Fitness Data Analysis Project

This project focuses on analyzing fitness data collected from various tracking devices to gain insights into users' activity levels, sleep patterns, calorie expenditure, and heart rate. The dataset used in this project consists of multiple CSV files, each containing different aspects of fitness-related data.

Objective

The primary objective of this project is to perform comprehensive data analysis and visualization to understand the relationships and patterns within the fitness data. Key areas of analysis include:

• Exploring activity levels and sedentary behavior.
• Analyzing sleep patterns and total sleep duration.
• Investigating calorie expenditure and its correlation with activity levels.
• Examining heart rate data and its relationship with activity intensity.

Technologies Used

The project utilizes the following technologies and libraries:

• R Programming Language: Used for data analysis, manipulation, and visualization.
• Tidyverse: Collection of R packages for data manipulation and visualization, including dplyr, ggplot2, and tidyr.
• Lubridate: R package for working with dates and times.
• Here: R package for managing file paths.
• Skimr: R package for generating summary statistics for data frames.
• Janitor: R package for data cleaning tasks, including column renaming and data frame tidying.

Project Structure

• Data Cleaning: The initial phase involves cleaning the dataset by removing duplicates and handling missing values. Data is also standardized to a consistent format for ease of analysis.
• Data Analysis: Various aspects of the fitness data are analyzed, including activity levels, sleep patterns, calorie expenditure, and heart rate. Summary statistics and correlations are calculated to uncover insights.
• Data Visualization: The project utilizes ggplot2 and other visualization libraries to create informative plots and charts, allowing for a visual understanding of the data relationships and trends.
• Merging Datasets: Multiple datasets are merged to facilitate comprehensive analysis and visualization across different aspects of fitness data.
• Correlation Analysis: Correlation patterns between various fitness metrics are determined to understand how different factors influence each other.

Conclusion

This project provides valuable insights into fitness data analysis, highlighting the importance of understanding activity levels, sleep patterns, calorie expenditure, and heart rate for maintaining overall health and well-being.

Feel free to explore the project files and code to delve deeper into the analysis and visualization of fitness data!