ggplot2

ggplot2 includes features that:

let you create many different types of plots
customize the visual features of a plot
add labels and annotations to a plot.

Main conception

Aesthetic:

An aesthetic is a visual property of an object in your plot.

Think of an aesthetic as a connection or mapping between a visual feature in your plot and a variable in your data.
Geom:

A geom refers to the geometric object used to represent your data.
Facet:

Facets let you display smaller groups or subsets of your data.
Label and Annotate functions:

the label and annotate functions let you customize your plot.

Example

ggplot(data = penguins) + geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

ggplot():

It creates a coordinate system that you can add layers.

The first argument of the ggplot() function is the dataset to use in the plot. In this case.
+: It adds a new layer to your plot.
geom_point():

The geom_point() function uses points to create scatterplots.

The geom_bar() function uses bars to create bar charts, and so on.
(mapping = aes(x = flipper_length_mm, y = body_mass_g):

Each geom function in ggplot2 takes a mapping argument. This defines how variables in your dataset are mapped to visual properties.

Create a ggplot

Start with the ggplot function and choose a dataset to work with.
Add a geom_function to display your data.
Map the variables you want to plot in the argument of the aes function.

The template is as followed:

ggplot(data = <data>) + <geom_function>(mapping = aes(<aesthetic mapping>))

Aesthetic attributes

Mapping is an argument that matches up a specific variable in your data set with a specific aesthetic.

color: This allows you to change the color of all of the points on your plot, or the color of each data group.
size: This allows you to change the size of the points on your plot by data group.
shape: This allows you to change the shape of the points on your plot by data group.
alpha: It makes some points on a plot more transparent, or see-through, than others.

ggplot(data, aes(x=distance, y=dep_delay, color=carrier, size=air_time, shape=carrier))

If you want to change all the color / shape / size of the points, set the parameter outside the aes() function.

ggplot(data, aes(x=distance, y=dep_delay, size=air_time, shape=carrier), color=carrier)

Smoothing

Smoothing enables the detection of a data trend even when you can't easily notice a trend from the plotted data points.

| Type | Description | Example | | --------------- | -------------------------------------------- | ---------------------------------------------------------------------------------------- | | Loess smoothing | for the plots with less than 1000 points | ggplot(data, aes(x=, y=))+ geom_point() + geom_smooth(method="loess") | | Gam smoothing | for plots with a large number of points. | ggplot(data, aes(x=, y=))+ geom_point() + geom_smooth(method="gam", formula = y ~s(x) |

The geom_jitter() function creates a scatterplot and then adds a small amount of random noise to each point in the plot to make the points easier to find.

Facet

Facets let you display smaller groups or subsets of your data.

facet_wrap(): To facet your plot by a single variable
```
facet_wrap(~speices)
```
facet_grid(): To facet your plot with two variables
```
facet_grid(sex~speices)
```

Annotate

Annotate means to add notes to a document or diagram to explain or comment upon it.

Title, subtitle and caption:

add labs(title=<title>, subtitle=<subtitle>, caption=<caption>) as a new layer.

Annotation: put the note wherever you want

annotation("text", x=100, y=200, label="my note")

Save the plot

ggsave()
You can open an R graphics device like png() or pdf(); these will allow you to save your plot as a .png or .pdf file. You can also choose to print the plot and then close the device using dev.off().