ggplot2 uses the basic units of the “grammar of graphics” to construct data visualizations in a layered approach.
The basic units in the “grammar of graphics” consist of:
Visualizations in ggplot2 begin with a blank canvas, which is just an empty plot with data associated to it. Geoms are “added” as layers to the original canvas, adding representations of the data to the visualization.
In the visual above:
mtcars) and creates the aesthetic mapping of
The key is that the
aes() (aesthetic function) on line one maps the data onto each of the two geom layers
In ggplot2 geom aesthetics are data-driven instructions that determine the visual properties of an individual geom.
Geom aesthetics allow individual layers of a visualization to have their own aesthetic mappings. These aesthetic mappings can vary depending on the geom.
For example, the
geom_point() geom can color-code the data points on a scatterplot based on a property with the following code:
viz <- ggplot(data=airquality, aes(x=Ozone, y=Temp)) +geom_point(aes(color=Month)) +geom_smooth()
The code above would only change the color of the point layer, it would not affect the color of the smooth layer since the
aes() aesthetic mapping is passed at the point layer.
geom_bar() layer adds a bar chart to a ggplot2 canvas.
Typically when creating a bar chart, an
aes() aesthetic mapping with a single categorical value on the
x axes and the
aes() function will compute the count for each category and display the count values on the
To create a bar chart displaying the number of books in each
Language from a
books data frame :
bar <- ggplot(books, aes(x=Language)) + geom_bar()bar
In ggplot2 aesthetics are the instructions that determine the visual properties of a plot and its geometries.
Examples of ggplot2 aesthetics include:
Aesthetics are set either manually or by aesthetic mappings. Aesthetic mappings “map” variables from the bound data frame to visual properties in the plot. These mappings are provided in two ways using the
aes() mapping function:
For example, the following code assigns
aes() mappings for the
y scales at the canvas level:
viz <- ggplot(data=airquality, aes(x=Ozone, y=Temp)) +geom_point() +geom_smooth()
In the example above:
aes()aesthetic mapping function as an additional argument to
geom_smooth()use the scales defined inside the aesthetic mapping assigned at the canvas level.
You could create the same plot by setting the aesthetics at the geom level, as follows:
viz <- ggplot(data=airquality) +geom_point(aes(x=Ozone, y=Temp)) +geom_smooth(aes(x=Ozone, y=Temp))
In ggplot2, labels add meaning and clarity to a data visualization.
ggplot2 automatically assigns the name of the variable corresponding to components, like axes labels. Because data frame variable names are not always legible to outside readers, the
labs() function allows you to manually set labels.
To customize a plot’s labels, add a
labs() function call to the ggplot object. Inside the function call to
labs(), you can provide labels for the
y axes as well as a
caption. The list of available label arguments can be found in the
labs() function call and these specified arguments would render the following plot:
viz <- ggplot(df, aes(x=rent, y=size_sqft)) +geom_point() +labs(title="Monthly Rent vs Apartment Size in Brooklyn, NY", subtitle="Data by StreetEasy (2017)", x="Monthly Rent ($)", y="Apartment Size (sq ft.)")viz
ggplot() function returns an object that serves as the base of a ggplot2 visualization.
viz <- ggplot()viz # renders blank plot
Data is bound to a ggplot2 visualization by passing a data frame as the first argument in the
ggplot() function call. Layers can be added to the plot object by adding function calls after
ggplot() with a
+ plus sign. These functions have access to the data frame and can use the column names as variables.
For example, consider a data frame
sales with the columns
profit. To assign the data frame
sales to the
ggplot() object that is initialized:
viz <- ggplot(data=sales) +geom_point(aes(x=cost, y=profit))viz # renders plot
In the example above:
salesassigned to it
geom_pointlayer used the
profitcolumns to define the scales of the axes for that particular geom. Notice that it referred to those columns with their column names.