Types of visualisations

.title[
# Types of visualisations
]
.subtitle[
## <br><br> Introduction to Data Science
]
.author[
### University of Edinburgh
]
.date[
### <br> 2024/2025
]

---

# Understanding your data

* There are many types of data visualisation, each designed with a particular purpose.
* To determine which visualisation style is appropriate, consider:

1. What are the properties of variables?
  2. How many variables to include in the data visualisation?
  3. What aspect of the data do you want to explore, investigate and communicate?

---

## Types of variables

- **Quantitative variables** are variables whose data are numerical values.
  - **Discrete variables** describes count data, typically non-negative integer values. <br />
    &emsp; Eg, Number of IDS students or number of election votes.
  - **Continuous variables** are typically real-valued measurements, rounded to a specified number of decimal places or significant figures. <br />
    &emsp; Eg, Penguin bill length, body mass and flipper length.

- **Qualitative variable** are variables that are descriptive.
  - **Categorical (factor) variables** usually identify one option from a small set of cases. <br />
    &emsp; Eg, Penguin breeds (Adelie, Chinstrap or Gentoo).
  - **Ordinal variables** are categorical variables that have a natural ordering. <br />
    &emsp; Degree grades: First, upper-second, lower-second and third.

---

## Number of variables involved

How many variables do you want to use in your data visualisation?

- One variable (Univariate data analysis)
  
  - What is the _location_, _spread_ and _shape_ of the data?
  - Types of visualisations:**histogram**, **box plot**, **bar chart**, etc.
- Two variables (Bivariate data analysis)
  - Investigating the relationship between two variables
  - Positive/negative correlation? Linear or non-linear relationship?
  - Types of visualisations: **scatter plot**, **box plots**, **segmented bar plot**, etc.
- Three or more variables (Multivariate data analysis)
  - Investigating the relationship between many variables simultaneously.
  - Does the structure of the relationship between two variables change depending on the value of a third?
  - Use of **colour**, **style** and **faceting** - be creative!
  - YouTube: Hans Rosling, The Joy of Stats, BBC - [<svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z"/></svg>](https://www.youtube.com/watch?v=jbkSRLYSojo)

---

## ggplot2 package

.pull-left-wide[ 
- **ggplot2** is tidyverse's data visualization package 
- All visualisations begin with the `ggplot()` command
- The plot is then constructed by _adding_ (`+`) layers
]
.pull-right-narrow[
<img src="img/ggplot2-logo.png" width="50%" />
]

``` r
ggplot(data = [dataset],                                         # Data
       mapping = aes(x = [x-variable], y = [y-variable])) +      # Aesthetics
   geom_[*]() +                                                  # Geometries
   [other options]                                               # ...
```

- Today we will examine many of the commonly used features.
- More information can be found on the [ggplot2 cheat sheet](https://www.rstudio.com/resources/cheatsheets/)

---

## Motivating Data Set: Bats 🦇

* Extracted from the [Atlantic Mammal Traits](https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.2106) survey on animal morphology.
* Data contains information about 4334 Neotropical fruit bats (_Aribeus_) from South America between 2010-2017.

``` r
bat_data <- read_csv("data/bats.csv")
glimpse(bat_data)
```

```
## Rows: 4,334
## Columns: 6
## $ species   <chr> "Artibeus cinereus", "Artibeus cinereus", "Artibeus cinereus…
## $ body_mass <dbl> 12, 13, 11, 8, 13, 19, 12, 16, 11, 15, 18, 11, 11, 12, 14, 1…
## $ forearm   <dbl> 41.0, 38.6, 39.1, 39.2, 39.0, 43.0, 40.0, 41.0, 41.0, 40.5, …
## $ age       <chr> "adult", "adult", "adult", "adult", "adult", "adult", "juven…
## $ sex       <chr> "male", "female", "male", "male", "male", "female", "male", …
## $ year      <dbl> 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, …
```

---

## Data Dictionary

<br>

| Variable    | Description | Type |
|:------------|:------------|:-----|
| `species`   | Name of bat species | Categorical (5 cases) |
| `body_mass` | Body mass (in grams)| Numerical, continuous |
| `forearm`   | Forearm length (in mm) | Numerical, continuous |
| `age`       | Adult or Juvenile | Categorical (2 cases) |
| `sex`       | Female or Male | Categorical (2 cases) |
| `year`      | Year of measurement | Numerical, discrete |

