Fitness Tracker Data: R
My Google Capstone Project using Exploratory Data Analysis to explore fitness tracker data using R
Introduction
The rivalry between R and Python has always been a strong one! R is the chosen language of the Google Data Analytics Certificate. As a result, I have learned the building blocks of the language and its immense power. Not to mention R also has a great data science community.
Findings
The market can be split into two distinct sections: one which focuses on steps and calories whilst the other focuses on athletic performance. This has a key implication for tailoring marketing strategies. Of course, products which do not fall into a key demographic often are the ones to fail as shown by Porters 3 Generic strategies.
The amount of athletes was measured in two ways:
- Low HR levels
- Percentage of activity that was intense
Athletes have a lower heart rate. We have shown that these athletes also perform more intense exercise.
Athletes tend to have a higher number of steps and burn more calories.
