Acknowledgement

This session shadows Chapter 3 of the excellent:

ggplot2

Is one of several plotting systems in R

{plotly} is used by Public Health Scotland

Why is ggplot popular?

1. Well designed and supported
2. Highly versatile
3. Attractive graphics (with a little work)

ggplot2

ggplot2 is part of the tidyverse.
So, at the top of your script type:

library(tidyverse)Copy Code

Pressures in A&E

What is a data frame?

A data frame stores tabular data:

Artwork by @allison_horst

tibble = data frame

In the tidyverse you may see the term "tibble"
We’ll take "tibble" to be synonymous with "data frame"

A tibble... is a modern reimagining of the data.frame...

Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does not exist).

This forces you to confront problems earlier, typically leading to cleaner, more expressive code.

emphasis added to quote

Viewing the data frame

Option 1

Viewing the data frame

This brings up a view of the data in a new tab:

Viewing the data frame

Click here to show the data frame in a new window

Useful when using multiple monitors

Viewing the data frame

Option 2

Type the name of the dataset in editor/console, and run the line (shortcut Ctrl + Enter)

"The simple graph has brought more information to the data analyst's mind than any other device"

John Tukey, quoted in R for Data Science

Q. Is a change in the number of cubicles available in A&E associated with a change in length of attendance?

Let's explain the code

We begin our plot with ggplot2

ggplot() +Copy Code

Inside ggplot() we can specify the dataset

ggplot(data = capacity_ae)Copy Code

Next, we add layer(s) with + at the end

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))Copy Code

A statistical graphic

Shape/colour/size geom all default

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))Copy Code

Functions ()

Other arguments like axes x and y are in a particular order;

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))Copy Code

It is possible to write it like:

ggplot(data = capacity_ae) + 
  geom_point(aes(y = dwait, x = dcubicles))Copy Code

But could be confusing.

Functions ()

Here, we have provided ggplot() with one named argument

And given aes() two named arguments

Unspecified (yet required) arguments will often revert to default values

Shorthand

Since ggplot2 knows the order of essential arguments, it is not necessary to name arguments:

data = can be omitted

and

x = goes first and y = goes second

ggplot(capacity_ae) + 
  geom_point(aes(dcubicles, dwait))Copy Code

geoms

We tend to describe plots in terms of the geom used:

Layering geoms

We can display more than one geom in a plot:

to add a layer

ggplot(data = capacity_ae)
geom_point(aes(x = dcubicles, y = dwait))
geom_smooth(aes(x = dcubicles, y = dwait))

then specify another geom...

Your turn

This is our current plot:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))Copy Code

Add a geom_smooth layer (to help identify patterns)

Hint: Don't forget the + and aes() values in the new layer

Your turn - Answer

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  geom_smooth(aes(x = dcubicles, y = dwait))Copy Code

One more thing

We'd probably prefer a linear fit rather than a non linear fit:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm")Copy Code

What is happening here?

Adding another dimension

Put an argument inside aes() if you want a visual attribute to change with different values of a variable.

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait, 
                 colour = staff_increase)) +
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm")Copy Code

We could have equally have chosen size or shape but these make graphic less clear

What is happening here? - Answer

The two sites have indeed seen an increase in staff levels which has had an effect on the dwait even though dcubicles are relatively low.

Important distinction

If you want a visual attribute to be applied across the whole plot, the argument goes outside aes():

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait),
             colour = "red") + 
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm")Copy Code

This works too because the colour is generically applied:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait,
             colour = "red")) + 
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm")Copy Code

Important distinction

Or apply a size globally:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait),
                 size = 4) +
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm")Copy Code

Layering geoms

To avoid duplication, we can pass the common local aes() arguments to ggplot to make them global. Instead of duplicating the same aes(dcubicles, dwait):

ggplot(data = capacity_ae) +
  geom_point(aes(dcubicles, dwait)) +
  geom_smooth(aes(dcubicles, dwait))Copy Code

Move the aes to the "global":

ggplot(data = capacity_ae, aes(dcubicles, dwait)) +
  geom_point() +
  geom_smooth()Copy Code

Small multiples magic

Another way to visualise the relationship between multiple variables is with a facet_wrap() layer:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  facet_wrap(~ staff_increase)Copy Code

Small multiples

Another way to visualise the relationship between multiple variables is with a facet_wrap() layer:

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  facet_wrap(~ staff_increase,
             ncol = 1)Copy Code

Q. How are "wait" values distributed?

Histogram

ggplot(data = capacity_ae) + 
  geom_histogram(aes(dwait))Copy Code

Q. How are “wait” values distributed?

Histogram

ggplot(data = capacity_ae) + 
  geom_histogram(aes(dwait),
                 binwidth = 10)Copy Code

With "bins" set so more uniformed in spread:

Q. Number of attendances by site?

Bar plot

ggplot(data = capacity_ae) +
  geom_col(aes(x = site,
               y = attendance2018))Copy Code

Reorder site by attendances

ggplot(data = capacity_ae) +
  geom_col(aes(x = reorder(site, attendance2018),
               y = attendance2018))Copy Code

Q. Number of attendances by site?

Boxplot

ggplot(data = capacity_ae) +
  geom_boxplot(aes(staff_increase, dwait))Copy Code

Plot labels

Can be applied to all types of charts:

ggplot(data = capacity_ae) +
  geom_boxplot(aes(staff_increase, dwait)) +
  labs(title = "Do changes in staffing...",
       y = "Waiting")Copy Code

To save a plot

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) + 
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm") +
  ggsave("plot_name.png")Copy Code

To save a plot

ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) + 
  geom_smooth(aes(x = dcubicles, y = dwait),
              method = "lm") +
  ggsave("plot_name.png", units = "cm",
         height = 10, width = 8)Copy Code

By default saves a plot in the same dimensions as plot window.

In future, you'll wish to add height, width and "units" arguments to specify plot dimensions.

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