The Quantified Self is a movement to collect and analyze data on a person’s daily life, including workout habits and results. With custom maps, you can visualize your personal workout data in location context. This presenter collected detailed data about her weight training workouts for many years, but only recently put it into electronic format for exploration. With more than 10,000 rows of workout data in a table, she uses this information to learn about past and present exercise patterns.
She encountered many of the problems you’d expect to see in a set of manually entered logs, like redundancy of exercise names, missing workout details and errors. To cope with the dirty data, she developed a series of Recode scripts to simplify the cleanup process and categorize the exercises that appeared in her workout history.
To place the training metrics in context, she created a set of custom muscle-area shape files and colored them using variables in the data table. Later, she incorporated body shape maps as graphical selection filters in dashboards. Selection filters allow her to click on various body areas to drill down into detailed graphs of her workout data that would otherwise be too complex to understand.