# ggplot2: Box Plots

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### Introduction

This is the 9th post in the series **Elegant Data Visualization with
ggplot2**. In the previous post, we learnt how to build bar charts. In this

post, we will learn to:

- build box plots
- modify box
- color
- fill
- alpha
- line size
- line type

- modify outlier
- color
- shape
- size
- alpha

The box plot is a standardized way of displaying the distribution of data. It

is useful for detecting outliers and for comparing distributions and shows the

shape, central tendancy and variability of the data.

## Structure

- the body of the boxplot consists of a “box” (hence, the name), which goes from the first quartile (Q1) to the third quartile (Q3)
- within the box, a vertical line is drawn at the Q2, the median of the data set
- two horizontal lines, called whiskers, extend from the front and back of the box
- the front whisker goes from Q1 to the smallest non-outlier in the data set, and the back whisker goes from Q3 to the largest non-outlier
- if the data set includes one or more outliers, they are plotted separately as points on the chart

## Data

We are going to use two different data sets in this post. Both the data sets have the same data but are in

different formats.

daily_returns <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/tickers.csv') daily_returns ## # A tibble: 250 x 5 ## AAPL AMZN FB GOOG MSFT #### 1 1.38 24.2 2.12 22.4 1.12 ## 2 2.83 3.25 -0.860 5.99 0.767 ## 3 -0.0394 9.91 1.45 6.75 0.973 ## 4 0.108 3.76 -0.770 -10.7 -0.285 ## 5 1.64 19.8 4.75 8.66 0.501 ## 6 0.0689 5.33 -0.300 -0.930 0.256 ## 7 -0.561 -5.21 -0.630 -7.28 -0.708 ## 8 0.551 0.25 -0.460 0.690 0.128 ## 9 -0.217 -13.6 0.0300 6.56 0.0786 ## 10 -0.108 -4.25 0.460 2.60 0.472 ## # ... with 240 more rows

## Univariate Box Plot

If you are not comparing the distribution of continuous data, you can create

box plot for a single variable. Unlike `plot()`

, where we could just use

1 input, in ggplot2, we must specify a value for the X axis and it must be

categorical data. Since we are not comparing distributions, we will use `1`

as the value for the X axis and wrap it inside `factor()`

to treat it as a

categorical variable. In the below example, we examine the distribution of

stock returns of Apple.

ggplot(daily_returns) + geom_boxplot(aes(x = factor(1), y = AAPL))

## Data

For the rest of the post, we will use the below data set. Instead of 5 columns,

we have two columns. One for the stock names and another for returns.

tidy_returns <- read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/tidy_tickers.csv', col_types = list(col_factor(levels = c('AAPL', 'AMZN', 'FB', 'GOOG', 'MSFT')), col_double())) tidy_returns ## # A tibble: 1,254 x 2 ## stock returns #### 1 AAPL 1.38 ## 2 AAPL 2.83 ## 3 AAPL -0.0394 ## 4 AAPL 0.108 ## 5 AAPL 1.64 ## 6 AAPL 0.0689 ## 7 AAPL -0.561 ## 8 AAPL 0.551 ## 9 AAPL -0.217 ## 10 AAPL -0.108 ## # ... with 1,244 more rows

## Box Plot

With the above data, let us create a box plot where we compate the distribution

of stock returns of different companies. We map X axis to the column with stock

names and Y axis to the column with stock returns. Note that, the column names

are wrapped inside `aes()`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns))

To create a horizontal bar plot, we can use `coord_flip()`

which will flip the

coordinate axes.

## Horizontal Box Plot

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns)) + coord_flip()

## Notch

Notches are used to compare medians. You can use the `notch`

argument and set

it to `TRUE`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), notch = TRUE)

## Jitter

Just for comparison, let us plot the returns as points on top of the box plot

using `geom_jitter()`

. We modify the color of the points using the `color`

argument and the spread using the `width`

argument.

ggplot(tidy_returns, aes(x = stock, y = returns)) + geom_boxplot() + geom_jitter(width = 0.2, color = 'blue')

## Outliers

To highlight extreme observations, we can modify the appearance of outliers

using the following:

- color
- shape
- size
- alpha

To modify the color of the outliers, use the `outlier.color`

argument. The

color can be specified either using its name or the associated hex code.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), outlier.color = 'red')

The shape of the outlier can be modified using the `outlier.shape`

argument.

It can take values between `0`

and `25`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), outlier.shape = 23)

The size of the outlier can be modified using the `outlier.size`

argument. It

can take any value greater than `0`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), outlier.size = 3)

You can play around with the transparency of the outlier using the

`outlier.alpha`

argument. It can take values between `0`

and `1`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), outlier.color = 'blue', outlier.alpha = 0.3)

## Box Aesthetics

The appearance of the box can be controlled using the following:

- color
- fill
- alpha
- line type
- line width

## Specify Values

The background color of the box can be modified using the `fill`

argument. The

color can be specified either using its name or the associated hex code.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), fill = c('blue', 'red', 'green', 'yellow', 'brown'))

To modify the transparency of the background color, use the `alpha`

argument. It

can take any value between `0`

and `1`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), fill = 'blue', alpha = 0.3)

The color of the border can be modified using the `color`

argument. The

color can be specified either using its name or the associated hex code.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), color = c('blue', 'red', 'green', 'yellow', 'brown'))

The width of the border can be changed using the `size`

argument. It can take

any value greater than `0`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), size = 1.5)

To change the line type of the border, use the `linetype`

argument. It can take

any value between `0`

and `6`

.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns), linetype = 2)

## Map Variables

Instead of specifying values, we can map `fill`

and `color`

to variables as

well. In the below example, we map `fill`

to the variable stock. It assigns

different colors to the different stocks.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns, fill = stock))

Let us map `color`

to the variable stock. It will assign different colors

to the box borders.

ggplot(tidy_returns) + geom_boxplot(aes(x = stock, y = returns, color = stock))

### Summary

In this post, we learnt to:

- build box plots
- modify outlier color, shape, size etc.
- modify box color
- modify box line color, size and line type

### Up Next..

In the next post, we will learn to build histograms.

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