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“R Graphics with ggplot2”

By “Mohcine Madkour”, Sat 08 April 2017, in category Misc

ggplot

About ggplot2

Developed by Hadley Wickham in 2005.

Implements the graphics scheme described in the book The Grammar of Graphics by Leland Wilkinson.

Does not create interactive or 3D graphics.

~~As of February 2014, officially in maintenance mode, meaning no new features will be added.~~ ggplot2 2.0 released in December 2015 with over 100 fixes and improvements.

The Grammar of Graphics

The Grammar of Graphics boiled down to 5 bullets, courtesy of Wickham (2016, p. 4):

a statistical graphic is a mapping from data to aesthetic attributes (location, color, shape, size) of geometric objects (points, lines, bars).

the geometric objects are drawn in a specific coordinate system.

scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).

the plot may also contain statistical transformations of the data (means, medians, bins of data, trend lines).

faceting can be used to generate the same plot for different subsets of the data.

The Grammar of Graphics - illustration

www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf

Basic ggplot2 syntax

Specify data, aesthetics and geometric shapes

ggplot(data, aes(x=, y=, color=, shape=, size=)) +

geom_point(), or geom_histogram(), or geom_boxplot(), etc.

This combination is very effective for exploratory graphs.

The data must be a data frame.

The aes() function maps columns of the data frame to aesthetic properties of geometric shapes to be plotted.

ggplot() defines the plot; the geoms show the data; layers are added with +

Some examples should make this clear

The iris data set

This is a famous data set from 1936, courtesy of Sir Ronald Fisher, that comes with R and is excellent for demonstrating ggplot2.

str(iris)

An iris

sebastianraschka.com/Images_old/2014_python_lda/

ggplot2 example - scatter plot coded by species

```{r, message=FALSE, fig.height=3, fig.width=6} library(ggplot2) # once per session ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, color=Species)) + geom_point()

## ggplot2 example - scatter plot coded by species

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width,
                 color=Species, shape=Species)) +
  geom_point()

ggplot2 example - scatter plot coded by species, size by Petal.Length

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, color = Species, size = Petal.Length)) + geom_point()

## ggplot2 example - add multiple geoms (points and smooth line)

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width,
                 color=Species)) +
  geom_point() + geom_smooth(method="lm")

ggplot2 example - boxplot (statistical transformation)

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x = Species, y = Sepal.Width)) + geom_boxplot()

## Moving beyond `ggplot` + `geoms`

- A natural next step in exploratory graphing is to create plots of subsets of data. These are called facets in ggplot2.

- Use `facet_wrap()` if you want to facet by one variable and have `ggplot2` control the layout. Example:

  - `+ facet_wrap( ~ var)`

* Use `facet_grid()` if you want to facet by one and/or two variables and control layout yourself.

Examples:

- `facet_grid(. ~ var1)` - facets in columns
- `facet_grid(var1 ~ .)` - facets in rows
- `facet_grid(var1 ~ var2)` - facets in rows and columns

## ggplot2 example - `facet_wrap` (common scales)

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width)) +
  geom_point() + facet_wrap(~ Species)

ggplot2 example - `facet_wrap` (free x scales)

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x = Petal.Width, y = Sepal.Width)) + geom_point() + facet_wrap(~ Species, scales = “free_x”)

## ggplot2 example - `facet_grid` (histograms)

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width)) +
  geom_histogram() + facet_grid(Species ~ .)

Customizing scales

Scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).

Every aesthetic has a default scale. To modify a scale, use a scale function.

All scale functions have a common naming scheme: scale _ name of aesthetic _ name of scale

Examples: scale_y_continuous, scale_color_discrete, scale_fill_manual

Heads up: The documentation for ggplot2 scale functions will frequently use functions from the scales package (also by Wickham)!

ggplot2 example - update scale for y-axis

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, color=Species)) + geom_point() + scale_y_continuous(limits=c(0,5), breaks=seq(0,5,0.5))

## ggplot2 example - update scale for color

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width,
                 color=Species)) + geom_point() +
  scale_color_manual(name="Iris Species",
                     values=c("red","blue","black"))

stat functions

All geoms perform a default statistical transformation.

For example, geom_histogram() bins the data before plotting. geom_smooth() fits a line through the data according to a specified method.

In some cases the transformation is the “identity”, which just means plot the raw data. For example, geom_point()

These transformations are done by stat functions. The naming scheme is stat_ followed by the name of the transformation. For example, stat_bin, stat_smooth, stat_boxplot

Every geom has a default stat, every stat has a default geom.

ggplot2 example - geom using default stat

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) + geom_point() + geom_smooth()

## ggplot2 example - stat using default geom

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) +
  geom_point() + stat_smooth()

Why should I care about `stat` versus `geom`?

The stat and geom functions can use each other’s arguments.

When consulting the documentation for a particular geom you’ll notice there is also documentation for an associated stat, and vice versa. (an exception is geom_point and stat_identity.)

Understanding how geoms and statistical transformations work together in ggplot2 can help you master the syntax faster.

Update themes and labels

The default ggplot2 theme is excellent. It follows the advice of several landmark papers regarding statistics and visual perception. (Wickham 2016, p. 176)

However you can change the theme using ggplot2’s themeing system. To date, there are seven built-in themes: theme_gray (default), theme_bw, theme_linedraw, theme_light, theme_dark, theme_minimal, theme_classic

You can also update axis labels and titles using the labs function.

ggplot2 example - update labels

```{r, message=FALSE, fig.height=3, fig.width=6} ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, color=Species)) + geom_point() + labs(title=”Sepal vs. Petal”, x=”Petal Width (cm)”, y=”Sepal Width (cm)”)

## ggplot2 example - change theme

```{r, message=FALSE, fig.height=3, fig.width=6}
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width,
                 shape=Species)) + geom_point() +
  theme_bw()

ggplot2 - some tips

Can do a lot with ggplot(data, aes()) + geom!
Data must be a data frame (not a matrix or collection of vectors)
The ggplot2 documentation has many good examples
Prepare to invest some time if you want master ggplot2; the RStudio ggplot2 cheat sheet can help.

Let’s go to R!

References

Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis (2nd ed), Springer.

Wickham, H. (2010), “A Layered Grammar of Graphics”, Journal of Computational and Graphical Statistics, Volume 19, Number 1.

Chang, W. (2013), R Graphics Cookbook, O’Reilly.

ggplot2 cheat sheet

https://www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf

Cookbook for R - Graphs

http://www.cookbook-r.com/Graphs/

Official ggplot2 web site

http://ggplot2.org/

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