Introduction to ggplot2

# Introduction to ggplot2

### *The 100 Days of Data.trek Challenge*

![](https://ivado.ca/wp-content/themes/ivadooo/assets/svg/siteLogo.svg)

January 26, 2022 (updated: 2022-01-26)

## Philippe Massicotte

---

<br>

<b>Research assistant at Takuvik (Laval University)</b><br>

- Remote sensing, modelling, data science, data visualization<br>

<br>

</p>

---

# Outline

- Why data visualization?

- `ggplot2` basic plots
    - Histograms and bar plots
    - Points and lines plots
    - Boxplots

- `ggplot2` aesthetics and appearance
    - Color, size
    - Axes and titles
    - Faceting (small multiples)
    - Overview of the `theme()` function

- Combining plots with `patchwork`

- Exporting `ggplot2` graphics

---

# Data visualization

<center>
<figure>
  <img src="https://blog.rstudio.com/2019/11/18/artist-in-residence/horst-eco-r4ds.png" width="800">
<figcaption>Artwork by <a href="https://twitter.com/allison_horst?s=20">@allison_horst</a></figcaption>
</figure>
</center>

---

# Data visualization

- Important aspect of data sciences:
    - **Communicate information clearly and efficiently to the community.**

- Powerful tool to discover patterns in the data:
    - It makes complex data more accessible: **reveal the data**.

- Bad graphics can be a reason for paper rejection:
    - **Readers should rapidly understand the message you are trying to convey.**

- A picture is worth a thousand words:
    - **Always, always, always plot your data!**
    - When possible, replace tables with figures.

---

# Visualization to convey your message

It is difficult to grasp the information contained in a table.

.pull-left[
<table class="table" style="font-size: 18px; margin-left: auto; margin-right: auto;">
<caption style="font-size: initial !important;">Sea ice extent in the Arctic between 1978 and 2021.</caption>
 <thead>
  <tr>
   <th style="text-align:left;"> date </th>
   <th style="text-align:right;"> extent </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-10-26 </td>
   <td style="text-align:right;width: 3in; "> 10.231 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-10-28 </td>
   <td style="text-align:right;width: 3in; "> 10.420 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-10-30 </td>
   <td style="text-align:right;width: 3in; "> 10.557 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-01 </td>
   <td style="text-align:right;width: 3in; "> 10.670 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-03 </td>
   <td style="text-align:right;width: 3in; "> 10.777 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-05 </td>
   <td style="text-align:right;width: 3in; "> 10.968 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-07 </td>
   <td style="text-align:right;width: 3in; "> 11.080 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-09 </td>
   <td style="text-align:right;width: 3in; "> 11.189 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-11 </td>
   <td style="text-align:right;width: 3in; "> 11.314 </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 3in; "> 1978-11-13 </td>
   <td style="text-align:right;width: 3in; "> 11.460 </td>
  </tr>
</tbody>
</table>
]

.pull-right[
<center>
    <img src="img/arctic_seaice_extent.png" alt="Map showing the Arctic sea ice extent in October 2021." height="400"/>
</center>
]

---

# Visualization to convey your message

Graphics, on the other hand, help to convey your message.

---

# Visualization to convey your message

The same data but presented differently.

Interesting reading: [Arctic Sea Ice Extent Second Highest in 18 Years at the end of 2021](https://www.severe-weather.eu/global-weather/arctic-sea-ice-second-highest-18-years-end-2021-rrc/)

---

# Datasaurus Dozen

**These 13 datasets have the same statistical properties (*mean*, *variance*, *correlation*).** However, they look quite different!

---

# Different types of visualizations

There are many types of visualization to choose from to present data. The decision depends on the data itself and how you want to present it to your audience.

