Exploratory Data Analysis for BellaBeat, a fitness smart gadgets and wearables maker using R Programming Language
Bellabeat is a tech company that makes health smart gadgets.
They have a product catalogue of smart devices which function by collecting key user-data which includes, activity, sleep, stress, reproductive health, etc to empower women about their daily habits and inform them on their health lifestyles.
This case study focuses on the analysis of the user data generated by the fitness smart devices to see how it can help Bellabeat become smarter in their marketing, messaging, and maximize growth opportunities in the fitness industry.
This Case Study is completed as part of my Google Data Analytics Professional Capstone projects and i will be working with the R programming languge for this analysis.
The outcome of this analysis will help Bellabeat gain more insights on how consumers use smart devices and apps and this will help Bellabeat develop new on-demand features for their flagship Bellabeat App.
Bellabeat App is a consumer-centric app which provides users with health lifestyle data that covers activity, sleep, stress, menstrual cycle, and mindfullness habits. The Bellabeat App pulls and aggregate these data from their wearable smart devices that their customers use.
My task is to uncover trends on how consumers use non-bellabeat smart devices. The idea is to transfer these uncovered trends and insights to similar Bellabeat products and use them to inform Bellabeats marketing strategy.
Our stakeholders include:
Urska Srsen: Ursen is Bellabeat cofounder and chief creative officer.
Sando Mur: Sando is a cofounder of Bellabeat and a key member of Bellabeat executive team
Bellabeat Marketing Analytics Team; who would be directly using and implementing the insights generated from this study.
We are using the publicly available Fitbit Fitness Tracker which is gathered from Kaggle.com. Thanks to the Morbius team for putting it up.
We are going to be using these list of R packages for our analysis.
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tidyverse: this includes dplyr for data wrangling, tidyr for tidying data, and ggplot2 for data visualization
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janitor: for data cleaning and tidying, contains functions for column name cleaning, duplicate rows removal, handling missing data, etc
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lubridate: for working with date and time data
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skimr: for summarizing data
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ggrepel: extends the capabilities of ggplot2 in making data points cleaner and removing overlappings
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ggpubr: extends the abilities of ggplot2 to create multiple plots, etc.
You can find all case study codes and visualizations here ... BellaBeat Case Study