Introduction to ggplot2

# Introduction to ggplot2
----
## **Session 4**
### 
###

]

---

# Acknowledgement

This session shadows [Chapter 3](https://r4ds.had.co.nz/data-visualisation.html) of the excellent:

---

# ggplot2

Is one of several plotting systems in R

{plotly} is used by [Public Health Scotland](https://github.com/Public-Health-Scotland/scotpho-plotly-charts/blob/master/plotly_chart_functions.R)
---

# Why is ggplot popular?
</br>

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

.footnote[1. Why start with ggplot : http://varianceexplained.org/r/teach_ggplot2_to_beginners/
2. Argument against:
https://simplystatistics.org/2016/02/11/why-i-dont-use-ggplot2/]

---

[</br> <img class="center" src="data:image/png;base64,#img/session04/bbc-plots.PNG"/>](https://bbc.github.io/rcookbook/#left-alignright-align_text)

---

# ggplot2
</br>

</br> ggplot2 is part of the tidyverse. </br>
</br> So, at the top of your script type:

```r
library(tidyverse)
```

---

## Project 1: </br>
</br> Let’s explore a perennial </br>
</br> challenge for the NHS:

---

# Pressures in A&E

</br> <img class="center" src="data:image/png;base64,#img/session04/demand-and-capacity.PNG"/>

.footnote[
1. [Picture 1](https://www.deviantart.com/luckymarine577/art/Animated-Ambulance-338970039) by Unknown Author is licensed under [CC BY SA NC](https://creativecommons.org/licenses/by-nc-sa/3.0/)
2. [Picture 2](https://creativecommons.org/licenses/by-nc-sa/3.0/) by Unknown Author is licensed under [CC BY SA NC](https://creativecommons.org/licenses/by-nc-sa/3.0/)
]

---

# Data: Capacity in A&E
</br>

The dataset we loaded earlier, `capacity_ae`, shows </br>
</br> changes in the capacity of A&E departments from </br>
</br> 2017 to 2018

--
</br>

</br> The object named .green[capacity_ae] is a data frame

---

## What is a data frame?

A data frame stores tabular data:

---

## tibble = data frame

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

</br>
>A tibble... is a modern reimagining of the data.frame...
>
>Tibbles are data.frames that are .blue[**lazy and surly**]: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and .blue[**complain more**] (e.g. when a variable does not exist). 
>
> This forces you to confront problems earlier, typically leading to cleaner, more expressive code.

---

## 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 <kbd> Ctrl + Enter</kbd>)

---

## Q. Do we understand the variable names?

_(and what they mean)_

---

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

.footnote[John Tukey, quoted in [R for Data Science](https://r4ds.had.co.nz/data-visualisation.html)]

---

# 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

```r
ggplot() + 
```

Inside ggplot() we can specify the dataset

```r
ggplot(data = capacity_ae)  
```

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

```r
ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))
```

---

# Choices

There are choices about the chart to use but also the details of the chart

---

# Choices

</br>

</br> 1. What shape will represent the data points?
</br> 
</br>

# .black[<svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M512 512H0V0h512v512z"></path></svg>]

---

# Choices
</br>

</br> 1. What shape will represent the data points?
</br>
</br>

# .black[<svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z"></path></svg>]

---

# Choices

.darkgrey[
1\. What shape will represent the data? .blue[geom]] 
2\. What visual (.blue[**aes**]thetic attributes do we give to the geom?)

---

# Choices

2\. What visual (.blue[**aes**]thetic attributes do we give to the geom?)

# .black[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z"></path></svg>]

---

# Choices

2\. What visual (.blue[**aes**]thetic attributes do we give to the geom?)

# <svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M377.941 169.941V216H134.059v-46.059c0-21.382-25.851-32.09-40.971-16.971L7.029 239.029c-9.373 9.373-9.373 24.568 0 33.941l86.059 86.059c15.119 15.119 40.971 4.411 40.971-16.971V296h243.882v46.059c0 21.382 25.851 32.09 40.971 16.971l86.059-86.059c9.373-9.373 9.373-24.568 0-33.941l-86.059-86.059c-15.119-15.12-40.971-4.412-40.971 16.97z"></path></svg> .black[<svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z"></path></svg>]

---

# Choices

2\. What visual (.blue[**aes**]thetic attributes do we give to the geom?)

# <svg viewBox="0 0 256 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M214.059 377.941H168V134.059h46.059c21.382 0 32.09-25.851 16.971-40.971L144.971 7.029c-9.373-9.373-24.568-9.373-33.941 0L24.971 93.088c-15.119 15.119-4.411 40.971 16.971 40.971H88v243.882H41.941c-21.382 0-32.09 25.851-16.971 40.971l86.059 86.059c9.373 9.373 24.568 9.373 33.941 0l86.059-86.059c15.12-15.119 4.412-40.971-16.97-40.971z"></path></svg> .black[<svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z"></path></svg>]

---

# Choices

2\. What visual (.blue[**aes**]thetic attributes do we give to the geom?)

