Exercise

# The sunflowerplot() function for repeated numerical data

A scatterplot represents each (x, y) pair in a dataset by a single point. If some of these pairs are *repeated* (i.e. if the same combination of x and y values appears more than once and thus lie on top of each other), we can't see this in a scatterplot. Several approaches have been developed to deal with this problem, including *jittering*, which adds small random values to each x and y value, so repeated points will appear as clusters of nearby points.

A useful alternative that is equally effective in representing repeated data points is the *sunflowerplot*, which represents each repeated point by a "sunflower," with one "petal" for each repetition of a data point.

This exercise asks you to construct both a scatterplot and a sunflowerplot from the same dataset, one that contains repeated data points. Comparing these plots allows you to see how much information can be lost in a standard scatterplot when some data points appear many times.

Instructions

**100 XP**

- Use the
`par()`

function to set the`mfrow`

parameter for a side-by-side plot array. - For the left-hand plot, use the
`plot()`

function to construct a scatterplot of the`rad`

variable versus the`zn`

variable, both from the`Boston`

data frame in the`MASS`

package. - Use the
`title()`

function to add the title`"Standard scatterplot"`

to this plot. - For the right-hand plot, apply the
`sunflowerplot()`

function to the same data to see the presence of repeated data points, not evident from the scatterplot on the left. - Use the
`title()`

function to add the title`"Sunflower plot"`

.