<center>
    <img src="img/chart_types.png" alt="xxx" height="375"/>
    <figcaption>
    <a href="https://www.kdnuggets.com/2019/03/how-choose-right-chart-type.html">How to Choose the Right Chart Type</a>
    </figcaption>
</center>

---

<center>
<figure>
  <img src="https://github.com/allisonhorst/stats-illustrations/blob/master/rstats-artwork/ggplot2_masterpiece.png?raw=true" width="700">
<figcaption>Artwork by <a href="https://twitter.com/allison_horst?s=20">@allison_horst</a></figcaption>
</figure>
</center>

---

# ggplot2

`ggplot2` is a system for declaratively creating graphics, based on [The Grammar of Graphics](https://amzn.to/2ef1eWp).

> Presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems.

<center>
<figure>
  <img src="img/grammar_of_graphics.png" width="600">
<figcaption>Source: <a href="https://bit.ly/3JwHavs">Thomas de Beus</a></figcaption>
</figure>
</center>

---

# ggplot2

`ggplot2` is not part of base R, so it needs to be installed.

.left-column[
<center>
<figure>
  <img src="https://raw.githubusercontent.com/tidyverse/ggplot2/master/man/figures/logo.png" width="300">
</figure>
</center>
]

```r
install.packages("ggplot2")
```

After the installation, you will have to load it.

```r
library(ggplot2)
```
]

---

# ggplot2

- The learning curve can be difficult, but **the time investment will eventually pay off.**
]

.column-right[
<center>
<figure>
  <img src="https://github.com/allisonhorst/stats-illustrations/blob/master/rstats-artwork/r_first_then.png?raw=true" height="500">
    <figcaption>
    Artwork by <a href="https://twitter.com/allison_horst?s=20">@allison_horst</a>
    </figcaption>
</figure>
</center>
]

---

# TidyTuesday

TidyTuesday is a weekly challenge where people use (mostly) `ggplot2` to explore a new dataset.

> A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with `ggplot2`, `tidyr`, `dplyr`, and other tools in the `tidyverse` ecosystem.

<center>
    <img src="https://github.com/rfordatascience/tidytuesday/blob/master/static/tt_logo.png?raw=true" alt="drawing" width="450"/>
    <figcaption>
    <a href="https://twitter.com/search?q=%23TidyTuesday&src=hashtag_click">#TidyTuesday on <i class="fab fa-twitter"></i></a>
    </figcaption>
</center>

---

# TidyTuesday

As already said, `ggplot2` can be intimidating at first. However, with some practice, you will be able to make stunning graphics.

<center>
<div class="row">
    <div class="column">
        <img src="https://github.com/loreabad6/TidyTuesday/blob/master/plot/2022_week_03.png?raw=true"
            alt="Who makes the best chocolate with Ecuadorian beans?" style="width:120%">
        <figcaption>
            Graphic by <a href=https://twitter.com/loreabad6>@loreabad6</a>
        </figcaption>
    </div>
    <div class="column">
        <img src="https://github.com/jkaupp/tidytuesdays/blob/437b47ad14bd3d6d66dca41e0a4a84c9d96716fb/2020/week44/tw44_plot.png?raw=true"
            alt="Wind power generation in the Maritimes" style="width:80%">
        <figcaption>
            Graphic by <a href=https://twitter.com/jakekaupp">@jakekaupp</a>
        </figcaption>
    </div>
    <div class="column">
        <img src="https://github.com/gkaramanis/aRtist/blob/main/genuary/2021/2021-3/2021-3.png?raw=true" alt="Person's face ploted using ggplot2"
            style="width:80%">
        <figcaption>
            Graphic by <a href=https://twitter.com/geokaramanis?lang=en">@geokaramanis</a>
        </figcaption>
    </div>
</div>
</center>

**Most people participating in the TidyTuesday challenges share their code on Github. It is a great way to learn more advanced techniques!**

---

# Let's start with ggplot2!

---

# The Palmer Penguins: the new iris

Data were collected and made available by [Dr. Kristen Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php) and the [Palmer Station, Antarctica LTER](https://pal.lternet.edu/), a member of the [Long Term Ecological Research Network](https://lternet.edu/).

