Module 7: Mtcars Visualizations

Hi everyone!

This week we learned how to utilize R to capture new visualizations such as box plots and scatterplots. We explored the three dimensions of distributions including shape, location, and spread. I have some experience utilizing ggplot2, so I utilized this package for my visualizations. 

I chose to explore the spread of a car's fuel efficiency (miles per gallon) for manual transmission cars versus automatic cars. It is clear from these box plots that while there is a wider range of fuel efficiency for automatic cars the average mpg is much higher than all manual cars.

I decided to explore location through a scatterplot of fuel efficiency versus the horsepower of cars with different numbers of cylinders. It is clear from the scatterplot that cars with the least number of cylinders have the lowest horsepower but the highest fuel efficiency. There seems to be a larger range of efficiency for 4 cylinders unlike 8 cylinders that have a wider variety of horsepower with similar efficiencies.
Few (2009) discusses the importance of clarity in visualizations, so in scatterplots there should not be too much data that the points are overlapping heavily and box plots should not have too many outliers skewing the plot. Both the boxplot and scatterplot graphs follow Few's recommendation by utilizing color to differentiate each categorical variable and create consistency in the data while eliminating clutter.

Check the code out in Github!

-Ramya's POV

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