06-plot-ggplot2.Rmd

---
title: "Creating Publication-Quality Graphics with ggplot2"
teaching: 60
exercises: 30
questions:
- "How can I create publication-quality graphics in R?"
objectives:
- "To be able to use `ggplot2` to generate publication quality graphics"
- "To apply geometry, aesthetic, and statisics layers to a `ggplot` plot"
- "To manipulate the aesthetics of a plot using different colours, shapes, and lines"
- "To improve data visualization through transforming scales and paneling by group"
- "To save a plot created with ggplot to disk"
keypoints:
- "Use `ggplot2` to create plots"
- "Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping"
source: Rmd
---

```{r setup, include=FALSE}
source("bin/chunk-options.R")
knitr_fig_path("06-")
# Silently load in the packages and data so the rest of the lesson works
library(readr)
library(ggplot2)
gapminder <- read_csv("data/gapminder_data.csv")
```

# Creating Publication-Quality Graphics with ggplot2

Plotting is one of the best ways to
quickly explore your data and the various relationships
between variables.

There are three main plotting systems in R,
the [base plotting system][base], the [`lattice`][lattice]
package, and the [`ggplot2`][ggplot2] package.

[base]: http://www.statmethods.net/graphs/index.html
[lattice]: http://www.statmethods.net/advgraphs/trellis.html
[ggplot2]: http://www.statmethods.net/advgraphs/ggplot2.html

Today we'll be learning about the `ggplot2` package, because
it is the most effective for creating publication-quality
graphics.

`ggplot2` is built on the grammar of graphics, the idea that any plot can be
expressed from the same set of components: a **data** set, a
**coordinate system**, and a set of geometric objects or **geoms**---the visual representation of data points.

The key to understanding `ggplot2` is thinking about a plot or figure in layers.
This idea may be familiar to you if you have used image editing programs like Photoshop, Illustrator, or
Inkscape.

## Getting started

Let's start off by installing and loading the the `ggplot2` package:

```{r, eval=FALSE}
install.packages("ggplot2")
library(ggplot2)
```

And loading our gapminder data:

```{r load-data}
library(readr)
gapminder <- read_csv("data/gapminder_data.csv")
gapminder
```


Now let's jump right into making an example `ggplot2` plot:

```{r lifeExp-vs-gdpPercap-scatter, message=FALSE}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

So the first thing we do is call the `ggplot()    ` function. This function lets R
know that we're creating a new plot, and any of the arguments we give the
`ggplot` function are the *global* options for the plot: they apply to all
layers on the plot.

We've passed in two arguments to `ggplot`. First, we tell `ggplot` what data we
want to show on our figure, in this example the gapminder data we read in
earlier. For the second argument&mdash;`mapping`&mdash;we passed in the `aes` function, which
tells `ggplot` how variables in the **data** map to *aesthetic* properties of
the figure, in this case the **x** and **y** locations. Here we told `ggplot` we
want to plot the "gdpPercap" column of the gapminder data frame on the x-axis, and
the "lifeExp" column on the y-axis. Notice that we didn't need to explicitly
pass `aes` these columns (e.g. `x = gapminder$gdpPercap`), this is because
`ggplot` is smart enough to know to look in the **data** for that column!

By itself, the call to `ggplot` isn't enough to draw a figure:

```{r no-geom}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
```

We need to tell `ggplot` how we want to visually represent the data, which we
do by adding a new **geom** layer. In our example, we used `geom_point()`, which
tells `ggplot` we want to visually represent the relationship between **x** and
**y** as a scatterplot of points:

```{r lifeExp-vs-gdpPercap-scatter2}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

_Recap_: Every `ggplot2` plot has three key components: (1) **data**, (2) a set of aesthetic **mappings** between variables in the data and visual properties on the plot, and (3) at least one layer, created with a **geom** function.


