In this week in Visual Analytics, we are asked to generate a visual from the mtcars dataset that is either based on correlation and regression analysis.
In this case, I will be conducting correlation analysis on the variables number of cylinders (cyl) and horsepower (hp). That is, I want to know if these variables have a positive correlation or negative correlation relationship.
Let's begin by plotting the relationship via ggplot2 in a scatterplot. I will also draw in a line of best fit using the lm argument.
Here's my code:
The resulting output:
Interestingly, there appears to be a rather strong positive correlation between the number of cylinders and horsepower. Perhaps, this is alluding to cars that have a greater number of cylinders have a higher horsepower. Let's take a look at the correlation coefficient.
With a value of .832, that's a pretty high correlation value!
Lastly, let's finish up by creating a linear regression model of these variables:
Looking at the significance codes, cyl is highly significant at the (***) mark at 0.001. However, the model can only capture about 68% of the variability so it is a moderately strong correlation.
Reflecting on the design remarks of Few from the textbook, my visual does follow a few of his best practices. To begin, I do feature some grid lines for greater readability of graph and one can easily determine the points and where they fall in terms of cyl and hp. However, I do see how grid lines can be helpful if multiple scatter plots are used in the analysis. In addition, my visual also has a line of best fit so that we easily see the correlation's linear shape and positive slope.
I appreciate Few's recommendations when it comes to visualizing correlations between variables. Although my visual does not include all his suggestions and I know that they will be helpful for other datasets.
~ Katie
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