---

# Visualising numerical data

---

# Histogram - `geom_histogram()`

---

## Histogram

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram()
```

```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

<img src="w04-L08_files/figure-html/unnamed-chunk-3-1.png" width="50%" style="display: block; margin: auto;" />
* Message specifies the use of default/assumed values

---

## Number of bins

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
* geom_histogram(bins = 30)
```
]
]

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
* geom_histogram(bins = 80)
```
]

]

.panel[.panel-name[Fewer bins]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-6-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
* geom_histogram(bins = 10)
```
]
]
]

---

## Customizing options

.panel[.panel-name[Labels]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-7-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(bins = 30) +
* labs(
*   x = "Body mass (in grams)",
*   y = "Frequency",
*   title = "Body mass of fruit bats",
*   subtitle = "(2010-2017)"
* )
```
]
]

.panel[.panel-name[Theme]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-8-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(bins = 30) +   
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
  ) +
* theme_bw()
```

* See [here](https://ggplot2.tidyverse.org/reference/ggtheme.html) for more theme defaults.

]
]

.panel[.panel-name[Colour]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-9-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(
    bins = 30,
*   fill = "darkcyan",
*   colour = "darkblue"
    ) +     
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
    ) +
  theme_bw()
```

]
]

]

---

# Density plot - `geom_density()`

---

## Density plot

]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
* geom_density()
```

Construction:
<img src="w04-L08_files/figure-html/eg_den-1.png" style="display: block; margin: auto;" />

]

---

## Customizing options

.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-11-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) + 
  geom_density(
    fill = "darkcyan"
    ) +   
  labs(
    x = "Body mass (in grams)",
    y = "Density",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
    ) +
  theme_bw()
```
]

---

# Box plot - `geom_boxplot()`

---

## Box plot

.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-12-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) + 
* geom_boxplot( ) +
  labs(
    x = "Body mass (in grams)",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
    ) +
  theme_bw()
```

Construction:
<img src="w04-L08_files/figure-html/boxplot_description-1.png" style="display: block; margin: auto;" />

]

---

## Customizing options

.panel[.panel-name[Orientation]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-13-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
*      mapping = aes(y = body_mass)) +
  geom_boxplot( ) +   
  labs(
    y = "Body mass (in grams)",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
    ) +
  theme_bw()
```
]
]

.panel[.panel-name[Whisker Length]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-14-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(y = body_mass)) + 
* geom_boxplot(coef = 1) + # default: 1.5
  labs(
    y = "Body mass (in grams)",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)"
    ) +
  theme_bw()
```

]
]

.panel[.panel-name[More Styles]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-15-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(y = body_mass)) + 
  geom_boxplot(coef = 1,
*   fill = "darkcyan",
*   colour = "darkblue",
*   lwd = 1.5,          # line width scaling
*   outlier.shape = 4,  # see below
*   outlier.colour = "darkred") +
  labs(
    y = "Body mass (in grams)",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)" ) +
  theme_bw()
```
<img src="img/point_shapes.PNG" width="50%" style="display: block; margin: auto;" />

]
]

]

---

# Adding a categorical variable...

---

## ...via aesthetics

.panelset[
.panel[.panel-name[Histogram]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-17-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
         x = body_mass,
*        fill = species
         )) +
  geom_histogram(
    bins = 30,
*   position = "identity",
*   alpha = 0.5  # opacity
    ) +
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats,",
    subtitle = "by species" ) 
```
]
]

.panel[.panel-name[Density]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-18-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
         x = body_mass,
*        fill = species
         )) +
  geom_density(
*   alpha = 0.5
    ) +
  labs(
    x = "Body mass (in grams)",
    y = "Density",
    title = "Body mass of fruit bats,",
    subtitle = "by species" ) 
```
]
]

.panel[.panel-name[Box plot]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-19-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
         x = body_mass, 
*        y = species
         )) +
  geom_boxplot() +
  labs(
    x = "Body mass (in grams)",
    y = "Species",
    title = "Body mass of fruit bats",
    subtitle = "(2010-2017)" ) 
```
]
]

]

---

## ...via faceting

.panelset[
.panel[.panel-name[Wrap (same axes)]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-20-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(bins = 30) +
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats,",
    subtitle = "by species" )  +
* facet_wrap(~ species,
*            ncol = 3)
```
]
]

