My Projects

Cyclistic bike-share analysis

Cyclystic share data

A slideshow to showcase the procedure for gaining insights.

This slideshow takes you through the few key steps I used in RStudio to clean the datasets, summarize the data, and create a final table for visualizing the insights. It highlights the cleaning process, how I handled inconsistencies, and how I transformed the data into a structured format for clearer analysis. The final visualization presents the key insights, making the data more accessible and actionable.

Daily insights

This visualization compares ride lengths between casual riders and members across the week.Casual riders take significantly longer rides, peaking on Thursday, while members show more consistent ride durations, with Friday having the highest ride length. The data highlights how casual cyclists ride more for leisure, while members follow a steadier routine, likely for commuting.

Tableau Insights

This visualization compares ride lengths between casual riders and members across the week. Casual riders take significantly longer rides, peaking on Thursday, while members show more consistent ride durations, with Friday having the highest ride length. The data highlights how casual cyclists ride more for leisure, while members follow a steadier routine, likely for commuting.

(~Facet Wrap)

With a slight adjustment, we can view Casual and Members separately for clearer insights. It shows that Casual riders take fewer short-term trips compared to long-term ones.

Member Casual

Displayed back-to-back for easy daily comparison.

Monthly Insights

Casual riders take spontaneous trips, peaking on weekends but dipping midweek. Members, on the other hand, ride with consistency, following a steady routine. When separated, the story becomes clear—Casual riders explore, Members commit.

Every trend has a story, and this data reveals how people move through the week....View my dashboard on Tableau Public!

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