In-class worksheet, class 16

We will be using the R package ggplot2 for all plots. To use it, we first need to load it:

library(ggplot2)

The default theme of ggplot2 is not the most beautiful. This code switches to a more pleasant theme:

theme_set(theme_bw())

1. Plotting the iris data set.

For this exercise we are using the iris data set available in R. This data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica:

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Make a scatter plot of petal length vs. sepal length for the three species. Make a single plot that shows the data for all three species at once, in different colors. Then do the same plot but facet by species instead of coloring.

ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, color=Species)) + geom_point()

ggplot(iris, aes(x=Sepal.Length, y=Petal.Length)) + geom_point() + facet_wrap(~Species)

Now see if you can make side-by-side boxplots of sepal lengths for the three species of iris. The geom you need to use is geom_boxplot(). See if you can guess the correct aesthetic mapping.

ggplot(iris, aes(y=Sepal.Length, x=Species)) + geom_boxplot() 

2. Plotting tree-growth data.

The data set Sitka from the MASS package contains repeated measurements of tree size for 79 Sitka spruce trees, which were grown either in ozone-enriched chambers or under control conditions.

library(MASS) # we need to load the MASS library to have access to this dataset
head(Sitka)

##   size Time tree treat
## 1 4.51  152    1 ozone
## 2 4.98  174    1 ozone
## 3 5.41  201    1 ozone
## 4 5.90  227    1 ozone
## 5 6.15  258    1 ozone
## 6 4.24  152    2 ozone

Make line plots of tree size vs. time, for each tree, faceted by treatment. First, use the same color for all lines.

ggplot(Sitka, aes(x=Time, y=size, group=tree)) + geom_line() + facet_wrap(~treat)

Then, color by tree.

ggplot(Sitka, aes(x=Time, y=size, color=tree, group=tree)) + geom_line() + facet_wrap(~treat)

Finally, color by size.

ggplot(Sitka, aes(x=Time, y=size, color=size, group=tree)) + geom_line() + facet_wrap(~treat)

In this last example, the lines actually change color from left to right. It would be nicer to have a single, uniform color for each tree, and, e.g., color by maximum size. To do this efficiently we need the dplyr package, which we will discuss later. If you have experience with dplyr, see if you can make a plot where the lines for each tree are of a uniform color determined by maximum size.

3. If this was easy

Show the 2d distribution of petal length vs. sepal length in the iris dataset, by making an x-y plot that shows the individual data points as well as contour lines indicating the density of points in a given spatial region.

ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, color=Species)) + geom_point() + geom_density2d()

If this was still easy, now instead of contour lines add a fitted straight black line (not a curve, and no confidence band!) to each group of points.

ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, color=Species)) + geom_point() + geom_smooth(aes(group=Species), method=lm, color='black', se=F)                                                                                    

In this last example, because we are manually overriding the color of the lines, we need to set the group aesthetic to tell ggplot2 to draw a separate line for each species.