Module # 9 assignment

 


I used the default mtcars visualization because it has many continuous and categorical car performance variables. It also is a familiar dataset that lets you find relationships between horsepower, engine displacement, fuel efficiency and other performance numbers. It contains both numeric and factor variables, so it's good to test whether multivariate design techniques in ggplot2 reveal something more than two-variable plots.

It plots engine displacement (disp) versus quarter-mile time (qsec), with horsepower (hp) coded in color, weight (wt) via point size and number of cylinders (cyl) via shape. The plot is also broken down by transmission type for automatic versus manual cars. This layout reveals a few things - larger engines and more horsepower get the quarter-mile done faster than heavier cars. It also shows that manual transmissions generally give better acceleration with the same displacement.

A multivariate visualization worked well here because I could see how engine power, weight and transmission interact simultaneously. A scatterplot would have shown two variables only, but with size, shape and color encoding the plot tells more about performance trade-offs. This shows the whole dataset in one view without overwhelming the viewer with lots of clusters and trending performance across car types.

I used at least three of the 5 principles from the module - alignment, contrast & balance - in designing the plot. Alignment was done by transmission faceting, so that the plot could be compared side by side with the same scales. In the case of horsepower, contrast came in the form of a red to blue color gradient. Finally balance was achieved by consistent sizing, minimal gridlines & even spacing between facets - no section could dominate the visual. These design choices produced a clean, logical graphic telling the data story.

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