 By Rudolf Kingslake

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Extra info for Applied optics and optical engineering,Vol.II

Example text

One challenge with ggplot(mpg, aes(class, hwy)) + geom boxplot() is that the ordering of class is alphabetical, which is not terribly useful. How could you change the factor levels to be more informative? Rather than reordering the factor by hand, you can do it automatically based on the data: ggplot(mpg, aes(reorder(class, hwy), hwy)) + geom boxplot(). What does reorder() do? Read the documentation. 3. Explore the distribution of the carat variable in the diamonds dataset. What binwidth reveals the most interesting patterns?

1 Exercises 1. Experiment with the colour, shape and size aesthetics. What happens when you map them to continuous values? What about categorical values? What happens when you use more than one aesthetic in a plot? 2. What happens if you map a continuous variable to shape? Why? What happens if you map trans to shape? Why? 3. How is drive train related to fuel economy? How is drive train related to engine size and class? 5 Facetting Another technique for displaying additional categorical variables on a plot is facetting.

For geom boxplot() and geom violin(), you can control the outline colour or the internal fill colour. 3 Histograms and Frequency Polygons Histograms and frequency polygons show the distribution of a single numeric variable. They provide more information about the distribution of a single group than boxplots do, at the expense of needing more space. ggplot(mpg, aes(hwy)) + geom_histogram() #> stat_bin() using bins = 30 . Pick better value with #> binwidth . ggplot(mpg, aes(hwy)) + geom_freqpoly() #> stat_bin() using bins = 30 .