17 Getting started with ggplot

Before you start

You need to be

familiar with different types of data (continuous, categorical)
able to handle tibbles
know how to install a package

17.1 The `ggplot2` package

ggplot2 is part of tidyverse so if you have already installed tidyverse, you already have ggplot2 installed. If not, see Section 5.6.2.

To activate ggplot2, you can use

library(ggplot2)

But most of the time you should use library(tidyverse).

17.2 The basics

ggplot is based on the grammar of graphics, a terminology describing the components of a figure.

Let us have a look at these terms.

To produce a figure or plot, we take data values and use elements like dots, squares, lines, and colour to convert the data into a visual graphic. There are many different ways to make a figure, but there are some rules that apply in general. A plot is always built on data, and a number of other components called aesthetics, geometry and scales. These different components combined make up a figure.

17.3 The code

The code for to make a scatterplot with the penguins dataset from the palmerpenguins package looks something like this:

ggplot(data = penguins,
       mapping = aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point() +
  labs(x = "Body mass, g", y = "Bill length, mm", colour = "Species") +
  theme_bw()

The main function is ggplot() and is used to define the data and the aesthetics. The data is a data frame or tibble containing the variables to produce the figure. The mapping uses aes() to describe how the variables should be mapped onto the aestetics, such as x and y position or colour. Every other component of the plot is added with +. It makes the code easier to read if you put a new line after each + (the + must go at the end of a line, not at the start). These other elements include the graphical elements that display the data, usually created with a geom_*() function, labels created with labs(), and themes (Chapter 19) that change the non-data elements of the plot with themes().

You can think of it as different layers that are put on top of each other see Figure 17.1.

Figure 17.1: Visualization of how ggplots are built.

17.4 Building a ggplot figure

Now we will describe the different components a ggplot is made of.

17.4.1 Data

To make a figure we use the function ggplot(). The first argument in this function is for the data object containing all the variables that you need to make a figure. Generally, it is advisable to have the data in a long format (Chapter 13).

ggplot(data = penguins)

The ggplot function by itself will produce an empty plot.

17.4.2 Aesthetics

The second argument in the ggplot() function, mapping, is where we define the plot’s aesthetics with the function aes(). The different elements from the aesthetics are used to display the data. The most important element is the position which describes the location of the data on the plot, usually by x and y. Other important elements are shape, fill, size, colour and line type, which describe how the data is presented on the plot.

In the penguin example, if we want to plot the bill length against the bill depth, we choose these two variables as x and y. As a second aesthetic we have chosen to map species to colour.

ggplot(penguins, aes(x = bill_length_mm,
                     y = bill_depth_mm,
                     colour = species))

One thing that might be confusing about the argument colour is that it only allows to be a variable, for example colour = species or colour = island. So for example colour = "red" does not work. If you want to choose a specific colour, this has to be done in geom or in scales (Section 20.1).

Now the ggplot function has added the x and y axis to the plot.

17.4.3 Geoms

To plot the actual data, you need a geom function. There are many different geom functions that you can choose. See Chapter 18 for an overview.

Here, we will use four different geoms to display aspects of the penguins data in different ways. The legend is automatically plotted on the right side when needed.

p1 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) +
  geom_point()
p2 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) +
  geom_density2d()
p3 <- ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar()
p4 <- ggplot(penguins, aes(x = species, y = bill_depth_mm, fill = species)) +
  geom_boxplot()

p1
p2
p3
p4

Exercise

Make a histogram of the penguins bill length.

Hint

ggplot(data = ___, mapping = aes(x = ___)) +
  geom____()

Make the histogram red.

Hint

ggplot(data = ___, mapping = aes(x = ___)) +
  geom____(fill = "red")

Make a violin plot of the penguins bill length by species.

Hint

ggplot(data = ___, mapping = aes(x = ___, y = ___)) +
  geom____()

Make a scatterplot of penguin bill length against flipper length.

