Visual Analytics

  I used mtcars just for simplicities sake. The images show mile per gallon vs weight, and also colorizes the number of cylinders in the engine as well as its horsepower. I think this works well as you can clearly visualize most the major differences between cars and the impact it has on MPG.  The colors contrast each other nicely, allowing the visual to be easily readable with little confusion. There is balance in the display as the opaqueness of the circles overlapping still allows you to clearly identify each individual data point. The alignment of the graph is also scaled consistently, not skewing the data in a way that could be misrepresentative of the data.

  What patterns or relationships did you observe? weight and horsepower have a negative effect on MPG. The lower the horsepower and weight the more mpg the car gets How did your use of grid layout or facets enhance interpretation? Grid layout lets you look at different information at the same time, providing information that can be compared instantly rather then by scrolling. In your opinion, how do Few’s recommendations help or hinder your design choices? Using grid layouts aligns with Few’s recommendations, because they present multiple comparisons in an organized and clean manner without overloading any of the plots. 

  This distribution shows the distribution of horsepower in different vehicles in the mtcars dataset. This test was mostly done using 100 horsepower cars. There were very few high horsepower cars in the dataset which could lead to some biases in the data.  I did a separate visual to see how many cylinders were in each vehicle tested. This was a bit more even then I expected seeing how low the horsepower count was.  Each of these charts is simple to read and follows the principals by being simple, uncluttered, neutral colored, and they are not intentionally misleading.

  What kind of chart did you create? I created a boxplot using. Each section has the range of mpg per cylinder on the chart and has the median as a line so you can visually identify the range median and deviation. Did your visualization reveal any  differences between groups or variables ? The number of cylinders in the engine dramatically impacts the mpg of the vehicle Did it reveal any  deviations from an expected value  or benchmark? There was a large deviation in 4 cylinder engines with some nearly performing nearly as well as 6 cylinder engines on mpg.  How well does your chart align (or not align) with the principles discussed by  Few (Chapter 9)  and  Yau (Chapter 7) ? The graphs are simple and convey the information in a clear and informative way that people can quickly understand what it is trying to say.  What challenges did you face in interpreting the visual output? Next to none, I have performed this task in several other courses...