Author: Shivani Kotian
Institution: California State University, Los Angeles
Email: skotian@calstatela.edu
The average person spends about 26 years sleeping in their life, equating to 9,490 days or 227,760 hours. That's one-third of our entire lives spent asleep in bed! Yet for most people, sleep remains a mystery, and they don't understand what actually happens during sleep. It's challenging to improve something without fundamental understanding. The bottom line is - The better you sleep, the longer you live. Sleep significantly affects our productivity and health throughout our lives.
Poor sleep quality has been linked to various ailments from dementia to attention lapses, reduced cognition, delayed reactions, and mood shifts. My motivation for analyzing this dataset is to identify factors affecting quality sleep and determine what changes people can make to improve their sleep.
My approach to analyzing and visualizing sleep patterns involves exploring the "Sleep Efficiency" dataset, which includes:
- Demographics (age, gender)
- Sleep metrics (bedtime, wakeup times, duration, efficiency)
- Sleep stages (REM and deep sleep percentages)
- Sleep quality indicators (awakenings)
- Lifestyle factors (caffeine/alcohol consumption, smoking status, exercise frequency)
The visualizations will uncover patterns and correlations within the data through:
- Histograms for overall sleep efficiency distribution
- Age-based sleep duration analysis
- Scatter plots for sleep duration vs. quality correlations
- Box plots comparing lifestyle factors' impact
- Gender-based sleep pattern analysis
- Deep sleep vs. light sleep percentage analysis
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Data Analysis: Focus on sleep patterns and lifestyle correlations through categorical formats and basic visualizations.
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Visualization Design: Emphasize accessibility and preattentive learning by:
- Simplifying complex visuals
- Ensuring colorblind accessibility
- Iterative improvement of charts
- Proper handling of outliers
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Results Interpretation: Transform data into actionable insights about factors affecting sleep efficiency.
Future scope includes analyzing:
- Phone usage impact on sleep
- Mental health correlations
- Comparison of healthy vs. harmful habits
- Age group and population-specific patterns
- Tableau Desktop: Data Visualization and Analysis
- Microsoft Excel: Data Storage
- Sleep Efficiency Dataset: Kaggle
- Sleep and Dementia Research: doi.org/10.1016/j.smrv.2017.06.010