ggplot2_intermediate.qmd

---
title: "R ggplot2: intermediate data visualisation"
format:
  gfm:
    toc: true
    toc-depth: 2
editor: source
date: today
author: UQ Library
---

## Essential shortcuts

-   function or dataset help: press <kbd>F1</kbd> with your cursor anywhere in a function name.
-   execute from script: <kbd>Ctrl</kbd> + <kbd>Enter</kbd>
-   assignment operator (`<-`): <kbd>Alt</kbd> + <kbd>-</kbd>

## Open RStudio

On Library computers:

-   Log in with your UQ username and password (use your student credentials if you are both staff and student)
-   Make sure you have a working internet connection
-   Go to search the magnifying glass (bottom left)
-   Open the ZENworks application
-   Look for the letter R
-   Double click on RStudio which will install both R and RStudio

If you are using your own laptop:

-   Make sure you have a working internet connection
-   Open RStudio

## Disclaimer

We will assume you are an R intermediate user and that you have used ggplot2 before.

## What are we going to learn?

During this hands-on session, you will:

-   install a tool for picking colours
-   customise scales and ranges
-   divide a visualisation into facets
-   explore new geometries
-   modify statistical transformations
-   adjust a geometry's position
-   further modify themes
-   make a plot interactive

## Setting up

**Install ggplot2** if you don't already have it, with: `install.packages("ggplot2")`

**Create a new project** to keep everything nicely contained in one directory:

-   Click the "Create a project" button (top left cube icon)
-   Click "New Directory"
-   Click "New Project" ("Empty project" if you have an older version of RStudio)
-   In "Directory name", type the name of your project, e.g. "ggplot2_intermediate"
-   Select the folder where to locate your project: e.g. `Documents/RProjects`, which you can create if it doesn't exist yet. You can use your H drive at UQ to make sure you can find it again.
-   Click the "Create Project" button

Let's also create a "plots" folder to store exports:

```{r eval=FALSE}
dir.create("plots")
```

Create a **new script** (File \> New File \> R Script) and add a few comments to give context:

    # Description : ggplot2 intermediate with gapminder data
    # Author: <your name>
    # Date: <today's date>

Finally, make sure you **load ggplot2** so we can use its functions:

```{r}
library(ggplot2)
```

## Import data

### Challenge 1 – import data

Our data is located at <https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv>

Using the following syntax, how can you read the online CSV data into an R object?

    gapminder <- ...

You have to use the `read.csv()` function, which can take a URL:

```{r}
gapminder <- read.csv(
  file = "https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv")
```

If you are not familiar with the dataset, `View()` and `summary()` can help you explore it.

```{r eval=FALSE}
View(gapminder)    # view as a separate spreadsheet
summary(gapminder) # summary statistics for each variable
```

The Environment pane gives you an overview of the variables.

## Explore data visually

Let's start with a question: how do Gross Domestic Product (GDP) and life expectancy relate?

We can make a simple plot with the basics of ggplot2:

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

Remember that the 3 main elements of a ggplot2 visualisation are:

-   the *data*
-   the *mapping of aesthetics to variables*
-   the *geometry*

## Aesthetics available

So far we have been using the `x` and `y` aesthetics. There are more available, depending on the geometry that you are using.

Here are some common examples:

-   To change the *shape* based on a variable, use `shape = <discrete variable>` inside the `aes()` call.
-   If you want to change the size of the geometric object, you can use the `size = <continuous variable>` argument.
-   Similarly, to change the *colour* based on a variable, use `colour = <variable>` and `fill = <variable>` inside the `aes()` call.

Let's modify our plot to colour the points according to the continent variable.

```{r}
ggplot(data = gapminder,
       mapping = aes(x = gdpPercap,
                     y = lifeExp)) +
  geom_point(aes(colour = continent)) +
  geom_smooth()
```

### Challenge 2 - save our plot

How can we save our scatter plot to the 'plots' directory we created using a function?

```{r}
ggsave(filename = "plots/gdpPercap_v_lifeExp.png", width = 7, height = 4)
```

> Remember that the `ggsave` function saves the last plot.

## Modifying scales

### More control over colours

This plot uses the default discrete palette.

> **Saving some typing**: We will keep modifying this plot. To reuse the constant base of our plot (the `ggplot()` call and the point geometry), we can create an object:

```{r}
p <- ggplot(data = gapminder,
       mapping = aes(x = gdpPercap,
                     y = lifeExp)) +
  geom_point(aes(colour = continent)) +
  geom_smooth()
```

We can use other palettes than the default one. ggplot2 provides extra functions to modify colour scales. For example:

```{r}
p +
  scale_colour_viridis_d()
```

Viridis is a collection of palettes that are designed to be accessible (i.e. perceptually uniform in colour or black and white, and perceivable for various forms of colour blindness). The structure of the function name is `scale_<aesthetic>_viridis_<datatype>()`, the different data types being **d**iscrete, **c**ontinuous or **b**inned.