<center>
<figure>
  <img src="https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png" width="600">
</figure>
<figcaption>
    Artwork by <a href="https://twitter.com/allison_horst?s=20">@allison_horst</a>
</figcaption>
</center>

---

# The Palmer Penguins

The dataset contain data for **344 penguins** from **3 different species** (<pu><b>Chinstrap</b></pu>, <cy><b>Gentoo</b></cy> and <or><b>Adélie</b></or>) collected from **3 islands** in the Palmer Archipelago, Antarctica.

<small>
.pull-left[

- `species`: penguin species (*Adélie*, *Chinstrap* and *Gentoo*)

- `island`: island in Palmer Archipelago, Antarctica (*Biscoe*, *Dream* and *Torgersen*)

- `bill_length_mm`: bill length (millimeters)

- `bill_depth_mm`: bill depth (millimeters)

- `flipper_length_mm`: flipper length (millimeters)

- `body_mass_g`: body mass (grams)

- `sex`: penguin sex (*female*, *male*)

- `year`: year of the study (2007, 2008 and 2009)

]
</small>

.pull-right[
<center>
<figure>
  <img src="https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/culmen_depth.png" width="700">
</figure>
</center>
]

---

# Installing the data

.left-column[
<center>
<figure>
  <img src="https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/logo.png" width="300">
</figure>
</center>
]

```r
# install.packages("remotes")
remotes::install_github("allisonhorst/palmerpenguins")
```
]

---

# The palmer penguins data

<small>

```r
library(palmerpenguins)
penguins
```

```
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
```
</small>

---

# Understanding the ggplot2 syntax

A `ggplot2` plot is built layer by layer by using the `+` operator.

<center>
    <img src="img/ggplot2_structure.png" alt="drawing" width="800"/>
</center>
<figcaption>
    The basic structure of a ggplot2 plot.
</figcaption>
        
---

# The geoms

`geoms` is the abbreviation for *geometric objects* which are used to specify which type of graphic you want to produce (**boxplot**, **barplot**, **scatterplot**, **histogram**, ...). All `ggplot2` geoms start with the `geom_` prefix.

<small>

```
##  [1] "geom_abline"            "geom_area"              "geom_bar"              
##  [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
##  [7] "geom_boxplot"           "geom_col"               "geom_contour"          
## [10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
## [13] "geom_curve"             "geom_density"           "geom_density_2d"       
## [16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
## [19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
## [22] "geom_freqpoly"          "geom_function"          "geom_hex"              
## [25] "geom_histogram"         "geom_hline"             "geom_jitter"           
## [28] "geom_label"             "geom_line"              "geom_linerange"        
## [31] "geom_map"               "geom_path"              "geom_point"            
## [34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
## [37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
## [40] "geom_rect"              "geom_ribbon"            "geom_rug"              
## [43] "geom_segment"           "geom_sf"                "geom_sf_label"         
## [46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
## [49] "geom_step"              "geom_text"              "geom_tile"             
## [52] "geom_violin"            "geom_vline"
```
</small>

---

# One variable graphics

---

# One variable graphics

In this section, we are going to see the two main types of one variable graphics:

| **Graphic type** | **Geom**           | **Description**                          |
| ---------------- | ------------------ | ---------------------------------------- |
| Histogram        | `geom_histogram()` | Produces histograms for continuous data. |
| Barplot          | `geom_bar()`       | Produces histograms for discrete data.   |

---

```r
*ggplot(
* data = penguins,
* mapping = aes(x = body_mass_g)
*) 
```
]
 
.panel2-histogram1-auto[
<img src="index_files/figure-html/histogram1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Histogram
.panel1-histogram1-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(x = body_mass_g)
) +
* geom_histogram()
```
]
 
.panel2-histogram1-auto[
<img src="index_files/figure-html/histogram1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = penguins,
* mapping = aes(x = island)
*) 
```
]
 
