Explore Bike Share Data in 3 US Cities
Bike Share Data: In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.
This project will be completed in a Jupyter notebook
PROJECT SPECIFICATION
CODE QUALITY
The student writes error free R code.: All code is functional (i.e. no errors are thrown by the code). Warnings are okay.
The students R code has appropriate comments, functions, and loops.: The project uses functions and loops where possible to reduce repetitive code. Comments and docstrings are used as needed to document code functionality.
DATA VISUALIZATIONS
The student asks questions which can be explored and answered with the data.: Questions and observations are well documented in a way that is easy to understand for any audience. At least three questions of interest that are explored.
The insights are clearly conveyed for each question.: At least three visualizations are used in the presentation to convey key insights. These key insights match those documented in the summary.
Each visualization is associated with comments that accurately depict their purpose.
All of the plots have appropriate labels.: All visualizations include a title and axis labels, have a legend where applicable, and are easily understood.
Every visualization should have a chart title, X-axis label, and a Y-axis label.
SUMMARY STATISTICS
All questions are answered using numeric summaries.: For each question appropriate summary statistics are used to support each plot. Questions comparing groups have visualizations that compare as well compare means, medians, and other summaries.
Descriptions of summary statistics are accurate.: Any summary statistics that are used in answering the main questions of interest are described appropriately.
Suggestions to Make Your Project Stand Out!
During the exploration, use a variety of plot types to explore different relationships in the dataset. Be willing to investigate unexpected relationships and dont be afraid of finding a dead-end in your exploration.
As part of your exploration, document your thought processes to justify the steps you take.
When you select key insights for your explanatory presentation, focus on one or two paths that tell a compelling story.
When planning your explanations flow, document design decisions that make your visualizations information-rich but still easy to read.
Gather feedback from others to get a different perspective on your explanatory presentation. Document that feedback and note any changes in your designs based on that feedback.
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