Another collection of palettes is the ColorBrewer collection:

```{r}
p +
  scale_colour_brewer(palette = "Set1")
```

You can see the palettes available by looking at the help page of `scale_colour_brewer()`, under the header "Palettes". However, the names alone might not be enough to picture them, so head to <http://colorbrewer2.org/> to find the one that you like. Importantly, the website allows you to tick the options "colorblind safe" and "print friendly"... which would rule out all the qualitative palettes for our 5 continents!

A useful package that introduces many palettes for ggplot2 is the colorspace package, which promotes the [Hue-Chroma-Luminance (HCL) colour space](https://colorspace.r-forge.r-project.org/articles/color_spaces.html#human-color-vision-and-the-hcl-color-model-1). This colour space is perceptually-based, which means it is particularly suited for human perception of colours.

For colorspace, the function names are structured as follows: `scale_<aesthetic>_<datatype>_<colorscale>()`

Let's first use an alternative qualitative palette:

```{r}
library(colorspace)
p +
  scale_colour_discrete_qualitative()
```

This is the default ggplot2 palette! Which means ggplot2 already use a HCL palette. But having the colorspace package loaded, we can now see all the palettes available:

```{r}
hcl_palettes(plot = TRUE)
```

Let's try a different one:

```{r}
p +
  scale_colour_discrete_sequential("Batlow")
```

Finally, to use a custom palette, we can use the ggplot2 function `scale_colour_manual()` and provide a list of colour names.

```{r}
p +
  scale_colour_manual(values = c("lightblue", "pink", "purple", "black", "red"))
```

You can list all the R colour names with the function `colours()`, which prints out a list of their names, but know that you are not limited to these 657 values: you can also use HEX values, which is particularly useful if you have to respect a colour scheme you were given.

You can find [visual lists](https://r-charts.com/colors/) of all the R colours, but there is a way to pick colours more comfortably: we can use the colourpicker package, which adds a handy add-in to RStudio. Install it and use the new "Addins \> Colour Picker" tool to create a vector of colours for your custom palette.

> That was a lot of options about colours, but know that in some cases, the most straight-forward way to make your visualisations readable by most is to use symbols instead of colours. In ggplot2, you would use the `shape` aesthetic.

### Axis scale modifiers

We could further modify our plot to make it more readable. For example, we can use a different x axis scale to distribute the data differently:

```{r}
p +
  scale_x_log10()
```


We can also further customise a scale with breaks and labels:

```{r}
p +
  scale_x_log10(breaks = c(5e2, 1e3, 1e4, 1e5),
                labels = c("500", "1 k", "10 k", "100 k"))
```

> You can use the scientific notation `1e5` to mean "a 1 followed by 5 zeros".

## Zooming in

We might want to focus on the left hand side part of our original plot:

```{r}
p
```

To zoom in, we might want to change our axis limits by using `ylim()`.

```{r}
p +
  xlim(c(0, 6e4))
```

Notice the warning message? ggplot2 informs us that it couldn't represent part of the data because of the axis limits.

The method we use works for our point geometry, but is problematic for other shapes that could disappear entirely or change their appearance because they are based on different data: we are actually **clipping** our visualisation! Notice how the trend line now looks different?

A better way to focus on one part of the plot would be to modify the **coordinate system**:

```{r}
p +
  coord_cartesian(xlim = c(0, 6e4))
```

The Cartesian coordinate system is the default one in ggplot2. You could change the coordinate system to `coord_polar()` for circular visualisations, or to `coord_map()` to visualise spatial data.

## Interactive plots

The plotly package brings the power of the Plotly javascript library to R. Install it with `install.packages(plotly)`, and you'll then be able to convert a ggplot2 visualisation into an interactive HTML visualisation with one single function!

