{"id":3679,"date":"2025-02-15T12:14:32","date_gmt":"2025-02-15T12:14:32","guid":{"rendered":"https:\/\/erikap.co.uk\/?page_id=3679"},"modified":"2025-02-23T22:53:39","modified_gmt":"2025-02-23T22:53:39","slug":"fitbit-tracker-data","status":"publish","type":"page","link":"https:\/\/erikap.co.uk\/?page_id=3679","title":{"rendered":"Fitbit Tracker Data"},"content":{"rendered":"

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R programming<\/p>\n

 <\/p>\n

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Fitness Tracker Data Analysis<\/span><\/h1>\n

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This analysis explores a public dataset of Fitbit users to uncover potential insights into activity habits and health trends.<\/span><\/p>\n

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Key Questions:<\/h4>\n
    \n
  1. \n

    Exercise Minutes<\/strong>: How much exercise do Fitbit users typically log per week?
    This can be compared to the recommended 150 minutes of moderate activity for maintaining a healthy lifestyle.<\/p>\n<\/li>\n

  2. \n

    Steps and Activity Levels<\/strong>: What is the average number of daily steps, categorized by activity levels?
    Understanding these patterns can reveal user preferences and highlight opportunities for wearables and coaching programs.<\/p>\n<\/li>\n

  3. \n

    Activity Patterns<\/strong>: When are users most active throughout the day and the week?
    These insights can help identify peak activity periods and encourage more consistent physical activity.<\/p>\n

    <\/h4>\n

    <\/h4>\n

    Analysis:<\/h4>\n

    Tools:<\/strong> R packages (tidyverse<\/code>, scale<\/code><\/strong>)
    Data Source:<\/strong> The dataset is publicly available on Kaggle (Fitbit Fitness Tracker Data) and is classified as 100% complete, credible, and compatible.<\/p>\n

    Sample Overview:<\/strong>
    The dataset contains activity data from 33 Fitbit users over one month. However, several limitations should be considered:<\/p>\n