Plotting

class: center, middle, inverse, title-slide

# Plotting
## With ggplot2
### The R Bootcamp Twitter: <a href='https://twitter.com/therbootcamp'>@therbootcamp</a>
### April 2018

---

# You can do amazing plots in R!

- As good as R is for statistics, it's as good if not better for plots.

---

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## Base R Plotting

There are different frameworks for doing plotting in R.

The classic framework is known as Base R plotting.

In Base R plotting, there is a separate function for each 'type' of plot

| Plot type| Function|
|:------|:----|
|     Bar plot| `barplot()`|
|     Box plot|   `boxplot()`|
|     Scatterplot|    `plot()`|

]

.pull-right5[

```r
# Histogram in base R

hist(x = ChickWeight$weight,
     xlab = "Weight",
     ylab = "Frequency",
     main = "Histogram in Base R")
```

]

---

.pull-left45[

## Base R Plotting

There are different frameworks for doing plotting in R.

The classic framework is known as Base R plotting.

In Base R plotting, there is a separate function for each 'type' of plot

| Plot type| Function|
|:------|:----|
|     Bar plot| `barplot()`|
|     Box plot|   `boxplot()`|
|     Scatterplot|    `plot()`|

]

.pull-right5[

```r
# Boxplot in base R

boxplot(formula = cty ~ trans,
        data = mpg,
        xlab = "Transmission",
        ylab = "City miles per gallon",
        main = "Boxplot in Base R")
```

]

---

.pull-left45[

## Base R Plotting

There are different frameworks for doing plotting in R.

The classic framework is known as Base R plotting.

In Base R plotting, there is a separate function for each 'type' of plot

| Plot type| Function|
|:------|:----|
|     Bar plot| `barplot()`|
|     Box plot|   `boxplot()`|
|     Scatterplot|    `plot()`|

]

.pull-right5[

```r
# Scatterplot in base R

plot(x = economics$date,
     y = economics$unemploy,
     xlab = "Date",
     ylab = "Unemployment rate",
     main = "Scatterplot in Base R"
     )
```

]

---

.pull-left5[
## Problems with Base R plotting
 
Complex plots can quickly require a LOT of code.

Default plots look pretty outdated

No way to set plotting themes

Can't store plots as objects to reference and update later

]

.pull-right5[
 
This plot would take a lot of code in Base R

]

---
## ggplot2

.pull-left45[
 
How do we make elegant, easy to program plots according to the grammar of graphics in R?

Answer: ggplot2

One of the most widely used R packages, used to generate the vast majority of plots from R.

]

.pull-right5[

]

---

## Grammar of Graphics

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1. A plot is built of simple building blocks

2. By combining different building blocks, plots of any complexity can be created.

3. Plots that look superficially different, can actually be created with very similar code.

]

.pull-right5[

]

---

## Grammar of Graphics

.pull-left5[

The Grammar of graphics breaks down plots into several key pieces:

| aesthetics| Description|
|:------|:----|
|     Data|    What dataframe contains the data?|
|     Aesthetics| What does the x-axis, y-axis, color (etc) represent?|
|     Geometries| What kind of geometric object do you want to plot?|
|     Facets| Should there be groups of plots?|
|     Statistics|What statistic summaries / transformations should be done?|
|    Coordinates| What is the scale of the axes?|
|    Theme| What should the overall plot look like?|

]

.pull-right5[
<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/complexplot1.png" width="100%" style="display: block; margin: auto;" />

]

---
## Our goal

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- In this introduction, we'll introduce the basic building blocks of creating plots with ggplot2 by creating the following plot from the ground-up:

]

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### In the practical, you will create all of these!!

]

---
## ggplot2

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]

.pull-right4[

### Load the ggplot2 package

```r
# Load the tidyverse (includes ggplot2)
library(tidyverse)
```

### Or

```r
# Load ggplot2 directly
library(ggplot2)
```

]

---
## `mpg` data

.pull-left6[

The `mpg` data is a tibble of car data contained in the `ggplot2` package

|manufacturer |model              | cty| hwy|class      |
|:------------|:------------------|---:|---:|:----------|
|land rover   |range rover        |  11|  15|suv        |
|hyundai      |sonata             |  19|  28|midsize    |
|toyota       |camry              |  21|  31|midsize    |
|jeep         |grand cherokee 4wd |  15|  20|suv        |
|chevrolet    |c1500 suburban 2wd |  14|  20|suv        |
|ford         |mustang            |  15|  23|subcompact |
|volkswagen   |new beetle         |  29|  41|subcompact |
|ford         |expedition 2wd     |  11|  17|suv        |

]

.pull-right3[

]

