Difference between revisions of "Carolyn.Rush"
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===Assignment 4 === | ===Assignment 4 === | ||
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Note: This is an adjustment to Project 2. The goal is to present the data in a way that is easy to read (and understand) and allows the viewer to manipulate the image for their purposes. | Note: This is an adjustment to Project 2. The goal is to present the data in a way that is easy to read (and understand) and allows the viewer to manipulate the image for their purposes. | ||
Latest revision as of 20:25, 12 November 2008
Contents
Assignment 4
Note: This is an adjustment to Project 2. The goal is to present the data in a way that is easy to read (and understand) and allows the viewer to manipulate the image for their purposes.
Additions and Changes:
1. Classes (colours/ departments) 2. Colours and Design (from class feedback and Pooya's suggestions) 3. Widgets (Slider: size of the ellipse, movement along the axis (will be helpful when there is a greater amount of data to view; Button to RESET the screen; A pull-down menu that enables the user to select what data value gets plotted along X, and what gets plotted along Y)
Assignment 3
Project 2a - Scatterplot
Note: Description emailed to Dr. Shaw on October 8, 2008. My proposal for this project would be to take a very basic data set (I may actually generate it myself or try to get something from work) on employee data. For example, it would include: Name, Age, Tenur (time with company), and then Specialization. For this first scatterplot, I will be focusing on age (number), tenur (number) and specialization (colour).
My intent is to try to create a class "employee" that has the age, tenur and specialization variables. Then I will create arrays for each of the different areas (eg. array of age, array of tenur, array of specialization) and use those to create dots on the screen that represent each employee.
Project 2b - Scatterplot
ADJUSTMENTS TO PROPOSAL:
The code does not use a class but rather a function that reads all of the data points. Then draw is simply called to apply all of the data points and assigned colors to the screen to form the scatterplot
LEGEND:
==X-Axis== (Age ... increases as data points go across the screen)
==Y-Axis== (Tenure... increases as data points go down the screen)
==Color== (Specialization/ Department)
Light Green - Outreach Representatives
Dark Green - Engineering
Yellow - Field Operations
Red - Corporate
Blue - Finance
Purple - Marketing
Orange - HR
OBSERVATIONS:
You can see that there are quite a few engineers which have a higher age and tenure, meaning that they are close to retiring. This is an important salient feature for the company.
