Module # 9 assignment

Create your own multi-variates visualization graph (RStudio, ggplot2, Adobe Illustrator, Plotly or Tableau). You can use your own data you find or use the data we employ in class. In your discussion on your blog, judge if multivariables is a good way to visualize the outcome or not and discuss how you can implement the 5 principles/rules of design to your work, I covered in this module's presentation. 

The five principles of design include Alignment, Repetition, Contrast, Proximity, and Balance. 

The dataset I am using is carprise.csv. Below is a screenshot of the data. It tells us information such as car type, minimum and maximum price, the actual price, range price, mpg city and mpg highway. 

For my visualization, I used a scatter bubble chart with multiple variables. I focused on mpg.highway, price, range.price and type of car. From these we are able to see various relationships. We can see that as mpg for highway increases, the price tends to stay lower. When the mpg is lower, the price is actually higher. We also see lower mpg cars are vans while higher mpg cars tend to be smaller cars. Large, sporty, and midsize cars tend to be pricier. The one variable that is harder to establish a relationship is range.price. If this visualization was recreated, this variable would be switched out. But we can see that vans tend to have a higher range in price point while compact cars tend to stay relatively the same size. 

> ggplot(df, aes(x = MPG.highway, y = Price, size = Range.Price, color = Type)) +
+   geom_point(alpha = 0.7) +
+   labs(title = "MPG.highway vs Price by Car Type",
+        x = "MPG.highway",
+        y = "Price") +
+   scale_size_continuous(range = c(1, 10))  

I believe multivariables is a good way to visualize the outcome. We are able to understand how these variables effect each other. I also attempted to include the five principles of design. Since this is a scatter bubble chart, it is harder to maintain balance. Due to this chart showing relationships, there will be a skew of data rather than even distribution. The use of contrast is effectively shown with color to demonstrate the various vehicle types. Proximity is achieved through the plotting of points. The visualization is properly aligned. And repetition is appropriately used with the bubble plots to effectively portray relationships.