Histograms are plots used to explore how one or more quantitative variables are distributed. To show some examples of histograms, we will use the iris data. This dataset contains measurements in centimetres of the length and width variables of the sepal and petal, and these measurements are available for 50 flowers from each of three species of iris: Iris setosa, versicolor, and virginica. You can get more details upon running ?iris.

The geometric attribute used to produce histograms is defined simply by specifying geom="histogram" in the qplot function. This default histogram will represent the variable specified in the function on the x axis, while the y axis will represent the number of elements in each bin. One other very useful way of representing distributions is to look at the kernel density function, which represents an approximation of the distribution of the data as a continuous function instead of different bins, by estimating the probability...

Bar charts are usually used to explore how one (or more) categorical variables are distributed. In qplot(), this is done using the geom option bar. This geometry counts the number of occurrences of each factor variable, which appears in the data. To show an example of the bar chart, we will use the movies dataset, which is included within the ggplot2 package. We have already seen how to recall the dataset included with the basic installation of R, but if you are interested in the list of datasets within a specific package (ggplot2 in this case), you can use the following code:

require(ggplot2)       ## Load ggplot2 if needed
data(package="ggplot2")  ## List of dataset within ggplot2

The movies dataset contains information about movies, including the rating, from the http://imdb.com/ website. You can get a more detailed description in the help page of the dataset.

This dataset contains different variables but, for our example, we will not need all of them, so let´...

Box plots, also known as box-and-whisker plots, are a type of plot used to depict a distribution by representing its quartile values. In such plots, the upper and lower sides of the box represent the twenty-fifth and seventy-fifth percentiles (also called the first and third quartiles), while the horizontal line within the box represents the median of the data. The difference between the first and third quartiles is defined as Inter-Quartile Range (IQR), and it is often used as a measure of statistical dispersion of a distribution. The upper whisker represents the higher values up to 1.5*IQR of the upper quartile, while the lower whisker represents lower values within 1.5*IQR of the lower quartile. The pieces of data not in the whisker range are plotted as points and are defined as outliers. You can get additional details and references in the package website shown at the end of the chapter.

In this section, we will see some examples of boxplots using the dataset created in the...

Scatterplots are probably among the most common plots, since they are frequently used to display the relationship between two quantitative variables. When two variables are provided, ggplot2 will make a scatterplot by default. Now that you have already acquired some experience from the previous sections of this chapter, the representation of the scatter plot will be quite straightforward for you.

For our example on how to build a scatterplot, we will use a dataset called ToothGrowth, which is available in the base R installation. Reported in this dataset are measurements of the length of the teeth of 10 guinea pigs for three different doses of vitamin C (0.5, 1, and 2 mg). It is delivered in two different ways—as orange juice or as ascorbic acid (a compound with vitamin C activity). You can find details on the dataset help page at ?ToothGrowth.

We are interested in seeing how the length of the teeth changed for each different dose. We are not able to distinguish the different...