### Challenge 1 (5 minutes)
>
> Modify the example so that the figure shows how life expectancy has
> changed over time:
>
> ```{r, eval=FALSE}
> ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
> geom_point()
> ```
>
> Hint: the gapminder dataset has a column called "year", which should appear
> on the x-axis.
>
> <details>
> 
> <summary>
> Solution to challenge 1
> </summary>
> 
> <br />
> Here is one possible solution:
>
> ```{r ch1-sol}
> ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) +
> geom_point()
> ```
> </details>

### Challenge 2 (10 minutes)
> In the previous examples and challenge we've used the `aes` function to tell
> the scatterplot **geom** about the **x** and **y** locations of each point.
> Another *aesthetic* property we can modify is the point *colour*. Modify the
> code from the previous challenge to **colour** the points by the "continent"
> column. What trends do you see in the data? Are they what you expected?
>
> <details>
> 
> <summary>
> Solution to challenge 2
> </summary>
> 
> <br />
> ```{r ch2-sol}
> ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, colour = continent)) +
>   geom_point()
> ```
> </details>


## Layers

Using a scatterplot probably isn't the best for visualizing change over time.
Instead, let's tell `ggplot` to visualize the data as a line plot:

```{r lifeExp-line-no-group}
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, colour = continent)) +
  geom_line()
```

Instead of adding a `geom_point()` layer, we've added a `geom_line()` layer, however it looks like the lines are connecting the wrong points? We need to add the **group** *aesthetic*, which tells `ggplot()` to draw a line for each country.


```{r lifeExp-line}
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, colour = continent, group = country)) +
  geom_line()
```


What if we want to visualize both lines and points on the plot? We can simply add another layer to the plot:

```{r lifeExp-line-point}
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, colour = continent, group = country)) +
  geom_line() +
  geom_point()
```

It's important to note that each layer is drawn on top of the previous layer. In
this example, the points have been drawn *on top of* the lines. Here's a
demonstration:

```{r lifeExp-layer-example-1}
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, group = country)) +
  geom_line(mapping = aes(colour = continent)) +
  geom_point()
```

In this example, the *aesthetic* mapping of **colour** has been moved from the
global plot options in `ggplot` to the `geom_line` layer so it no longer applies
to the points. Now we can clearly see that the points are drawn on top of the
lines.

::: {.rmdtip}

**Tip: Setting an aesthetic to a value instead of a mapping**

So far, we've seen how to use an aesthetic (such as **colour**) as a *mapping* to a variable in the data. For example, when we use `geom_line(mapping = aes(colour = continent))`, ggplot will give a different colour to each continent. But what if we want to change the colour of all lines to blue? You may think that `geom_line(mapping = aes(colour = "blue"))` should work, but it doesn't. Since we don't want to create a mapping to a specific variable, we simply move the colour specification outside of the `aes()` function, like this: `geom_line(colour = "blue")`.

:::

```{r example-colour-all}
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, group = country)) +
  geom_line(colour = "blue") +
  geom_point()
```

### Challenge 3 (5 minutes)
>
> Switch the order of the point and line layers from the previous example. What
> happened?
>
> <details>
> 
> <summary>
> Solution to challenge 3
> </summary>
> 
> <br />
>
> ```{r ch3-sol}
> ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, group = country)) +
>  geom_point() +
>  geom_line(mapping = aes(colour = continent))
> ```
>
> The lines now get drawn over the points!
>
> </details>


## Transformations and statistics

`ggplot2` also makes it easy to overlay statistical models over the data. To
demonstrate we'll go back to our first example:

```{r lifeExp-vs-gdpPercap-scatter3, message=FALSE}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```

Currently it's hard to see the relationship between the points due to some strong
outliers in GDP per capita. We can change the scale of units on the x axis using
the *scale* functions. These control the mapping between the data values and
visual values of an aesthetic. We can also modify the transparency of the
points, using the *alpha* function, which is especially helpful when you have
a large amount of data which is very clustered.

```{r axis-scale}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) +
  scale_x_log10()
```

The `log10` function applied a transformation to the values of the gdpPercap
column before rendering them on the plot, so that each multiple of 10 now only
corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita
of 1,000 is now 3 on the y axis, a value of 10,000 corresponds to 4 on the y
axis and so on. This makes it easier to visualize the spread of data on the
x-axis.

::: {.rmdtip}

**Tip Reminder: Setting an aesthetic to a value instead of a mapping**

Notice that we used `geom_point(alpha = 0.5)`. As the previous tip mentioned, using a setting outside of the `aes()` function will cause this value to be used for all points, which is what we want in this case. But just like any other aesthetic setting, *alpha* can also be mapped to a variable in the data. For example, we can give a different transparency to each continent with `geom_point(mapping = aes(alpha = continent))`.

::: 

We can fit a simple relationship to the data by adding another layer, `geom_smooth`:

