Examining the structure of categorical inputs

For this exercise you will call model.matrix() to examine how R represents data with both categorical and numerical inputs for modeling. The dataset flowers (derived from the Sleuth3 package) is loaded into your workspace. It has the following columns:

Flowers: the average number of flowers on a meadowfoam plant
Intensity: the intensity of a light treatment applied to the plant
Time: A categorical variable - when (Late or Early) in the lifecycle the light treatment occurred

The ultimate goal is to predict Flowers as a function of Time and Intensity.

Instructions

The data frame flowers is in your workspace.

Call the str() function on flowers to see the types of each column.
Use the unique() function on the column flowers$Time to see the possible values that Time takes. How many unique values are there?
Create a formula to express Flowers as a function of Intensity and Time. Assign it to the variable fmla and print it.
Use fmla and model.matrix() to create the model matrix for the data frame flowers. Assign it to the variable mmat.
Use head() to examine the first 20 lines of flowers.
Now examine the first 20 lines of mmat.
- Is the numeric column Intensity different?
- What happened to the categorical column Time from flowers?
- How is Time == 'Early' represented? And Time = 'Late'?

Take Hint (-15xp)