Data

Consider the dataset you select for the project.  You can use almost any dataset, with my approval.  However, this course isn’t focused on the data cleaning process.  I would suggest the following criteria:

• Use relatively clean data.  This means data that are complete and correct, with few anomalies.  Use data that have few enough missing values that you can eliminate those with no effect on your results.
• Use data with at least 1 (preferably 2) categorical variables and at least 2 (preferably 3) numeric variables.  This gives you the flexibility to construct many different types of visualizations.
• Use enough observations (rows) that you can see the pattern, trend or relationships in your charts, but not so much data that your computer runs like a turtle.  The sweet spot depends on the resources of your computer.  Try some datasets and see what works in a reasonablie time for you.
• Creativity counts.  Select a domain of interest and ask some questions that can be visually captured with the dataset that you selected.  Your dataset can be drawn from the domains of science, technology, business, politics, social justice, sports, or practically any area, as long as you have data that support the exploration and explanation visualization processes.

1. What is the title of this presentation?  The title should capture the focus of your visualizations.
2. What is the organization/individual for whom this presentation is prepared?
3. What is the mission of this organization (or, for an individual, what is the underlying interest of the individual in pursuing this applications domain)? 
   
4. What is the purpose of this project?
5. Why will this project support the mission of the organization/individual?
6. Tell us about your dataset:
• How is it related to the organization?
• How is it related to the purpose that you stated?
• How many attributes?  Tell us about them.
• How many observations?  Any issues, cleanup?  (It's okay if it was completely clean.)
7. Walk through your exploration process.  I don't just want to see a series of slides with tables, graphs and charts.  I want to see how you used these visualizations to explore your dataset, to get a better understanding of the data, and show us how you progressed from one visualizaiton to the next.  What question was answered, or what new question was raised that inspired you to try another kind of graph or chart?  Were things too cluttered, and you needed clarification? Did you notice a trend or a relationship and decide to explore that further?  Did you want more detail?  Less detail?  A different type of visualization?  
8. Discuss the technical details of your choices:  Explain your use of color, tick marks, annotations, titles, axes scale, or any other feature that you included.
9. After showing us your visual exploration of the dataset, visually present some conclusions, talking points, or other explanatory visuals.  Did you make a point?  Did you tell a story?
   

1. Include at least one clustered (side-by-side) bar or column chart.
2. Include at least one stacked bar or column chart.
3. Include at least one scatter plot.
4. Include one graph or chart using RBase graphics.  (A hstogram or boxplot might work well, but you are not limited to those.)
5. Include one line or area graph.  It may not be appropriate for your data.  Do it anyway, and explain why it is or is not appropriate.
6. Include one R dotplot, or violin plot.
7. Use scale_color and/or scale_fill at least once.
8. Include at least one faceted graph.
9. Include one Tableau chart from your in-class Marks assignment.
10. Include one Tableau chart from your in-class drill-down assignment.
11. Include one Tableau chart from your in-class new-feature assignment.
    
12. For excellence points:  use melt and/or reshape in R.
13. For excellence points:  Find some package in R that we didn't use in class.  Use it.

1. Appropriate use of data.frames.
2. Conversion to/from factors.
3. Selection of rows and columns to extract the appropriate subset of data for graphing in R.
4. Approrpiate selection of graph for your data type.
5. Correct interpretation of the graph.
6. Understanding of the effectiveness of the graph.
7. Creativity.
8. Effective use of color for the application and for your audience.
9. Adherence to Tufte, Cleveland, etc. guidelines for effective visualization.
10. I am interested in the process!!  You may show the same graphs through several iterations, and how you refined it to make it better.

1. Your PPT presentation.
2. A zip file with your entire R application.  
3. Your Tableau .twbx file(s)