05b_ggplot2-intro.qmd

---
title: "Introduction to `ggplot2`"
---


## Graphics in R

The R language has extensive graphical capabilities.

Graphics in R may be created by many different methods including base graphics and more advanced plotting packages such as lattice and ggplot2.


You'll find a rich selection of graphs in R at [The R Graph Gallery](https://www.r-graph-gallery.com/)

```{r, echo=FALSE}
knitr::include_url("https://www.r-graph-gallery.com/")
```



### `ggplot2`

The ggplot2 package was created by Hadley Wickham and provides an intuitive plotting system to rapidly generate publication quality graphics.

**`ggplot2` builds on the "Grammar of Graphics"** 

#### Resources

[**Paper on the grammar of graphics as the foundation for `ggplot2`**](https://vita.had.co.nz/papers/layered-grammar.html)


**Online working draft of 3rd Edition of** [***`ggplot2`: Elegant Graphics for Data Analysis***](https://ggplot2-book.org/)

```{r, echo=FALSE}
knitr::include_url("https://ggplot2-book.org/")
```

[**`ggplot2` documentation**](https://ggplot2.tidyverse.org/index.html)

```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/index.html")
```


**`ggplot2` cheatsheet**

```{r, echo=FALSE}
knitr::include_url("assets/cheatsheets/data-visualization-2.1.pdf")
```



### `ggplot2` in practice. Plotting `mtcars`

To demonstrate the use of `ggplot2` to plot data according to the grammar of graphics, let's recreate the `mtcars` plot I just showed you.

Let's work in our `attic/development.R` script for now.

#### Initialising a plot

The first thing we need to do is initialise a new plot.

<div class="alert alert-info">

Function `ggplot()` is used to construct the initial plot object, and is [**almost always followed by `+`**](https://ggplot2.tidyverse.org/reference/gg-add.html) to add component to the plot. 

```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/reference/ggplot.html")
```


</div>

Let's load the library and create an empty plot:

```{r}
library(ggplot2)

ggplot()
```


#### Specifying the data

Next thing we need to do is specify the data.

The first argument to `ggplot()` is the data we want to use for plotting, usually a data.frame or tibble. We can pipe that in to `ggplot()`.

```{r}
mtcars |>
  ggplot()
```

#### Mapping to Aesthetics with `aes()`

Now that we've specified our data, we can start encoding our variables to visual properties of our plot.

Lets start by mapping `mpg` and `hp` to our two axes. We do that in the second argument `mapping` through function `aes()`. 

Function `aes()` is used to specify the **set of aesthetic mappings between variables in the data and visual properties** in our plot. 

```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/reference/aes.html")
```


Any aesthetics defined in `ggplot(aes())` will apply to all subsequent layers unless they are overriden within the individual layers.


##### Mapping variables to axes `x` and `y`.

In our case, we want to map variable `mpg` to aesthetic `x` (the x axis) and variable `hp` to aesthetic `y` (the y axis).

```{r, fig.height=7, message=FALSE}
mtcars |>
  ggplot(mapping = aes(x = mpg, y = hp))
```

#### Adding geometries with `geom_*()`

Currently, we only have our axes, initialised with reasonably infered scales from the data.

Following the specification of data and aesthetics, we next need to specify which geometries (or `geoms`s in ggplot) to use to present the data. The `geom` is a critical component that describes the type of plot used.

Several geoms are available in ggplot2 as separate functions:

* `geom_point()` - Scatter plots
* `geom_line()` - Line plots
* `geom_smooth()` - Fitted line plots
* `geom_bar()` - Bar plots
* `geom_boxplot()` - Boxplots
* `geom_jitter()` - Jitter to plots
* `geom_histogram()` - Histogram plots
* `geom_density()` - Density plots
* `geom_text()` - Text to plots
* `geom_errorbar()` - Errorbars to plots
* `geom_violin()` - Violin plots


```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/reference/index.html#section-layer-geoms")
```

##### Plotting a scatterplot

To visualise the relationship between the data points in our two variables, and given both are numeric, we can plot them as points on a scatterplot using `geom_point()`.

```{r, fig.height=7, message=FALSE}
mtcars |>
  ggplot(aes(x = mpg, y = hp)) +
  geom_point()
```

```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/reference/geom_point.html")
```



#### Mapping a third variable

Now that we exhausted our first two options for visual encoding, we can use other aesthetics in our plot to map additional variables to. Example aesthetics available for `geom_point()` are

- `color` or `colour`: colour mapping.
- `shape`: mapping to symbols used for points.
- `fill`: colour mapping to shapes that have a `fill` attribute.
- `size`: mapping to the size of points.

Let's now map the number of cylinders `cyl` to the `colour` of points.

```{r, fig.height=7, message=FALSE}
mtcars |>
  ggplot(aes(x = mpg, y = hp, colour = cyl)) +
  geom_point()
```

Now each point is coloured according to the value of `cyl`. 

Because `cyl` is numeric, the default behaviour of R is to present it on a continuous scale, hence the colour gradient legend. 

If we want to present `cyl` as a categorical variable, we can override that behaviour by turning it into a factor using `factor()`

```{r, fig.height=7, message=FALSE}
mtcars |>
  ggplot(aes(x = mpg, y = hp, colour = factor(cyl))) +
  geom_point()
```


#### Adding a plot theme

Finally, any design elements can be specified in the theme layer. Theme elements can be customised using function [`theme()`](https://ggplot2.tidyverse.org/reference/theme.html) 


```{r, echo=FALSE}
knitr::include_url("https://ggplot2.tidyverse.org/reference/theme.html")
```


`ggplot2` also has a number of in built themes. Let's just use one of those.

```{r, fig.height=7, message=FALSE}
mtcars |>
  ggplot(aes(x = mpg, y = hp, fill = factor(cyl))) +
  geom_point() +
  theme_minimal()
```