```{r lm-fit}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  scale_x_log10() +
  geom_smooth(method = "lm")
```

We can make the line thicker by *setting* the **size** aesthetic in the
`geom_smooth` layer:

```{r lm-fit2}
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  scale_x_log10() +
  geom_smooth(method = "lm", size = 1.5)
```

There are two ways an *aesthetic* can be specified. Here we *set* the **size**
aesthetic by passing it as an argument to `geom_smooth`. Previously in the
lesson we've used the `aes` function to define a *mapping* between data
variables and their visual representation.

### Challenge 4a (10 minutes)
>
> Modify the colour and size of the points on the point layer in the previous
> example.
>
> Hint: do not use the `aes()` function.
>
> <details>
> 
> <summary>
> Solution to challenge 4a
> </summary>
> 
> <br />
>
> ```{r ch4a-sol}
> ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
>  geom_point(size = 3, colour = "orange") +
>  scale_x_log10() +
>  geom_smooth(method = "lm", size = 1.5)
> ```
> </details>



### Challenge 4b (10 minutes)
>
> Modify your solution to Challenge 4a so that the
> points are now a different shape and are coloured by continent with new
> trendlines.  
>
> Hint: The colour argument can be used inside the aesthetic.
>
> <details>
> 
> <summary>
> Solution to challenge 4b
> </summary>
> 
> <br />
> ```{r ch4b-sol}
> ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, colour = continent)) +
> geom_point(size = 3, shape = 17) +
> scale_x_log10() +
> geom_smooth(method = "lm", size = 1.5)
> ```
> </details>


## Multi-panel figures

Earlier we visualized the change in life expectancy over time across all
countries in one plot. Alternatively, we can split this out over multiple panels
by adding a layer of **facet** panels. 

For this exercise we will use a subset of the gapminder data. If you are already familiar the `dplyr` package, you can subset the `gapminder` data frame:

```{r, eval=FALSE}
library(dplyr)
asia_oceania <- filter(gapminder, continent %in% c("Asia", "Oceania"))
```

Or you can use the `read_csv` function and read in this subset of the gapminder data already provided in the `/data` folder:

```{r facet}
asia_oceania <- read_csv("data/gapminder_asia_oceania.csv")

ggplot(data = asia_oceania, mapping = aes(x = year, y = lifeExp)) +
  geom_line() + 
  facet_wrap(facets = vars(country))
```

The `facet_wrap()` layer took the function `vars()` as its `facet` argument. This tells R to draw a panel for each unique value in the variable country of the gapminder dataset.


## Modifying text

To clean this figure up for a publication we need to change some of the text
elements. The x-axis is too cluttered, and the y axis should read
"Life Expectancy", rather than the column name in the data frame.

We can do this by adding a couple of different layers. The **theme** layer
controls the axis text, and overall text size. Labels for the axes, plot 
title and any legend can be set using the `labs` function. Legend titles
are set using the same names we used in the `aes` specification. Thus below
the colour legend title is set using `colour = "Continent"`, while the title 
of a fill legend would be set using `fill = "MyTitle"`. 

```{r theme}
ggplot(data = asia_oceania, mapping = aes(x = year, y = lifeExp, colour = continent)) +
  geom_line() +
  facet_wrap(facets = vars(country)) +
  labs(
    x = "Year",              # x axis title
    y = "Life Expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    colour = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(size = 5),
        plot.title = element_text(face = "bold"))
```


::: {.rmptip}
**Tip: Theming**

Note that we apply a "theme" definition to customize 
the x-axis label sizes to maintain readability.  Nearly everything in
ggplot2 is customizable.

:::

We can also use one of the many built in [themes][ggplot2-themes] in `ggplot2`. Like geom layers, each theme each layer is drawn on top of the previous theme layer.

```{r more-theme}
ggplot(data = asia_oceania, mapping = aes(x = year, y = lifeExp, colour = continent)) +
  geom_line() +
  facet_wrap(facets = vars(country)) +
  labs(
    x = "Year",              # x axis title
    y = "Life Expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    colour = "Continent"      # title of legend
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 5),
        plot.title = element_text(face = "bold"))
```

## Exporting the plot

The `ggsave()` function allows you to export a plot created with `ggplot2`. You can specify the dimension and resolution of your plot by adjusting the appropriate arguments (`width`, `height` and `dpi`) to create high quality graphics for publication. In order to save the plot from above, we first assign it to a variable `lifeExp_plot`, then tell `ggsave` to save that plot in `png` format to a directory called `results`. (Make sure you have a `results/` folder in your working directory.)

