Promoting the use of R in the NHS

#### Blog Article

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

1. `library(ggplot2)`
2. `ggplot(mtcars, aes(x=CarBrand, y=mpg_z_score, label=mpg_z_score)) +`
3. `geom_point(stat='identity', aes(col=mpg_type), size=6) +`
4. `scale_color_manual(name="Mileage (deviation)",`
5. `labels = c("Above Average", "Below Average"),`
6. `values = c("above"="#00ba38", "below"="#0b8fd3")) +`
7. `geom_text(color="white", size=2) +`
8. `labs(title="Diverging Dot Plot (ggplot2)",`
9. `subtitle="Z score showing Normalised mileage", caption="Produced by Gary Hutson") +`
10. `ylim(-2.5, 2.5) +`
11. `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.