Essentials 06: Data Visualisation with ggplot2

# Essentials 06: Data Visualisation with ggplot2

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# Session Objectives

### Data Visualisation with ggplot2

- Introduction to ggplot2 
- Boxplot  
- Grouped Boxplot  
- Scatterplot  
- Means plot

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# Setup

### .orange[Tasks]:

1. Open the slides for today

1. Open/create your seminRs project

1. Download the [Rmd document](https://and.netlify.app/seminr/06/essentials/essentials_06.Rmd) to the r_docs folder of your seminRs project & open it

You can [download the Solutions Rmd](https://and.netlify.app/seminr/06/essentials/essentials_solutions_06.Rmd) to check your answers later

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# Intro to ggplot2

Figures/graphs/plots are a useful way to look at your data

- Quickly see general trends of data with minimal effort

- Easy check of data

- Gives readers a general overview of your results/data

<br>

ggplot2 is a really useful package for data visualisation

- Part of tidyverse

- Follows .italic[The Grammar of Graphics]

- Plots are built in layers from 3 key components: data, coordinate system, & geometric elements (geoms)

- Super customisable

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# Using ggplot()

- Built layer by layer +

- Variables are .italic[mapped] onto elements of the plot as 'aesthetics'  
  - Use aes() to include variable(s) as an aesthetic

- Geoms are 'visual marks' that represent data points e.g.

- geom_bar() – creates a layer of bars  
  - geom_point() – creates a layer of data points  
  - geom_histogram() – creates a layer with a histogram  
  - geom_text() – creates layer with text on

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# General Process

1. Create base layer by specifying the data & the variables to map onto the plot

1. Add geom layer (to display our data points)

1. Add/edit visual properties (scale, colours, shapes etc.)

1. Add semantic properties (e.g. title, labels)

1. Add a theme to make our plot pretty

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# Basic Boxplot

```r
peng_boxplot <- ggplot2::ggplot(peng_data, aes(island, bill_length_mm))
peng_boxplot +
  geom_boxplot() +
  labs(title = "Penguin Bill Length Split by Island", x = "Island", y = "Bill Length (mm)") + 
  theme_classic()
```

### .orange[Task]:

Create a boxplot of peng_data with sex on the x axis & flipper_length_mm on the y axis

]

]]

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# Grouped Boxplot

```r
peng_boxplot_2 <- ggplot2::ggplot(peng_data, aes(island, bill_length_mm, fill = sex))
peng_boxplot_2 +
  geom_boxplot() +  
  labs(title = "Penguin Bill Length Split by Island and Sex", x = "Island", y = "Bill Length (mm)", fill = "Sex") + 
  theme_minimal()
```

### .orange[Task]:

Create a grouped boxplot of peng_data with sex on the x axis, flipper_length_mm on the y axis, & grouped by species

]

]]

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# Scatterplots

```r
peng_scatter <- ggplot2::ggplot(peng_data, aes(bill_length_mm, bill_depth_mm))
peng_scatter +
  geom_point() +
  labs(title = "Scatterplot of Bill Length & Bill Depth", x = "Bill Length (mm)", y = "Bill Depth (mm)") + 
  theme_bw()
```

]

```r
peng_scatter_2 <- ggplot2::ggplot(peng_data, aes(bill_length_mm, bill_depth_mm))
peng_scatter_2 +
  geom_point(colour = "dark blue", size = 3, shape = 20, alpha = .5) +
  labs(title = "Scatterplot of Bill Length & Bill Depth", x = "Bill Length (mm)", y = "Bill Depth (mm)") + 
  theme_bw()
```

### .orange[Task]:

Create a scatterplot of peng_data with body_mass_g on the x axis, flipper_length_mm on the y axis

]

]]

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# Means Plots

```r
peng_means <- ggplot2::ggplot(peng_data, aes(species, flipper_length_mm))
peng_means +
  stat_summary(fun = "mean", geom = "point", size = 4, colour = "pink") +  
  labs(title = "Mean Flipper Length per Penguin Species", x = "Species", y = "Flipper Length (mm)") + 
  theme_minimal()
```

### .orange[Task]:

Create a means plot of peng_data with island on the x axis, body_mass_g on the y axis

]

]]

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# Don't Be Misleading!

]

<br>

]

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# Changing the Coordinate System

<br>

### .orange[Task]:

Using the means plot in the previous task, alter the y axis & add appropriate scale breaks (use your judgement here for what would be appropriate)

]

]]

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# General Tips

Think about what makes a good graph when creating your own:

- Clear & tidy    
- Simple – presents a lot with a little!  
- Coherent  
- Accurate – doesn’t distort results  
- Informative captions/titles & labels
- Is referred to in the text ("see Figure 1")

### To Avoid Problems

- Run & inspect every change you make  
- Be aware that creating plots with ggplot is often a long process   
- A really common error is forgetting the .orange[+] for each layer  
- Set code chunk options to echo = FALSE (plot is displayed but the code isn't)

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