```{r directory-check, echo = FALSE}
dir.create("results", showWarnings = FALSE)
```

```{r save}
lifeExp_plot <- ggplot(data = asia_oceania, mapping = aes(x = year, y = lifeExp, colour = continent)) +
  geom_line() +
  facet_wrap(facets = vars(country)) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    colour = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(size=10),
        plot.title = element_text(face = "bold"))

ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, width = 18, height = 16, units = "cm")

ggsave(filename = "results/lifeExp_widetv.png", plot = lifeExp_plot, width = 13.33, height =  7.5)
```

There are two nice things about `ggsave`. First, it defaults to the last plot, so if you omit the `plot` argument it will automatically save the last plot you created with `ggplot`. Secondly, it tries to determine the format you want to save your plot in from the file extension you provide for the filename (for example `.png` or `.pdf`). If you need to, you can specify the format explicitly in the `device` argument.


## Combining `dplyr` and `ggplot2`

First install and load dplyr:

```{r,eval=FALSE}
install.packages('dplyr')
```

```{r,message=FALSE}
library("dplyr")
```

In this plotting lesson we will look at how to make a multi-panel figure by adding
a layer of facet panels using `ggplot2`. 

```{r}
# Filter countries that start with "A"
a_countries <- gapminder %>% 
  filter(country %in% c("Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria"))

# Make the plot
ggplot(data = a_countries, aes(x = year, y = lifeExp, color = continent)) +
  geom_line() + 
  facet_wrap(vars(country))
```


This code makes the right plot but it also provides a way to chain operations. Just as we used
`%>%` to pipe data along a chain of `dplyr` functions we can use it to pass data
to `ggplot()`. Because `%>%` replaces the first argument in a function we don't
need to specify the `data =` argument in the `ggplot()` function. By combining
`dplyr` and `ggplot2` functions we can alter this figure for only those continents in Europe.

```{r}
gapminder %>%
  filter(country %in% c("Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria")) %>% 
  filter(continent == "Europe") %>% 
  ggplot(aes(x = year, y = lifeExp, color = continent)) +
  geom_line() +
  facet_wrap(vars(country))
```

Using `dplyr` functions also helps us do calculations on the fly, for example if we were interested in 
converting `lifeExp` which is in years to days:

```{r}
gapminder %>%
  filter(country %in% c("Afghanistan", "Albania", "Algeria", "Angola", "Argentina", "Australia", "Austria")) %>% 
  filter(continent == "Europe") %>% 
  mutate(num_days = lifeExp*365) %>% 
  ggplot(aes(x = year, y = num_days, color = continent)) +
  geom_line() +
  facet_wrap(vars(country))
```

### Advanced Challenge

> Calculate the average life expectancy in 2002 of 2 randomly selected countries
> for each continent. Then arrange the continent names in reverse order.
> **Hint:** Use the `dplyr` functions `arrange()` and `sample_n()`, they have
> similar syntax to other dplyr functions.
> 
> <details>
> 
> <summary>
> Solution to challenge
> </summary>
> 
> <br />
>```{r}
>lifeExp_2countries_bycontinents <- gapminder %>%
>    filter(year==2002) %>%
>    group_by(continent) %>%
>    sample_n(2) %>%
>    summarize(mean_lifeExp=mean(lifeExp)) %>%
>    arrange(desc(mean_lifeExp))
>```
> </details>




This is a taste of what you can do with `ggplot2`. RStudio provides a
really useful [cheat sheet][cheat] of the different layers available, and more
extensive documentation is available on the [ggplot2 website][ggplot-doc].
Finally, if you have no idea how to change something, a quick Google search will
usually send you to a relevant question and answer on Stack Overflow or the [RStudio Community website][rstudio-community] with reusable
code to modify. Finally, there are many websites that provide examples of `ggplot2` plots with open code, for example [The R Graph Gallery][rgallery]. 

[cheat]: https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf
[ggplot-doc]: https://ggplot2.tidyverse.org/reference/index.html
[rstudio-community]: https://community.rstudio.com/
[ggplot2-themes]: https://ggplot2.tidyverse.org/reference/ggtheme.html
[rgallery]: https://www.r-graph-gallery.com/