Let’s get up and running with mmtable2 so we can make a killer table that impresses your bosses and helps you make reports that get you promoted.

Our goal is to analyze the mpg dataset (fuel economy of vehicles by important vehicle attributes like manufacturer, number of cylinders, etc). The table we put to into our report:

• Summarizes the average fuel econmony (City and Highway)
• By two categories: Car Manufacturer and Number of Engine Cylinders

First, we need to:

• Load Libraries: Load mmtable2 , gt, and tidyverse.
• Import Data: We’re using the mpg dataset that comes with ggplot2.

Our next step is to use dplyr and tidyr to get the data into the right format for the table. We’ll use 4 important data wrangling operations:

• group_by(): Groups by our grouping columns: Manufacturer and Number of Engine Cylinders.
• summarise(): We’ll calculate the average fuel economy for both City and Highway. We combine with the across() function which makes it easy to summarize multiple columns. We use the mean() function to calculate the averages by group.
• ungroup(): Ungrouping is needed to remove any leftover groups.
• pivot_longer(): Used to convert from a “wide” to a “long” data frame, which stacks the City and Highway average fuel economy on top of each other. If you’re familiar with ggplot2 the “long” format is critical to plotting.

With the mpg data summarized and in the long format, we can now use mmtable2 to make a table, just like we would use ggplot2 to make a plot. We perform 3 actions:

1. Setup the mmtable(): This is just like ggplot() function in ggplot2.
2. Specify the headers locations: This tells the location for each header needed to organize the table.
3. Format the header and table cells: This adds the lines that help to differentiate groups in our data.