A Grammar of Graphics

SISBID 2025
https://github.com/dicook/SISBID

TWO MINUTE CHALLENGE 🔮 👽 👼

Write down as many types of data plots that you can think of.

Go to menti.com and enter code 2979 2396

🕐 You've got 2 minutes!

What is a data plot?

data
aesthetics: mapping of variables to graphical elements
geom: type of plot structure to use
transformations: log scale, …
layers: multiple geoms, multiple data sets, annotation
facets: show subsets in different plots
themes: modifying style

Why?

With the grammar, a data plot becomes a statistic.
It is a functional mapping from variable to graphical element. Then we can do statistics on charts!
With a grammar, we don’t have individual animals in the zoo, we have the genetic code that says how one plot is related to another plot.

Elements of the grammar

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>

7 key elements:

DATA
GEOM_FUNCTION
MAPPINGS
STAT
POSITION
COORDINATE_FUNCTION
FACET_FUNCTION

Example: Tuberculosis data

(Current) TB case notifications data from WHO.
Also available via R package getTBinR.

ggplot(tb_us, aes(x = year, 
                  y = count, 
                  fill = sex)) +
  geom_bar(stat = "identity") +
  facet_grid(~ age)

TWO MINUTE CHALLENGE 🔮 👽 👼

Go to menti.com and enter code 2979 2396

What do you learn about tuberculosis incidences in the USA from this plot?
Give three changes to the plot that would improve it.

Results 🔮 👽 👼

Incidence is declining, in all age groups, except possibly 15-24
Much higher incidence for over 65s and 35-44 year olds
We do not know the underlying population size
There appears to be a structural change around 2008. Either a recording change or a policy change?
Missing information for 1998 # no longer true
Cannot compare counts for male/female using stacked bars, maybe fill to 100% to focus on proportion
Colour scheme is not color blind proof, switch to better palette
Axis labels, and tick marks?

Fix the plot

Manually selected fill colors; theme with white background for better contrast

# This uses a color blind friendly scale
ggplot(tb_us, aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity") + 
  facet_grid(~age_group)  + 
  scale_fill_manual("Sex", values = c("#DC3220", "#005AB5")) + 
  theme_bw()

Color deficiency friendly color schemes

Compare males and females

ggplot(tb_us, aes(x=year, y=count, fill=sex)) +  
  geom_bar(stat="identity", position="fill") + 
  ylab("proportion") + 
  facet_grid(~age_group) +  
  scale_fill_manual("Sex", values = c("#DC3220", "#005AB5"))

🔮 👽 👼 TWO MINUTE CHALLENGE

What do we learn about the data that is different from the previous plot?
What is easier and what is harder or impossible to learn from this arrangement?

Focus is now on proportions of male and female each year, within age group
Proportions are similar across year
Roughly equal proportions at young and old age groups, more male incidence in middle years

Separate plots

# Make separate plots for males and females, focus on counts by category
ggplot(tb_us, aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity") +
  scale_fill_manual("Sex", values = c("#DC3220", "#005AB5")) + 
  facet_grid(sex~age_group) + 
  theme_bw()

Counts are generally higher for males than females
There are very few female cases in the middle years
Perhaps something of a older male outbreak in 2007-8, and possibly a young female outbreak in the same years

Make a pie

# How to make a pie instead of a barchart - not straight forward
ggplot(tb_us, aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity") + 
  facet_grid(sex~age_group) + 
  scale_fill_manual("Sex", values = c("#DC3220", "#005AB5")) +
  coord_polar() + 
  theme_bw()

This isn’t a pie, it’s a rose plot!

Stacked bar

# Step 1 to make the pie
ggplot(tb_us, aes(x = 1, y = count, fill = factor(year))) +
  geom_bar(stat="identity", position="fill") + 
  facet_grid(sex~age_group) +
  scale_fill_viridis_d("", option="inferno")

Pie chart

# Now we have a pie, note the mapping of variables
# and the modification to the coord_polar
ggplot(tb_us, aes(x = 1, y = count, fill = factor(year))) + 
  geom_bar(stat="identity", position="fill") + 
  facet_grid(sex~age_group) +
  scale_fill_viridis_d("", option="inferno") +
  coord_polar(theta = "y")

🔮 👽 👼 TWO MINUTE CHALLENGE

What are the pros, and cons, of using the pie chart for this data?
Would it be better if the pies used age for the segments, and facetted by year (and sex)?

Go to menti.com and enter code 2979 2396

Line plot vs barchart

ggplot(tb_us, aes(x=year, y=count, colour=sex)) +
  geom_line() + geom_point() +
  facet_grid(~age_group) +
  scale_colour_manual("Sex", values = c("#DC3220", "#005AB5")) +
  ylim(c(0,NA)) +
  theme_bw()

We can read counts for both sexes
Males and females can be directly compared
Temporal trend is visible

Line plot vs barchart

tb_us |> group_by(year, age_group) |> 
  summarise(p = count[sex=="m"]/sum(count)) |>
  ggplot(aes(x=year, y=p)) +
  geom_hline(yintercept = 0.50, colour="grey50", linewidth=2) +
  geom_line() + geom_point() +
  facet_grid(~age_group) +
  ylab("Proportion of Males") +
  theme_bw()

Attention is forced to proportion of males
Direct comparison of counts within year and age
Equal proportion guideline provides a baseline for comparison

Your turn

Make sure you can make all the TB plots just shown. If you have extra time, try to:

Facet by gender, and make line plots of counts of age.
Show the points only, and overlay a linear model fit.

Resources

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