Bellabeat is a high-tech company that manufactures health-focused tech products. They collect data on activity, sleep, stress and reproductive health to empower women with knowledge about their own health and habits. Urska Srsen, Bellabeat's Co-founder and Chief Creative Officer asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices and recommendations for how these trends can inform Bellabeat marketing strategy. We will focus on one of Bellabeat’s products: Bellabeat app.

• Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer
• Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
• Bellabeat marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Bellabeat’s marketing strategy. You joined this team six months ago and have been busy learning about Bellabeat’’s mission and business goals — as well as how you, as a junior data analyst, can help Bellabeat achieve them.

Identify the trends in smart device usage, find out how these trends apply to Bellabeat customers and offer insights on how thses trends can influence Bellabeat marketing strategy

The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through Mobius.

• This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.
• Note that the dataset is not a current one and that the data was only collated for a period of two months. These limitations might affect the results of the analysis and the insights offered

The data analysis will be carried out in R due to the amount of data and in order to create data visualizations for the stakeholders. But first I previewed the datasets in Exel to get feel of them. The following R library will be used:

• tidyverse
• here
• skimr
• janitor
• lubridate
• ggpubr
• ggrepel

The following datasets are to be used in the analysis

• dailyActivity_merged
• dailyIntensities_merged
• dailySteps_merged
• dailySleep_merged

link to Rstudio: #https://rstudio.cloud/content/4321528