.panel2-barplot1-auto[
<img src="index_files/figure-html/barplot1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Barplot
.panel1-barplot1-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(x = island)
) +
* geom_bar()
```
]
 
.panel2-barplot1-auto[
<img src="index_files/figure-html/barplot1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---

# Two variables graphics

---

# Two variables graphics

There are several types of charts with two variables. Here are the most used.

| **Graphic type** | **Geom**         | **Description**                                               |
| ---------------- | ---------------- | ------------------------------------------------------------- |
| Barplot          | `geom_col()`     | Produces bars with heights that represent values in the data. |
| Scatter plot     | `geom_point()`   | Produces scatter plot between `x` and `y`.                    |
| Line plot        | `geom_line()`    | Produces line plot between `x` and `y`.                       |
| Boxplot          | `geom_boxplot()` | Boxplot between `x` and `y`.                                  |

---

# Barplot

We used `geom_bar()` to visualize the number/count of cases for each value of `x`. If we want to represent values in the data (`y`), we can use `geom_col()`. For this example, I will use the `starwars` data that is included in the `dplyr` package. **This graph shows the top 20 tallest characters (in cm).**

```r
ggplot(
  data = sw,
  mapping = aes(
*   x = name,
*   y = height
  )
) +
  geom_col()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-11-1.svg" style="display: block; margin: auto;" />
]

**What could be done to improve this graph?**

---

# Barplot

In the previous graph, it was difficult to read the names on the `x` axis. **A better way to present the data is by swapping the `x` and `y` axes.**

```r
ggplot(
  data = sw,
  mapping = aes(
*   x = height,
*   y = name
  )
) +
  geom_col()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-12-1.svg" style="display: block; margin: auto;" />
]

**Can we improve furthermore?**

---

# Ordering things

We have made some improvements in the previous graph by swapping the axes. However, **it would be even better if the characters were sorted by their height** to make the plot easier to understand.

One way to do it is with `forcats::fct_reorder()`. Note that the `forcats` library is not part of base R and must be installed separately.

```r
ggplot(
  data = sw,
  mapping = aes(
    x = height,
*   y = forcats::fct_reorder(name, height)
  )
) +
  geom_col()
```
</small>
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-13-1.svg" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* penguins,
* aes(
*   x = bill_length_mm,
*   y = body_mass_g
* )
*) 
```
]
 
.panel2-scatterplot1-auto[
<img src="index_files/figure-html/scatterplot1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Scatter plot
.panel1-scatterplot1-auto[

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
* geom_point()
```
]
 
.panel2-scatterplot1-auto[
<img src="index_files/figure-html/scatterplot1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---

## Adding a regression line

Use `geom_smooth(method = "lm")` to add a linear regression line to your data.

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
  geom_point()+
* geom_smooth(method = "lm")
```
</small>
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-14-1.svg" style="display: block; margin: auto;" />
]

---

## Adding a smoothing line

If the argument `method` is not specified, a `loess` or a `gam` model will be used (depending on the number of points to be fitted).

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
  geom_point()+
* geom_smooth()
```
</small>
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-15-1.svg" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* penguins,
* aes(
*   x = bill_length_mm,
*   y = body_mass_g
* )
*) 
```
]
 
.panel2-lineplot1-auto[
<img src="index_files/figure-html/lineplot1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Line plot
.panel1-lineplot1-auto[

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
* geom_line()
```
]
 
.panel2-lineplot1-auto[
<img src="index_files/figure-html/lineplot1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---

# Boxplot

> In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles (Wikipedia).

To make a boxplot, we need to have a **discrete/categorical** variable on `x` and a **continuous** variable on `y`.

---

```r
*ggplot(
* data = penguins,
* mapping = aes(
*   x = island,
*   y = body_mass_g
* )
*) 
```
]
 
.panel2-boxplot1-auto[
<img src="index_files/figure-html/boxplot1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Boxplot
.panel1-boxplot1-auto[

```r
ggplot(
  data = penguins,
  mapping = aes(
    x = island,
    y = body_mass_g
  )
) +
* geom_boxplot()
```
]
 
.panel2-boxplot1-auto[
<img src="index_files/figure-html/boxplot1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---

# Boxplot

We can reorder the `island` variable based on the `body_mass_g` variable to make the graphic more appealing with `forcats::fct_reorder()`.