Let's reuse our original plot object, and feed it to `ggplotly()`:

```{r eval=FALSE}
library(plotly)
ggplotly(p)
```

You can now identify single points, zoom into your plot, and show/hide categories. However, the pop-up does not tell us which country the point corresponds to. That's because we don't mention the `country` variable in our code. Let's reveal that information by slightly modifying the `p` object:

```{r eval=FALSE}
p <- ggplot(data = gapminder,
       mapping = aes(x = gdpPercap,
                     y = lifeExp,
                     label = country)) + # one extra aesthetic for an extra variable
  geom_point(aes(colour = continent)) +
  geom_smooth()
```

The interactive version now tells us which country each point correspond to:

```{r eval=FALSE}
ggplotly(p)
```

To add yet another variable to the visualisation, we can animate the plot but associating the `frame` aesthetic with the `year` variable. This adds a slider and a "Play" button under the visualisation.

```{r eval=FALSE}
p <- ggplot(data = gapminder,
       mapping = aes(x = gdpPercap,
                     y = lifeExp,
                     label = country,
                     frame = year)) + # one frame per year
  geom_point(aes(colour = continent))
ggplotly(p)
```

> You can export the visualisation as a HTML page for sharing with others.

## Histograms

### Challenge 3 – histogram of life expectancy

Search for the histogram geometry function, and plot the life expectancy. How can we modify the bars?

```{r}
ggplot(gapminder, aes(x = lifeExp)) +
  geom_histogram() # by default, bins = 30
```

> **Saving some typing:** remember we can omit the names of the arguments if we use them in order? Being explicit about the **argument names** is useful when learning the ins and outs of a function, but as you get more familiar with ggplot2, you can do away with the obvious ones, like `data =` and `mapping =` (as long as they are used in the right order!).

Let's change the bin width:

```{r}
ggplot(gapminder, aes(x = lifeExp)) +
  geom_histogram(binwidth = 1)
```

Here, each bar contains one year of life expectancy.

We can also change the number of bins:

```{r}
ggplot(gapminder, aes(x = lifeExp)) +
  geom_histogram(bins = 10)
```

Now, let's colour the bins by continent. Instinctively, you could try the `colour` aesthetic:

```{r}
ggplot(gapminder, aes(x = lifeExp, colour = continent)) +
  geom_histogram(bins = 10)
```

...but it only colours the outline of the rectangles!

Some aesthetics will work better with some geometries than others. We have to use the `fill` aesthetic to colour the areas instead:

```{r}
ggplot(gapminder, aes(x = lifeExp, fill = continent)) +
  geom_histogram(bins = 10)
```

Colouring our bins allows us to experiment with the geometry's **position**. The histogram geometry uses the "stack" position by default. It might convey different information if we make it use ratios instead, using the `position = "fill"` argument:

```{r}
ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 10,
                 position = "fill")
```

You might have noticed that the y-axis is still labeled 'count' when it has changed to a proportion. We can modify the y-axis labels to percent using the `percent()` function from the scales package.

We can also modify the y-axis labels with the `labs()` function.

```{r}
library(scales)

ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 10,
                 position = "fill") +
  scale_y_continuous(labels = percent) +
  labs(y = "Percent")
```

We can also make the bars "dodge" each other:

```{r}
ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 10,
                 position = "dodge")
```

## Faceting

An even more readable representation could use **faceting**:

```{r}
ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 40) +
  facet_wrap(vars(continent))
```

We have to wrap the variable(s) we want to facet by into the `vars()` function.

> Faceting is a great way to add yet another variable to your visualisation, instead of using another aesthetic.

## Theming

The legend is probably superfluous. We want to keep the colours, but we use the `theme()` function to customise the look of our plot and remove the legend:

```{r}
ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 40) +
  facet_wrap(vars(continent)) +
  theme(legend.position = "none")
```

If you use a pre-built theme function, make sure you place it before customising the legend. Otherwise it will bring the legend back!

```{r}
ggplot(gapminder,
       aes(x = lifeExp,
           fill = continent)) +
  geom_histogram(bins = 40) +
  facet_wrap(vars(continent)) +
  theme_minimal() + # before customising the legend!
  theme(legend.position = "none")
```

## A more refined facetted example

This extra example gives an idea of how a refined ggplot2 visualisation might be constructed. It represents 4 different variables, using the larger diamonds data set.

```{r}
ggplot(diamonds,
       aes(x = carat,
           y = price)) +
  geom_point(aes(colour = color),
             alpha = 0.5,
             size = 0.5) +
  scale_colour_viridis_d(option = "turbo") +
  geom_smooth(se = FALSE,
              linetype = "dashed",
              colour = "black",
              linewidth = 0.5) +
  facet_wrap(vars(cut)) +
  theme_minimal() +
  labs(y = "price (USD)")
```

In this visualisation:

* 4 different variables are represented, thanks to both aesthetics and facets
* two geometries are layered on top of each other to represent a relationship
* both geometries are customised to make the plot readable (important here, since there are close to 54,000 rows of data)
* the default colour scale is replaced
* a built-in theme is used
* a label clarifies the unit of measurement

## Boxplots

A simple boxplot can help visualise a distribution in categories:

```{r}
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
  geom_boxplot()
```

### Challenge 4 – code comprehension

What do you think this extra line might do to our boxplots?

```{r}
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90))
```

This is useful if the x labels get too cramped on the x axis: you can rotate them to whatever angle you want.