Hint

ggplot(data = ___, mapping = aes(x = ___, y = ___)) +
  geom____()

Make the points in the scatterplot different colours and shapes by species

Hint

ggplot(data = ___, mapping = aes(x = ___, y = ___, colour = ___, shape = ___)) +
  geom____()

17.4.4 Scales

Scales as the name says is for scaling, converting, inverting. With scales you can manipulate the labels, breaks, transformations and palettes. In other words you can manually change the axis text, labels, ticks, add breaks, make transformations, and manipulate aesthetics like colour, shape, linetype ( Chapter 20).

Here is just a small selection to show what is possible. We can log transform the y axis, change the axis titles, and change the colours.

ggplot(penguins, aes(x = bill_length_mm,
                     y = bill_depth_mm,
                     colour = species)) +
  geom_point() +
  scale_y_log10() +
  scale_x_continuous() +
  scale_color_viridis_d()

Exercise

Give your violin plot a colour-blind friendly fill.

Hint

ggplot(data = ___, mapping = aes(x = ___, y = ___, fill = ___)) +
  geom____() +
  scale________()

17.4.5 Labs

labs() is a useful function to modify the axis labels, titles and legend.

The axis titles can be renamed, and a title and tag can be added.

ggplot(penguins, aes(x = bill_length_mm,
                     y = bill_depth_mm,
                     colour = species)) +
  geom_point() +
  labs(
    x = "Bill length mm", 
    y = "Bill depth mm", 
    title = "Bill length vs. depth in penguins", 
    tag = "A")

Titles can be useful for presentations, but generally not useful for manuscripts, where you can put the information into the caption. Tags are useful when building a multipart figure (Chapter 22).

Exercise

Add axis & legend labels to your violin plot

Hint

violin_plot + 
  labs(x = ___, y = ___, fill = ___)

17.4.6 Facets

Facets can be used to divide the data into different subplots, which can enhance readability of a figure.

In the next example, we use facet_wrap() to split the penguin data into 3 panels, one for each island.

ggplot(penguins, aes(x = bill_length_mm, 
                     y = bill_depth_mm,
                     colour = species)) +
  geom_point() +
  facet_wrap(facets = vars(island))

If you want to split the data by two variables, facet_grid() can be useful.

ggplot(penguins, aes(x = bill_length_mm, 
                     y = bill_depth_mm,
                     colour = species)) +
  geom_point() +
  facet_grid(rows = vars(island), cols = vars(sex))

Exercise

split your scatterplot into facets by sex.

Hint

scatter_plot +
  facet_wrap(vars(___))

17.4.7 Themes

Themes is a powerful element that controls the look of the plot. In themes you can change, remove and add the background, gridlines, ticks, text, text size and much more.

In our example, we will change the axis tile text size, the colour of the axis text and change the theme to theme_minimal().

See Chapter 19 for more about using themes.

ggplot(penguins, aes(x = bill_length_mm, 
                     y = bill_depth_mm,
                     colour = species)) +
  geom_point() +
  facet_wrap(facets = vars(island)) +
  theme_minimal() +
  theme(axis.title = element_text(size = 14),
        axis.text = element_text(colour = "purple"))

Exercise

Change the theme of your scatterplot

Hint

scatter_plot +
  theme___()

17.4.8 A ggplots are built with layers

As you can see, there are almost no limits to what you can do in a ggplot. The trick is to know which element changes what and to remember all the names (i.e. what is axis.text and axis.ticks etc.).

The order in which you code the different components of a plot often does not matter, except that the ggplot() needs to come first, and geoms are plotted in the order they are in the code. You can imagine the different components (geom, scale, stats) of the plot as different layers that are plotted on top of each other.

For example, you want to plot the data below and the smoother on top, and not the other way round.

points_on_top <- ggplot(penguins, aes(x = bill_length_mm,
                                      y = bill_depth_mm, 
                                      colour = species)) +
  geom_smooth() +
  geom_point()

smooth_on_top <- ggplot(penguins, aes(x = bill_length_mm, 
                                      y = bill_depth_mm,
                                      colour = species)) +
  geom_point() +
  geom_smooth() 

points_on_top
smooth_on_top

Exercise

Make a boxplot of bill length by species on top of the jittered bill length data

Hint

ggplot(data = ___, mapping = aes(x = ___, y = ___)) +
  geom____() +
  geom____()

17.5 Trouble shooting

In the beginning you will make mistakes which will result in error messages, warnings or incomplete plots. These mistakes often occur when one of the layers is missing, because a + is forgotten at the end of a line, due to a typo, or the data or other elements are forgotten.