```r
ggplot(
  data = penguins,
  mapping = aes(
*   x = forcats::fct_reorder(
*     island,
*     body_mass_g,
*     na.rm = TRUE
*   ),
    y = body_mass_g 
  )
) +
  geom_boxplot()
```
</small>
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-16-1.svg" style="display: block; margin: auto;" />
]

Note the use of `na.rm = TRUE` in the `fct_reorder()` function.

---

# Geom aesthetics

---

# Geom aesthetics

Aesthetics such as *color*, *shape*, *size* of the displayed geoms can be controlled inside the `geom_()` functions. For example, we can change the `color` and the `size` of the point in the `geom_point()` function.

```r
geom_point(color = "red", size = 5) # All points will be red
```

---

# Working with colours

If we want to set a colour **based on a variable**, we have to use the aesthetic: `aes(colour = variable)`.

```r
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
* geom_point(aes(color = species))
```

---

count: false
 
# Setting colors manually 
 We can also create our own palette of colors using *scale_color_manual()*.
.panel1-manual_colors1-auto[

```r
*ggplot(
* penguins,
* aes(
*   x = bill_length_mm,
*   y = body_mass_g
* )
*) 
```
]
 
.panel2-manual_colors1-auto[
<img src="index_files/figure-html/manual_colors1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Setting colors manually 
 We can also create our own palette of colors using *scale_color_manual()*.
.panel1-manual_colors1-auto[

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
* geom_point(aes(color = species))
```
]
 
.panel2-manual_colors1-auto[
<img src="index_files/figure-html/manual_colors1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Setting colors manually 
 We can also create our own palette of colors using *scale_color_manual()*.
.panel1-manual_colors1-auto[

```r
ggplot(
  penguins,
  aes(
    x = bill_length_mm,
    y = body_mass_g
  )
) +
  geom_point(aes(color = species)) +
* scale_color_manual(
*   breaks = c(
*     "Adelie",
*     "Chinstrap",
*     "Gentoo"
*   ),
*   values = c(
*     "darkorange",
*     "purple",
*     "#008b8b"
*   )
* )
```
]
 
.panel2-manual_colors1-auto[
<img src="index_files/figure-html/manual_colors1_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---

background-image: url(https://github.com/EmilHvitfeldt/paletteer/blob/master/man/figures/logo.png?raw=true)
background-size: 90px
background-position: 90% 8%

# Color palettes for ggplot2

Many different R packages provide colour palettes.

`paletteer` is a comprehensive collection of colour palettes in R created by [@Emil_Hvitfeldt](https://twitter.com/Emil_Hvitfeldt). I have made a [paletteer gallery](https://github.com/PMassicotte/paletteer_gallery) to help me navigate all the palettes.

<small>

```r
*library(paletteer)

ggplot(
  data = sw,
  mapping = aes(
    x = height,
    y = forcats::fct_reorder(name, height),
*   fill = height
  )
) +
  geom_col() +
* scale_fill_paletteer_c("ggthemes::Red-Gold")
```
</small>
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-20-1.svg" style="display: block; margin: auto;" />
]

---

# Types of colour scales

**Attention to the type of colour scale that is mapped to your data.**

- Use `scale_color_*()` of `scale_colour_*()` for the **color** of the geom.

- Use `scale_fill_*()` for the **fill color** of the geom.

