supply column names and row names to the matrix:

colnames(space.rev) <- c("1990","1991","1992","1993","1994") rownames(space.rev) <- c("Commercial Satellites Delivered","Satellite Services","Satellite Ground Equipment","Commercial Launches","Remote Sensing Data") Have a look at the matrix:

> space.rev ......... ....1990 1991 1992 1993 1994 Commercial Satellites Delivered 1000 1300 1300 1100 1400 Satellite Services 800 1200 1500 1850 2330

Satellite Ground Equipment 860 1300 1400 1600 1970 Commercial Launches 570 380 450 465 580 Remote Sensing Data 155 190 210 250 300

Perfect. It looks just like the table.

With the data in hand, you move on to the bar plot. You create a vector of colors for the bars:

color.names = c("black","grey25","grey50","grey75","white")

A word about those color names: You can join any number from 0 to 100 with "grey" and get a color: "grey0" is equivalent to "black" and "grey100" is equivalent to "white". (Far more than fifty shades, . . . )

And now for the plot:

barplot(space.rev, beside = T, xlab= "Year",ylab= "Revenue  (X $1,000)", col=color.names) beside = T means the bars will be, well, beside each other. (You ought to try this without that argument and see what happens.) The col = color.names argument supplies the colors you specified in the vector.

The resulting plot is shown here.

Initial bar plot of the data.

What's missing, of course, is the legend. You add that with the legend() function to produce the figure:

> legend(1,2300,rownames(space.rev), cex=0.7, fill = color.names, bty = "n") The first two values are the x- and y-coordinates for locating the legend. (That took a lot of tinkering!). The next argument shows what goes into the legend (the names of the industries). The cex argument specifies the size of the characters in the legend. The value, 0.7, indicates that you want the characters to be 70 percent of the size they would normally be. That's the only way to fit the legend on the graph. (Think of "cex" as "character expansion," although in this case it's "character contraction.") fill = color.names puts the color swatches in the legend, next to the row names. Setting bty (the "border type") to "n" ("none") is another little trick to fit the legend into the graph.

About This Article

This article is from the book:

About the book author:

Joseph Schmuller, PhD, has taught undergraduate and graduate statistics, and has 25 years of IT experience. The author of four editions of Statistical Analysis with Excel For Dummies and three editions of Teach Yourself UML in 24 Hours (SAMS), he has created online coursework for and is a former Editor in Chief of PC AI magazine. He is a Research Scholar at the University of North Florida.

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