# <svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;fill:green;height:2em;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z"></path></svg>

---

# A statistical graphic

Shape/colour/size <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M190.5 66.9l22.2-22.2c9.4-9.4 24.6-9.4 33.9 0L441 239c9.4 9.4 9.4 24.6 0 33.9L246.6 467.3c-9.4 9.4-24.6 9.4-33.9 0l-22.2-22.2c-9.5-9.5-9.3-25 .4-34.3L311.4 296H24c-13.3 0-24-10.7-24-24v-32c0-13.3 10.7-24 24-24h287.4L190.9 101.2c-9.8-9.3-10-24.8-.4-34.3z"></path></svg> geom all default

```r
ggplot(data = capacity_ae) + 
* geom_point(aes(x = dcubicles, y = dwait))
```

---

# Functions ()

ggplot(), geom_point(), and aes() are functions </br>

</br> Running a function does something </br> 
</br> Functions are given zero or more inputs (arguments) </br> 
</br> Arguments of a function are separated by commas

---

# Functions ()

</br> You can explicitly name arguments; </br>

```r
ggplot(data = capacity_ae) +
```

</br> Or not: </br>

```r
ggplot(capacity_ae) +
```

---

# Functions ()

</br> Other arguments like axes x and y are in a particular order; </br>

```r
ggplot(data = capacity_ae) + 
* geom_point(aes(x = dcubicles, y = dwait))
```

</br> It is possible to write it like:

```r
ggplot(data = capacity_ae) + 
* geom_point(aes(y = dwait, x = dcubicles))
```

But could be confusing.
---

# Functions ()

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

.pull-right[
.darkgrey[ggplot(.blue[**data = capacity_ae**]) + 
</br>
geom_point(.blue[**aes(x = dcubicles, y = dwait)**])]
]

And given **aes()** two named arguments </br>

</br> Unspecified (yet required) arguments will often revert to .green[default values]

---

# Shorthand

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

and

```r
ggplot(capacity_ae) + 
* geom_point(aes(dcubicles, dwait))
```

---

# geoms

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

</br> <img class="center" src="data:image/png;base64,#img/session04/geoms.PNG" width="80%"/>

---

# Layering geoms

We can display more than one geom in a plot:

.blue[<svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z"></path></svg>] to add a layer

</br>

ggplot(data = capacity_ae) .blue[<svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z"></path></svg>] 
</br> .blue[geom_point](aes(x = dcubicles, y = dwait)) .blue[<svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z"></path></svg>] 
</br> .blue[geom_smooth](aes(x = dcubicles, y = dwait))

</br>

# Your turn

This is our current plot:

```r
ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait))
```

</br> Add a geom_smooth layer (to help identify patterns)

</br> Hint: Don't forget the .blue[+] and aes() values in the new layer

---

# Your turn - Answer

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

---

![](data:image/png;base64,#04-workshop_ggplot2_files/figure-html/unnamed-chunk-12-1.png)

---

# One more thing

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

```r
ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  geom_smooth(aes(x = dcubicles, y = dwait),
*             method = "lm")
```

---

![](data:image/png;base64,#04-workshop_ggplot2_files/figure-html/unnamed-chunk-13-1.png)

---

# What is happening here?

![](data:image/png;base64,#04-workshop_ggplot2_files/figure-html/plot-outliers-1.png)

---

# Hypothesis

</br> The two sites have seen staffing increases

</br> We can map point .blue[colour] (aesthetic attribute) to the staff_increase variable to find out

</br> We will add colour to the chart depending on the value of staff_increase (TRUE or FALSE, 1 or 0)

---

# Adding another dimension

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

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

</br> 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():

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

</br> This works too because the colour is generically applied:

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

---

# Important distinction

Or apply a size globally:

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

---

# 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):

```r
ggplot(data = capacity_ae) +
* geom_point(aes(dcubicles, dwait)) +
* geom_smooth(aes(dcubicles, dwait))
```

</br> Move the aes to the "global":

```r
ggplot(data = capacity_ae, aes(dcubicles, dwait)) +
* geom_point() +
* geom_smooth()
```

---

# Small multiples magic

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

```r
ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
* facet_wrap(~ staff_increase)
```

---

# Small multiples

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

```r
ggplot(data = capacity_ae) + 
  geom_point(aes(x = dcubicles, y = dwait)) +
  facet_wrap(~ staff_increase,
*            ncol = 1)
```
---

# Demonstrating geom charts

(note: these are simple,
</br> unpolished graphics)

---

# Q. How are "wait" values distributed?

## Histogram

```r
ggplot(data = capacity_ae) + 
  geom_histogram(aes(dwait))
```

---

# Q. How are “wait” values distributed?

## Histogram

```r
ggplot(data = capacity_ae) + 
  geom_histogram(aes(dwait),
                 binwidth = 10)
```

</br> With "bins" set so more uniformed in spread:

---

# Q. Number of attendances by site?

## Bar plot

```r
ggplot(data = capacity_ae) +
  geom_col(aes(x = site,
               y = attendance2018))
```

</br> Reorder site __by__ attendances

```r
ggplot(data = capacity_ae) +
* geom_col(aes(x = reorder(site, attendance2018),
               y = attendance2018))
```

---

# Q. Number of attendances by site?

## Boxplot

```r
ggplot(data = capacity_ae) +
  geom_boxplot(aes(staff_increase, dwait))
```

### Plot labels

Can be applied to all types of charts:

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

---

# To save a plot

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

---

# To save a plot

```r
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)
```

</br> 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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