<br>

<small>

```r
ggplot(
  data = sw,
  mapping = aes(
    x = height,
    y = forcats::fct_reorder(name, height),
*   fill = height,
*   color = eye_color
  )
) +
  geom_col(size = 1)
```
</small>
]

<img src="index_files/figure-html/unnamed-chunk-21-1.svg" height="80%" style="display: block; margin: auto;" />
]

---

# Working with size

As we did for the colours, the size of the geom (ex.: points) can be based on a particular variable.

```r
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
* geom_point(aes(size = body_mass_g))
```

---

# Continuous vs discrete mapping

**Attention to the type of data you are mapping to a colour or shape scale.**

If the mapped variable is **continuous**, the generated guide on the legend will be also **continuous**.

```r
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
* geom_point(aes(color = year))
```

---

# Continuous vs discrete mapping

**Attention to the type of data you are mapping to a colour or shape scale.**

If the mapped variable is **discrete**, the generated guide on the legend will be also **discrete**. You can convert a continuous variable into a factor with the `factor()` function.

```r
ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
* geom_point(aes(color = factor(year)))
```

---

# Axes and titles

---

# Axes and titles

There are many ways to change the titles of the graphic and the axes. Here we are going to use the `labs()` function.

These are the main parameters:

- `title`: Main title of the graph

- `x`, `y`: Titles for the axes

- `subtitle`: Subtitle title of the graph (default: under the main title)

- `caption`: Caption of the graph (default: bottom right of the graph)

---

```r
*ggplot(
* data = penguins,
* aes(
*   x = flipper_length_mm,
*   y = bill_length_mm,
*   color = species
* )
*) 
```
]
 
.panel2-axes_and_titles1-auto[
<img src="index_files/figure-html/axes_and_titles1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Axes and titles
.panel1-axes_and_titles1-auto[

```r
ggplot(
  data = penguins,
  aes(
    x = flipper_length_mm,
    y = bill_length_mm,
    color = species
  )
) +
* geom_point()
```
]
 
.panel2-axes_and_titles1-auto[
<img src="index_files/figure-html/axes_and_titles1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Axes and titles
.panel1-axes_and_titles1-auto[

```r
ggplot(
  data = penguins,
  aes(
    x = flipper_length_mm,
    y = bill_length_mm,
    color = species
  )
) +
  geom_point() +
* labs(
*   title = "Flipper and bill length",
*   subtitle = "Relationship between flipper and bill length",
*   caption = "Data from: palmerpenguins R package",
*   x = "Flipper length (mm)",
*   y = "Bill length (mm)",
*   color = "Penguin\nspecies"
* )
```
]
 
.panel2-axes_and_titles1-auto[
<img src="index_files/figure-html/axes_and_titles1_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = penguins,
* aes(
*   x = flipper_length_mm,
*   y = bill_length_mm,
*   color = species
* )
*) 
```
]
 
.panel2-axes_and_titles2-auto[
<img src="index_files/figure-html/axes_and_titles2_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Controling axis labels
.panel1-axes_and_titles2-auto[

```r
ggplot(
  data = penguins,
  aes(
    x = flipper_length_mm,
    y = bill_length_mm,
    color = species
  )
) +
* geom_point()
```
]
 
.panel2-axes_and_titles2-auto[
<img src="index_files/figure-html/axes_and_titles2_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Controling axis labels
.panel1-axes_and_titles2-auto[

```r
ggplot(
  data = penguins,
  aes(
    x = flipper_length_mm,
    y = bill_length_mm,
    color = species
  )
) +
  geom_point() +
* scale_x_continuous(
*   breaks = c(180, 220),
*   labels = c("abc", "def")
* )
```
]
 
.panel2-axes_and_titles2-auto[
<img src="index_files/figure-html/axes_and_titles2_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---

# Faceting (small multiples)

---

# Faceting (small multiples)

Faceting is a technique that allows displaying additional categorical variables in facets. Within `ggplot2`, there are two types of faceting: `facet_grid()` and `facet_wrap()`.

---