To debug ggplot errors and other problems, we need to find where the error is. Run the first line (excluding with + at the end) and see what happens. If it does what you expect that line to run, then run the first two lines. You can also comment lines out (by putting a # in front of them). I recommend typing NULL on the line after the plot code so that if there is a extra + after removing a line, the plot won’t give a strange error by adding the next line of code.

ggplot(penguins, aes(x = species, y = bill_length_mm)) +
  geom_violin() + # extra + after commenting out labs()
#  labs(x = "Species", y = Bill length)
NULL #This will end the plot code

Let us look at some common mistakes and how the ggplot is displayed or not.

17.5.1 No data

If you forget to specify the data ggplot will not be able to make a plot and you will get the following error message:

ggplot(aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) +
  geom_point()

Error in `fortify()`:
! `data` must be a <data.frame>, or an object coercible by `fortify()`,
  not a <uneval> object.
ℹ Did you accidentally pass `aes()` to the `data` argument?

17.5.2 Missing aesthetics

If you forget to define the aesthetics aes() you will also get an error message and a empty image:

ggplot(data = penguins) +
  geom_point()

Error in `geom_point()`:
! Problem while setting up geom.
ℹ Error occurred in the 1st layer.
Caused by error in `compute_geom_1()`:
! `geom_point()` requires the following missing aesthetics: x and y

17.5.3 Missing geometry

If the geometry is missing geom_point(), ggplot will draw a empty plot, with axis labels, but show not data, because you have not defined how to plot the data.

ggplot(data = penguins, aes(x = bill_length_mm, 
                            y = bill_depth_mm, 
                            colour = species))

Exercise

I make lots of mistakes and my plots don’t work. Please help me fix them.

ggplot(penguins, aes(x = bill_length, y = bill_depth)) |> 
  geom_point() + 
  labs(x = "Bill length", y = "Bill width")

Hint

Use + not pipes to add plot elements

ggplot(penguins, aes(x = bill_length, y = bill_depth))  
+  geom_point() 
+  labs(x = "Bill length", y = "Bill width")

Hint

+ must be at the end of the line not the beginning

Why is this plot without colour?

ggplot(penguins, aes(x = bill_length, y = bill_depth, fill = species)) +  
  geom_point() + 
  scale_fill_viridis_c() +
  labs(x = "Bill length", y = "Bill width")

Hint

Make sure the aesthetic in aes() match the aesthetics in the scale_\*_\*() function. Also note that the c in scale_colour_viridis_c() is for a continuous scale. See the help file for how to change this.

What’s next?

Next, we will show the essential elements of a plot (title, axes, legend, …) and you can learn how to change and adapt each of these elements. Further you can learn the most common plots types that are commonly used (Chapter 18).