> Try turning this plot into an interactive visualisation to see stats easily!

## Summarise data and plot

Let's try summarising the average and standard deviation of life expectancy by continent from the gapminder data and piping it directly into a ggplot. We will need to install/load dplyr for this.

```{r}
# install.packages("dplyr")
library(dplyr)

gapminder %>% 
  group_by(continent) %>% 
  summarise(aveLE = mean(lifeExp),
            sdLE = sd(lifeExp)) %>% 
  ggplot(aes(x = continent, 
             y = aveLE)) +
  geom_bar(stat = "identity",
           fill = "tomato") +
  geom_errorbar(aes(ymin = aveLE - sdLE,
                    ymax = aveLE + sdLE),
                width = 0.1) +
  labs(x = "Continent",
       y = "Life Expectancy in Years")
```

## Plot from multiple summarised dataframes 

In ggplot2, multiple data frames can be plotted on the same plot. For example, let's say we wanted to make a bar graph of total population by year with the gapminder data set colored by continent and also include the standard deviation of the total population per year at the top of each column as an error bar? This may or may not make the most sense in terms of representing the data, but we will do it here as an exercise.

1. summarise the data by year to calculate the standard deviation
2. summarise the data by continent and year for the filled columns
3. plot the continent data and add the error bars from the first summary

```{r}
total <- gapminder %>% 
   group_by(year) %>% # group by year
   summarise(tot = sum(pop), # sum the population for every year
             SD = sd(pop))
total

cont_ave <- gapminder %>% 
  group_by(continent, year) %>% 
  summarise(totalpop = sum(pop)) %>% 
   ungroup()
cont_ave
```

When specifying a data set outside of the main `ggplot()` function - the `data = ` argument must be used. The other functions `geom_bar()` etc. assume the first argument is `mapping = aes()` unless the data argument is explicitly defined.

```{r}
ggplot(data = cont_ave, aes(x = year, 
             y = totalpop)) +
  geom_bar(aes(fill = continent),
           stat = "identity") + # default is `stat = count` like a histogram
  geom_errorbar(data = total, # must use `data = ` to specify a new data set being used
                 aes(y = tot, # the y from the ggplot(aes()) is inherited, update
                     ymin = tot - SD, # error bar arguments
                     ymax = tot + SD),
                width = 0.8) +
      scale_x_continuous(breaks = seq(from = 1952, to = 2007, by = 5)) + # use seq to get years from 1952 - 2007 every 5 yrs to label every column
   scale_y_continuous(breaks = c(2e9, 4e9, 6e9), # keep the same breaks
      labels = c("2", "4", "6")) + # relabel so number represent billions
   labs(x = "Year", 
        y = "Population in Billions", # rename units in billions
        fill = "Continent") + # relabel legend from the 'fill' in the geom_bar
   theme_dark() + # different theme 
   theme(panel.grid = element_blank()) # remove the grid lines
```

## Close project

Closing RStudio will ask you if you want to save your workspace and scripts. Saving your workspace is usually not recommended if you have all the necessary commands in your script.

## Useful links

* For visualisations:
  + [ggplot2 cheatsheet](https://www.rstudio.org/links/data_visualization_cheat_sheet)
  + Official [ggplot2 documentation](https://ggplot2.tidyverse.org/reference/)
  + Official [ggplot2 website](https://ggplot2.tidyverse.org/)
  + [Chapter on data visualisation](https://r4ds.hadley.nz/data-visualize.html) in the book _R for Data Science_
  + [From Data to Viz](https://www.data-to-viz.com/), a website to explore different visualisations and the code that generates them
  + Selva Prabhakaran's [_r-statistics.co_ section on ggplot2](https://r-statistics.co/ggplot2-Tutorial-With-R.html)
  + [Coding Club's data visualisation tutorial](https://ourcodingclub.github.io/tutorials/datavis/)
  + [STHDA's ggplot2 essentials](https://www.sthda.com/english/wiki/ggplot2-essentials)
  + Lear more about plotly and exploratory data analysis with the book _[Interactive web-based data visualization with R, plotly, and shiny](https://plotly-r.com/)_
* Our compilation of [general R resources](/R/usefullinks.md)