Creating the Dot Plot Variance chart
The data preparation was used in the previous blog entitled: Diverging Bar Charts – Plotting Variance with ggplot2.
Refer to that if you need to know how to create the data prior to this tutorial.
Setting up the Dot Plot Variance chart
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library(ggplot2)
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ggplot(mtcars, aes(x=CarBrand, y=mpg_z_score, label=mpg_z_score)) +
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geom_point(stat='identity', aes(col=mpg_type), size=6) +
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scale_color_manual(name="Mileage (deviation)",
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labels = c("Above Average", "Below Average"), -
values = c("above"="#00ba38", "below"="#0b8fd3")) + -
geom_text(color="white", size=2) +
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labs(title="Diverging Dot Plot (ggplot2)",
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subtitle="Z score showing Normalised mileage", caption="Produced by Gary Hutson") +
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ylim(-2.5, 2.5) +
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coord_flip()
Setting up the Dot Plot Variance chart
This is very similar to the previous plot we created in the previous post, however there are a few differences. The main difference is that we use a geom_point() geometry and set the colour of the points based on whether the said point deviates above and below the average. In addition, we use the geom_text() to set the colour of the text in the points to white and specify the size of the text. The final difference is that I have added a Y limit (ylim) range of -2.5 standard deviation to positive 2.5 standard deviations.
Running this block of code, along with the data preparation code, will give you a chart that looks as below:
This blog was written by Gary Hutson, Principal Analyst, Activity & Access Team, Information & Insight at Nottingham University Hospitals NHS Trust, and was originally posted here.