FundRmentals 07

class: center, middle, inverse, title-slide

# FundRmentals 07
### Dr Danielle Evans | University of Sussex

---

# Setup & Q&A

- Open RStudio
- Open your fundRmentals R Project (click the blue cube in the top right corner of RStudio)
- Open a new Rmd file for today
- Install the **palmerpenguins** package by running the code below in the console:

```r
install.packages("palmerpenguins")
```

- In a new code chunk, load **tidyverse** & **palmerpenguins** using the **library()** command
- In another code chunk, create **peng_data** by copying the below code:

```r
peng_data <- palmerpenguins::penguins
```

.center[

### Any questions from the last tutorial?

]

---

# Overview

- ggplot2

- Scatterplot

- Means plot

- Next steps

---

# ggplot2

- **ggplot2** is a really useful package (part of tidyverse) for data visualisation

- We use the **ggplot2::ggplot()** function to create plots

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

- Plots created with **ggplot2** are *super* customisable, you can make pretty much anything you can imagine

- Because it's pretty much unlimited, there is a huge amount of resources online & there is no need to memorise everything, we can easily look it up!

- Today we're going to try creating a few plots with the **palmerpenguins** data!

---

# ggplot2::ggplot()

- Built layer by layer **+**

- Variables are mapped onto elements of the plot as 'aesthetics'
  + We 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

& many more options...

---

# Plot Building 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

---

# Scatterplots

.panelset[

.panel[.panel-name[Code]

.tiny[

```r
peng_scatter <- ggplot2::ggplot(peng_data, aes(x = bill_length_mm, y = 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()
```

]

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

.panel[.panel-name[Plot]

]

.panel[.panel-name[Code]

.tiny[

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

]

We can make it more *fancy* by changing the **colour**, **size**, **shape** & **alpha** (transparency) of the points, & by adding a regression line (the **geom_smooth** layer)

**Task**: Edit your previous scatterplot by changing the different options for displaying the points & add a regression line

]

.panel[.panel-name[Plot]

]]

---

# Means Plots

.panelset[

.panel[.panel-name[Code]

.tiny[

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

]

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

]

.panel[.panel-name[Plot]

]]

???

n the stat_summary() function, we’re asking  to calculate the means (fun = “mean”). The argument geom = “point” asks ggplot2 to display the means as dots using geom_point().

---

# Adding CIs

.panelset[

.panel[.panel-name[Code]

.tiny[

```r
peng_means <- ggplot2::ggplot(peng_data, aes(x = species, y = flipper_length_mm))
peng_means +
 stat_summary(fun.data = "mean_cl_normal", geom = "pointrange", colour = "orange") + 
 labs(title = "Mean Flipper Length per Penguin Species", x = "Species", y = "Flipper Length (mm)") + 
 theme_minimal()
```

]

**Task**: Edit our means plot to display confidence intervals

]

.panel[.panel-name[Plot]

]]

---

# Changing the Coordinate System

.panelset[

.panel[.panel-name[Code]

.tiny[

]

**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)

]

.panel[.panel-name[Plot]

]]

---

# Top Tips

- 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 **+** for each layer  
- Set code chunk options to echo = FALSE (plot is displayed but the code isn't):

### Next Steps

Save the RMarkdown document from today **Ctrl + S** or **Command + S**

For next week you should complete the **discovr_09** tutorial on comparing means by pasting the below code into the **Console** in RStudio, pressing **Enter**

```r
learnr::run_tutorial("discovr_09", package = "discovr")
```