```r
*ggplot(
* data = drop_na(penguins, sex),
* aes(
*   x = body_mass_g,
*   y = bill_depth_mm,
*   color = sex
* )
*) 
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 1D faceting
.panel1-facet1-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
* geom_point()
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 1D faceting
.panel1-facet1-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
  geom_point() +
* facet_wrap(~species)
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 1D faceting
.panel1-facet1-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
  geom_point() +
  facet_wrap(~species) +
* facet_wrap(~species, scales = "free_x")
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_04_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 1D faceting
.panel1-facet1-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
  geom_point() +
  facet_wrap(~species) +
  facet_wrap(~species, scales = "free_x") +
* facet_wrap(~species, scales = "free_y")
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_05_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 1D faceting
.panel1-facet1-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
  geom_point() +
  facet_wrap(~species) +
  facet_wrap(~species, scales = "free_x") +
  facet_wrap(~species, scales = "free_y") +
* facet_wrap(~species, scales = "free")
```
]
 
.panel2-facet1-auto[
<img src="index_files/figure-html/facet1_auto_06_output-1.svg" style="display: block; margin: auto;" />
]

---

```r
*ggplot(
* data = drop_na(penguins, sex),
* aes(
*   x = body_mass_g,
*   y = bill_depth_mm,
*   color = sex
* )
*) 
```
]
 
.panel2-facet3-auto[
<img src="index_files/figure-html/facet3_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 2D faceting
.panel1-facet3-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
* geom_point()
```
]
 
.panel2-facet3-auto[
<img src="index_files/figure-html/facet3_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# 2D faceting
.panel1-facet3-auto[

```r
ggplot(
  data = drop_na(penguins, sex),
  aes(
    x = body_mass_g,
    y = bill_depth_mm,
    color = sex
  )
) +
  geom_point() +
* facet_grid(island ~ species)
```
]
 
.panel2-facet3-auto[
<img src="index_files/figure-html/facet3_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---

# Using ggplot2 themes

---

# ggplot2 themes

> Themes are a powerful way to customize the non-data components of your plots: i.e. titles, labels, fonts, background, gridlines, and legends.

Many components can be changed using the `theme()` function. Today we are going to see just a few of them, but feel free to have a look at `?theme` to have more information.

`theme()` can be used to modify:

- The background aesthetics (colour, grid, etc.).

- Axis titles and ticks aesthetics.

- Legend titles and positions.

- Aesthetics of plot titles (title, subtitle, caption, etc.).

- Plot margins.

---

```r
*p 
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_01_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Built in themes
.panel1-theme1-auto[

```r
p +
* theme_bw()
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_02_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Built in themes
.panel1-theme1-auto[

```r
p +
  theme_bw() +
* theme_light()
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_03_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Built in themes
.panel1-theme1-auto[

```r
p +
  theme_bw() +
  theme_light() +
* theme_gray()
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_04_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Built in themes
.panel1-theme1-auto[

```r
p +
  theme_bw() +
  theme_light() +
  theme_gray() +
* theme_dark()
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_05_output-1.svg" style="display: block; margin: auto;" />
]

---
count: false
 
# Built in themes
.panel1-theme1-auto[

```r
p +
  theme_bw() +
  theme_light() +
  theme_gray() +
  theme_dark() +
* theme_void()
```
]
 
.panel2-theme1-auto[
<img src="index_files/figure-html/theme1_auto_06_output-1.svg" style="display: block; margin: auto;" />
]

---

# External themes

- You are not limited to using `ggplot2` built-in themes.

- Here are some themes provided by the `ggthemes` and `ggpubr` packages.

```r
install.packages("ggthemes")
install.packages("ggpubr")
```

---

```r
*p 
```
]
 
.panel2-theme2-auto[
<img src="index_files/figure-html/theme2_auto_01_output-1.svg" style="display: block; margin: auto;" />
]