# Chapter 13 Parallel Boxplot

Parallel boxplots are very useful for comparing groups of variables. They give a very quick visual impression of what is happening.

## 13.1 With a Grouping Variable (or Factor)

Let us look at the dataset built into R called chickwts. This dataset shows the chick weight, in grams, 6 weeks after newly hatched chicks were randomly placed into six groups by feed type. The dataset has 2 variables, weight and feed. The variable, weight, is quantitative while the variable, feed, is categorical.

### In Basic R

If there is a grouping variable, we use the function,
boxplot(quantitative_variable ~ factor, …)

where factor is the grouping variable desired.

Let us draw a boxplot of the chick weights grouped by feed type.

### Using Ggplot2

In the aesthetic mappings of the ggplot function, be sure to include the data, the x and the y variables to be used for plotting. The geometric shaped used here is geom_boxplot( ).

## 13.2 Without a Grouping Variable (or Factor)

Let us look at the dataset called swiss. This is a dataset on the fertility and socio-economic measures for the French-speaking provinces of Switzerland. For our boxplot, we will be comparing the socio-economic indicators Agriculture (% of males involved in agriculture as an occupation), Examination (% of draftees receiving highest mark on the army examination) and Catholic (% of Catholics).

### In Basic R

If there is no grouping variable, we use the function,
boxplot(quantitative_variable_1, quantitative_variable_2, …)

As you can see from the boxplot, the reader will have a hard time determining what the numbers 1, 2, 3 mean. To put a more meaningful label, add the argument, names( ) and list the socio-economic indicators, in the order they appear on the boxplot.

Alternatively, if you know the variable’s column number, you can state the column number to draw the boxplot. The variables will be used as the boxplot labels.