CSCE 5320 Homework 7: Arranging Tables
Sai Kiran Dasari
The ten views implement the supplied assignment with the UCI Bike Sharing daily dataset. The reusable closure-based barChart and chainable ordering follow the supplied starter7.html. All calculations are in index.html.
Dataset: https://gist.github.com/SaiKiran6305/9c3df744156e126e83febe11d6f53eff
Original source: Hadi Fanaee-T (2013), Bike Sharing, UCI Machine Learning Repository. https://doi.org/10.24432/C5W894 . CC BY 4.0. Original daily CSV values are unchanged. Weather values remain normalized to avoid ambiguity in source unit conversion metadata.
731 daily observations cover 2011-01-01 through 2012-12-31. There are 12 months, 7 weekdays, 16 original columns, and all 84 month-weekday combinations. No blank values or duplicate dates were found.
Q1 bars show total rentals by month across both years. Derived ordering is the monthly casual-rental share. Q1 stacked and grouped charts use the same month-weekday rental totals and vertical domain. Q2 heatmaps use mean daily rentals by month and weekday, preserve the same sequential colour domain, and reorder only months by their unweighted row means. Q3 uses the same monthly totals for the donut and all 731 daily records for four-dimensional parallel coordinates. The second parallel view moves total rentals from the final axis to the first with every other setting fixed.
The initial view is Figure 8, the radial chart. Before viewing Figure 2, the student must record their own estimated top-five month ranking. The report must use that observation rather than an invented result.
View menu: 1-3 reusable bars; 4 stacked bars; 5 grouped bars; 6-7 heatmaps; 8 donut; 9-10 parallel coordinates.
The source weather variables temp, hum, and windspeed remain normalized. Weather category colours use codes 1 (clear/partly cloudy), 2 (mist/cloudy), and 3 (light rain/snow), the three categories actually present in the daily data. No season names are used because the archived readme and current repository describe those codes differently.