.panel[.panel-name[Wrap (free axes)]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-21-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(bins = 30) +
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats,",
    subtitle = "by species" )  +
  facet_wrap(~ species, 
             ncol = 3,
*            scales = "free")
```
]
]

.panel[.panel-name[Grid]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-22-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(x = body_mass)) +
  geom_histogram(bins = 30) +
  labs(
    x = "Body mass (in grams)",
    y = "Frequency",
    title = "Body mass of fruit bats,",
    subtitle = "by age and sex" )  +
* facet_grid(age ~ sex,
*            scale = "free")
```
]
]

]

---

# Relationships numerical variables - `geom_point()` and `geom_bin_2d()`

---

## Scatterplot

.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-23-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
*        x = forearm,
*        y = body_mass
         )) +
* geom_point() +
  labs(
    x = "Forearm length (in mm)",
    y = "Body mass (in grams)",
    title = "Forearm & Mass Relationship,",
    subtitle = "(2010-2017)" )  
```
]

---

## Customise

.panel[.panel-name[Best fit line]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-24-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
         x = forearm,   
         y = body_mass
         )) +
  geom_point() +  
* geom_smooth(method = lm) +
  labs(
    x = "Forearm length (in mm)",
    y = "Body mass (in grams)",
    title = "Forearm & Mass Relationship",
    subtitle = "(2010-2017)" )  
```
]
]

.panel[.panel-name[Add variables via style]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-25-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes(
         x = forearm,   
         y = body_mass,
*        colour = species,
*        shape = age
         )) +
  geom_point() +  
  labs(
    x = "Forearm length (in mm)",
    y = "Body mass (in grams)",
    title = "Forearm & Mass Relationship,",
    subtitle = "by species and age" )  
```
]
]

.panel[.panel-name[Zoom]
.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-26-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes( 
         x = forearm, y = body_mass, 
         colour = species, shape = age)) +
  geom_point() +  
  labs(
    x = "Forearm length (in mm)",
    y = "Body mass (in grams)",
    title = "Forearm & Mass Relationship,",
    subtitle = "by bat species and age" )  +
* coord_cartesian(
*   xlim = c(30, 50),
*   ylim = c(0, 30))
```
]]

]

---

## 2D histogram colour map

.pull-left[
<img src="w04-L08_files/figure-html/unnamed-chunk-27-1.png" width="80%" />
]
.pull-right[

``` r
ggplot(data = bat_data, 
       mapping = aes( 
         x = forearm, 
         y = body_mass
         )) +
* geom_bin_2d() +
  labs(
    x = "Forearm length (in mm)",
    y = "Body mass (in grams)",
    title = "Forearm & Mass Relationship,",
    subtitle = "(2010-2017)" )  
```
]

---

# Bar plot - `geom_bar()`

Visualizing qualitative/categorical data

---

## Bar plot

]
.pull-right[

``` r
ggplot(bat_data, aes(
*   y = species  # y => Horizontal
    )) +
* geom_bar() +
  labs(
    x = "Count",
    y = "Species",
    title = "Bar plot of bat species",
    subtitle = "(2010-2017)")
```
]
---

## Bar plot variations

``` r
ggplot(bat_data, aes(
    y = species,  
*   fill = age
    )) +
  geom_bar() +  
  labs(
    x = "Count",
    y = "Species",
    title = "Bar plot of bat species,",
    subtitle = "segmented by age")
```
]]

``` r
ggplot(bat_data, aes(
    y = species,  
    fill = age      
    )) +
* geom_bar(position = "fill") +
  labs(
    x = "Count",
    y = "Species",
    title = "Age proportion per bat species",
    subtitle = "(2010-2017)")
```
]
]

``` r
ggplot(bat_data, aes(
*   x = "",
*   fill = species
    )) +
* geom_bar(position = "fill") +
* coord_polar("y", start = 0) +
* theme_void() +
  labs(
    title = "Bat sepcies pie chart",
    subtitle = "(2010-2017)")
```
]
]

]

---

## Data visualisation gallery

.pull-left[
* `ggplot2` is used to create a wide variety of data visualisation styles.
* It has many customisation tools to support your communication.
* Use `ggplot2` manual pages (and online searches) to support your creation.

* **But**, excessive customisation can be distracting.
* Remember the 4 respects:
  1. Respect the **people**
  2. Respect the **data**
  3. Respect the **mathematics**
  4. Respect the **computers**

]
.pull-right[

<br>

<img src="img/ggplot_plots_gallary.png" width="100%" style="display: block; margin: auto;" />
]