Create a dfidx object

I've pre-loaded the mlogit package and a data frame called chocolate_df that has the chocolate data in long format. We want to fit a choice model to this data, but first we need to convert it to a dfidx object, the indexed data format that mlogit() requires. This can be tricky, but it is vital to using mlogit() successfully. Before you start writing code, type head(chocolate_df) directly into the console so that you can refer to the column names.

This exercise is part of the course

Choice Modeling for Marketing in R

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Exercise instructions

dfidx() needs two index columns: one that identifies the choice question and one that identifies the alternative within that question. I've added a chid column to chocolate_df that numbers the choice questions. Fill in the two index column names for idx.
Identify the name of the column where the choice is stored and fill it in for choice.
Use str() to confirm that you've converted the data to a dfidx object.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# use dfidx() to convert chocolate_df to a dfidx object
chocolate <- dfidx(chocolate_df, 
                   idx = c("____", "____"), 
                   choice = "____")
                         
# use str() to confirm that chocolate is a dfidx object

Edit and Run Code

Choice Modeling for Marketing in R

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Our goal for this chapter is to get you through the entire choice modeling process as quickly as possible, so that you get a broad understanding of what we can do with choice models and how the choice modeling process works. The main idea here is that we can use a choice model to understand how customers' product choices depend on the features of those products. Do sportscar buyers prefer manual transmissions to automatic? By how much? In order to give you an overview, we will skip over many of the details. In later chapters, we will go back and cover important issues in preparing data, specifying and interpreting models and reporting your findings, so that you are fully prepared to use these methods with your own choice data.

Exercise 1: Why choice?Exercise 2: Choice data Exercise 3: Inspecting choice data Exercise 4: Finding the levels of a factor Exercise 5: Inspecting a choice observation Exercise 6: What did people choose?Exercise 7: Fitting and interpreting a choice model Exercise 8: Fitting a choice model Exercise 9: Interpreting parameters Exercise 10: Using choice models to make decisions Exercise 11: Predicting choice shares Exercise 12: Plotting choice shares