Invoking the 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 cost and 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:

• The ggplot object or canvas was initialized with the data frame sales assigned to it
• The subsequent geom_point layer used the cost and profit columns to define the scales of the axes for that particular geom. Notice that it referred to those columns with their column names.
• The variable name of the ggplot object is stated so the plot is viewable.

In ggplot2 aesthetics are the instructions that determine the visual properties of a plot and its geometries.

Examples of ggplot2 aesthetics include:

• scales for the x and y axes
• color of the data points on the plot based on a property or on a color preference
• the size or shape of different geometries

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:

1. At the canvas level: All subsequent layers on the canvas will inherit the aesthetic mappings defined when the ggplot object was created with ggplot().
2. At the geom level: Only that layer will use the aesthetic mappings provided.

For example, the following code assigns aes() mappings for the x and y scales at the canvas level:

viz <- ggplot(data=airquality, aes(x=Ozone, y=Temp)) +
       geom_point() + 
       geom_smooth()

In the example above:

• The aesthetic mapping is wrapped in the aes() aesthetic mapping function as an additional argument to ggplot().
• Both of the subsequent geom layers, geom_point() and 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 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.

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 x and y axes as well as a title, subtitle, or caption. The list of available label arguments can be found in the labs() documentation.

The following 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

The 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 y axis.

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