Contributors

Aud Halbritter
Richard Telford

``` {r setup, include=FALSE} source("R/setup.R") ``` # Getting started with ggplot ::: callout-note ## Before you start You need to be - familiar with different types of data (continuous, categorical) - able to handle tibbles - know how to install a package ::: ## The `ggplot2` package `ggplot2` is part of `tidyverse` so if you have already installed `tidyverse`, you already have `ggplot2` installed. If not, see @sec-tidyverse. To activate `ggplot2`, you can use ```{r load, eval=TRUE, echo=TRUE} library(ggplot2) ``` But most of the time you should use `library(tidyverse)`. ## The basics `ggplot` is based on the **grammar of graphics**, a terminology describing the components of a figure. Let us have a look at these terms. To produce a figure or plot, we take data values and use elements like dots, squares, lines, and colour to convert the data into a visual graphic. There are many different ways to make a figure, but there are some rules that apply in general. A plot is always built on data, and a number of other components called aesthetics, geometry and scales. These different components combined make up a figure. ## The code The code for to make a scatterplot with the `penguins` dataset from the [`palmerpenguins` package](getting-started-with-r.html#palmerpenguins) looks something like this: ```{r ggplot2-1, fig.height = 3, fig.width=3.5, warning = FALSE} ggplot(data = penguins, mapping = aes(x = body_mass_g, y = bill_length_mm, colour = species)) + geom_point() + labs(x = "Body mass, g", y = "Bill length, mm", colour = "Species") + theme_bw() ``` The main function is `ggplot()` and is used to define the data and the aesthetics. The `data` is a data frame or tibble containing the variables to produce the figure. The `mapping` uses `aes()` to describe how the variables should be mapped onto the **aes**tetics, such as x and y position or colour. Every other component of the plot is added with `+`. It makes the code easier to read if you put a new line after each `+` (the `+` must go at the end of a line, not at the start). These other elements include the graphical elements that display the data, usually created with a `geom_*()` function, labels created with `labs()`, and themes (@sec-themes) that change the non-data elements of the plot with `themes()`. You can think of it as different layers that are put on top of each other see @fig-ggplot-hierarchy. ```{r} #| label: fig-ggplot-hierarchy #| echo: false #| fig-cap: "Visualization of how ggplots are built." #| out-width: '100%' knitr::include_graphics("figures/ggplot_setup.png") ``` ## Building a ggplot figure Now we will describe the different components a ggplot is made of. ### Data To make a figure we use the function `ggplot()`. The first argument in this function is for the **data object** containing all the variables that you need to make a figure. Generally, it is advisable to have the data in a long format (@sec-tidying-data-with-tidyr). ```{r data1, echo = TRUE, eval = TRUE, fig.height = 3, fig.width = 3} ggplot(data = penguins) ``` The `ggplot` function by itself will produce an empty plot. ### Aesthetics The second argument in the `ggplot()` function, `mapping`, is where we define the plot's **aesthetics** with the function `aes()`. The different elements from the aesthetics are used to display the data. The most important element is the position which describes the location of the data on the plot, usually by x and y. Other important elements are shape, fill, size, colour and line type, which describe how the data is presented on the plot. In the penguin example, if we want to plot the bill length against the bill depth, we choose these two variables as `x` and `y`. As a second aesthetic we have chosen to map species to colour. ```{r aestetic, eval = TRUE, fig.height = 3, fig.width=3} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) ``` One thing that might be confusing about the argument colour is that it only allows to be a variable, for example `colour = species` or `colour = island`. So for example `colour = "red"` does not work. If you want to choose a specific colour, this has to be done in geom or in scales (@sec-colours-and-fill). Now the ggplot function has added the x and y axis to the plot. ### Geoms To plot the actual data, you need a **geom function**. There are many different geom functions that you can choose. See @sec-choosing-your-visualisation for an overview. Here, we will use four different geoms to display aspects of the penguins data in different ways. The legend is automatically plotted on the right side when needed. ```{r add-geom, fig.keep = 'none', eval = TRUE, fig.height=3, fig.width=3, warning = FALSE} p1 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() p2 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_density2d() p3 <- ggplot(penguins, aes(x = species, fill = island)) + geom_bar() p4 <- ggplot(penguins, aes(x = species, y = bill_depth_mm, fill = species)) + geom_boxplot() p1 p2 p3 p4 ``` ```{r four-geoms, echo = FALSE, warning=FALSE} library(patchwork) p1 + labs(title = "geom_point()") + p2 + labs(title = "geom_density2d()") + p3 + labs(title = "geom_bar()") + p4 + labs(title = "geom_boxplot()") ``` ::: callout-note ## Exercise - Make a histogram of the penguins bill length. <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___)) + geom____() ``` </details> - Make the histogram red. <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___)) + geom____(fill = "red") ``` </details> - Make a violin plot of the penguins bill length by species. <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___, y = ___)) + geom____() ``` </details> - Make a scatterplot of penguin bill length against flipper length. <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___, y = ___)) + geom____() ``` </details> - Make the points in the scatterplot different colours and shapes by species <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___, y = ___, colour = ___, shape = ___)) + geom____() ``` </details> ::: ### Scales **Scales** as the name says is for scaling, converting, inverting. With scales you can manipulate the labels, breaks, transformations and palettes. In other words you can manually change the axis text, labels, ticks, add breaks, make transformations, and manipulate aesthetics like colour, shape, linetype ( @sec-colours-shapes-and-linetypes). Here is just a small selection to show what is possible. We can log transform the y axis, change the axis titles, and change the colours. ```{r scales, eval = TRUE, fig.height = 3, fig.width=3.5, warning = FALSE} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + scale_y_log10() + scale_x_continuous() + scale_color_viridis_d() ``` ::: callout-note ## Exercise - Give your violin plot a colour-blind friendly fill. <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___, y = ___, fill = ___)) + geom____() + scale________() ``` </details> ::: ### Labs `labs()` is a useful function to modify the axis labels, titles and legend. The axis titles can be renamed, and a title and tag can be added. ```{r labs, eval = TRUE, fig.height = 3, fig.width = 3.5, warning = FALSE} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + labs( x = "Bill length mm", y = "Bill depth mm", title = "Bill length vs. depth in penguins", tag = "A") ``` Titles can be useful for presentations, but generally not useful for manuscripts, where you can put the information into the caption. Tags are useful when building a multipart figure (@sec-combining-plots). ::: callout-note ## Exercise - Add axis & legend labels to your violin plot <details> <summary>Hint</summary> ```{r, eval = FALSE} violin_plot + labs(x = ___, y = ___, fill = ___) ``` </details> ::: ### Facets **Facets** can be used to divide the data into different subplots, which can enhance readability of a figure. In the next example, we use `facet_wrap()` to split the penguin data into 3 panels, one for each island. ```{r facets, eval = TRUE, fig.height = 3, fig.width=5, warning = FALSE} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + facet_wrap(facets = vars(island)) ``` If you want to split the data by two variables, `facet_grid()` can be useful. ```{r facets-grid, eval = TRUE, fig.height = 3, fig.width=5, warning = FALSE} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + facet_grid(rows = vars(island), cols = vars(sex)) ``` ::: callout-note ## Exercise - split your scatterplot into facets by sex. <details> <summary>Hint</summary> ```{r, eval = FALSE} scatter_plot + facet_wrap(vars(___)) ``` </details> ::: ### Themes **Themes** is a powerful element that controls the look of the plot. In themes you can change, remove and add the background, gridlines, ticks, text, text size and much more. In our example, we will change the axis tile text size, the colour of the axis text and change the theme to `theme_minimal()`. See @sec-themes for more about using themes. ```{r theme, eval = TRUE, fig.height = 3, fig.width=5, warning = FALSE} ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + facet_wrap(facets = vars(island)) + theme_minimal() + theme(axis.title = element_text(size = 14), axis.text = element_text(colour = "purple")) ``` ::: callout-note ## Exercise - Change the theme of your scatterplot <details> <summary>Hint</summary> ```{r, eval = FALSE} scatter_plot + theme___() ``` </details> ::: ### A ggplots are built with layers As you can see, there are almost no limits to what you can do in a ggplot. The trick is to know which element changes what and to remember all the names (i.e. what is axis.text and axis.ticks etc.). The order in which you code the different components of a plot often does not matter, except that the `ggplot()` needs to come first, and geoms are plotted in the order they are in the code. You can imagine the different components (geom, scale, stats) of the plot as different layers that are plotted on top of each other. For example, you want to plot the data below and the smoother on top, and not the other way round. ```{r wrong-order, fig.keep='none', eval = TRUE, fig.height = 3, fig.width=3.5, warning = FALSE, message = FALSE} points_on_top <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_smooth() + geom_point() smooth_on_top <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() + geom_smooth() points_on_top smooth_on_top ``` ```{r wrong-order-plot, echo = FALSE, message = FALSE, fig.height = 3, fig.width=6, warning = FALSE} points_on_top + labs(title = "Points on top") + smooth_on_top + labs(title = "Smooth on top") & theme(legend.position = "none") ``` ::: callout-note ## Exercise - Make a boxplot of bill length by species on top of the jittered bill length data <details> <summary>Hint</summary> ```{r, eval = FALSE} ggplot(data = ___, mapping = aes(x = ___, y = ___)) + geom____() + geom____() ``` </details> ::: ## Trouble shooting In the beginning you will make mistakes which will result in error messages, warnings or incomplete plots. These mistakes often occur when one of the layers is missing, because a `+` is forgotten at the end of a line, due to a typo, or the data or other elements are forgotten. To debug `ggplot` errors and other problems, we need to find where the error is. Run the first line (excluding with `+` at the end) and see what happens. If it does what you expect that line to run, then run the first two lines. You can also comment lines out (by putting a `#` in front of them). I recommend typing `NULL` on the line after the plot code so that if there is a extra `+` after removing a line, the plot won't give a strange error by adding the next line of code. ```{r bad-code, eval = FALSE} ggplot(penguins, aes(x = species, y = bill_length_mm)) + geom_violin() + # extra + after commenting out labs() # labs(x = "Species", y = Bill length) NULL #This will end the plot code ``` Let us look at some common mistakes and how the ggplot is displayed or not. ### No data If you forget to specify the data ggplot will not be able to make a plot and you will get the following error message: ```{r no-data, echo=TRUE, error=TRUE, fig.height = 3, fig.width=3.5} ggplot(aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) + geom_point() ``` ### Missing aesthetics If you forget to define the aesthetics `aes()` you will also get an error message and a empty image: ```{r no-aestetics, echo=TRUE, error=TRUE, eval=TRUE, fig.height = 3, fig.width=3.5} ggplot(data = penguins) + geom_point() ``` ### Missing geometry If the geometry is missing `geom_point()`, ggplot will draw a empty plot, with axis labels, but show not data, because you have not defined how to plot the data. ```{r no-geom, fig.height = 3, fig.width=3.5} ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) ``` ::: callout-note ## Exercise I make lots of mistakes and my plots don't work. Please help me fix them. ```{r, eval = FALSE} ggplot(penguins, aes(x = bill_length, y = bill_depth)) |> geom_point() + labs(x = "Bill length", y = "Bill width") ``` <details> <summary>Hint</summary> Use `+` not pipes to add plot elements </details> ```{r, eval = FALSE} ggplot(penguins, aes(x = bill_length, y = bill_depth)) + geom_point() + labs(x = "Bill length", y = "Bill width") ``` <details> <summary>Hint</summary> `+` must be at the end of the line not the beginning </details> Why is this plot without colour? ```{r, eval = FALSE} ggplot(penguins, aes(x = bill_length, y = bill_depth, fill = species)) + geom_point() + scale_fill_viridis_c() + labs(x = "Bill length", y = "Bill width") ``` <details> <summary>Hint</summary> Make sure the aesthetic in `aes()` match the aesthetics in the `scale_\*_\*()` function. Also note that the **c** in `scale_colour_viridis_c()` is for a continuous scale. See the help file for how to change this. </details> ::: ::: callout-note ## What's next? Next, we will show the [essential elements]() of a plot (title, axes, legend, ...) and you can learn how to change and adapt each of these elements. Further you can learn the most common plots types that are commonly used (@sec-choosing-your-visualisation). ::: ::: {.column-margin} ### Contributors {.unlisted .unnumbered} - Aud Halbritter - Richard Telford :::

17.1 The ggplot2 package

17.2 The basics

17.3 The code

17.4 Building a ggplot figure

17.4.1 Data

17.4.2 Aesthetics

17.4.3 Geoms

17.4.4 Scales

17.4.5 Labs

17.4.6 Facets

17.4.7 Themes

17.4.8 A ggplots are built with layers

17.5 Trouble shooting

17.5.1 No data

17.5.2 Missing aesthetics

17.5.3 Missing geometry

Contributors

17.1 The `ggplot2` package