---
## Creating this plot

.pull-left5[

**Data**

- Use the `mpg` tibble

**Aesthetics**

- Show engine displacement (`disp`) on the x axis
- Show highway miles per gallon (`hwy`) on the y-axis
- Color plotting elements by the class of car (`class`)

**Geometric objects**

- Show data as points.
- Add a regression line

**Labels and themes**

- Add plotting labels
- Use a black and white plotting theme

]

.pull-right5[

Our goal is to build the following plot step by step:

]

---
## ggplot(data)

.pull-left5[

- To create a ggplot2 object, use the `ggplot()` function.

| argument| description|
|:------|:----|
|     `data`|    A dataframe (or tibble)|
|     `mapping`|A call to aes()|

- Including only a `data` argument returns a blank plotting space, because we haven't specified any plotting *aesthetics* or *geometric objects*

]

.pull-right5[

```r
ggplot(data = mpg)
```

]

---
## ggplot(mapping = aes())

.pull-left4[

- An **aesthetic mapping** is a visual property of the objects in your plot.

### Common aesthetics

| aesthetics| Description|
|:------|:----|
|     `x`, `y`|    Data mapped to coordinates|
|     `color`, `fill`|    Border and fill colors|
|     `alpha`| Transparency|
|    `size`|  Size|
|    `shape`|  Shape|

- Add aesthetics with the `aes()` function
- Arguments can be columns in your dataframe, or in some cases fixed (i.e.; string or integer) values

]

.pull-right5[

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy))
```

]

---
## Adding elements to plots with '+'

.pull-left45[

- Once you have specified data with `data` argument, and global aesthetics with `mapping = aes()`, you can add additional elements to the plot with `+`

- The `+` sign works just like the pipe `%>%` in dplyr. It just means "and then..."

```r
ggplot(data = mpg,
       mapping = aes(x = displ, 
                     y = hwy)) +  # and then...
       # MORE    + (and then...)
       # MORE    + (and then...)
```

]

.pull-right5[

### ggplot uses `+` to add additional elements to a plot

]

---
## Geometric objects (geoms)

.pull-left4[

- A **geom** is a geometric object in a plot that represents data
- To add a geom to a plot, just include ` + geom_X()` where X is the type of geom.

### Common geoms

| geom| output|
|:------|:----|
|     `geom_point()`|    Points|
|     `geom_bar()`|    Bar|
|     `geom_boxplot()`| Boxplot
|     `geom_count()`| Points with size reflecting frequency|
|     `geom_smooth()`|  Smoothed line|

]

.pull-right5[

### Add a point geom with `geom_point()`

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) + 
  geom_point()
```

]

---

.pull-left45[

### geom_boxplot()

```r
ggplot(data = mpg,
  mapping = aes(x = class, y = hwy, 
                fill = class)) + 
  geom_boxplot()
```

]

.pull-right45[

### geom_violin()

```r
ggplot(data = mpg,
  mapping = aes(x = class, y = hwy, 
                fill = class)) + 
  geom_violin()
```

]

---

.pull-left45[

### geom_bar()

```r
ggplot(data = mpg,
  mapping = aes(x = class, 
                fill = class)) + 
  geom_bar()
```

]

.pull-right45[

### geom_count()

```r
ggplot(data = mpg,
  mapping = aes(x = displ, y = hwy)) + 
  geom_count()
```

]

---

.pull-left45[

### geom_density()

```r
ggplot(data = mpg,
  mapping = aes(x = hwy, 
                fill = class)) + 
  geom_density()
```

]

.pull-right45[

### geom_tile()

```r
ggplot(nba_long, 
       mapping = aes(x = Name, 
                     y = measure, 
                     fill = value)) + 
  geom_tile(col = "white") + 
  scale_fill_gradientn(colors = c("red", 
                                  "white", 
                                  "blue")) + 
  coord_flip()
```

]

---
## Aesthetics

.pull-left45[

> What plotting aesthetic is missing from the code below to make our plot on the right?

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) + 
  geom_point()
```

]

.pull-right5[

]

---
## Color aesthetic

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy, col = class)) +  # Map mpg$class to color aes
  geom_point()   # Add points that respect the aesthetics
```

---
## Geometric objects (geoms)

.pull-left45[

*Code WITHOUT color aesthetic*

```r
ggplot(data = mpg,
       mapping = aes(x = displ, 
                     y = hwy)) + 
  geom_point()
```

]

.pull-right45[

*Code WITH color aesthetic*

```r
ggplot(data = mpg,
       mapping = aes(x = displ, 
                     y = hwy, 
                     col = class)) + 
  geom_point()
```

]

---

### What's next?

.pull-left45[

```r
ggplot(data = mpg,
       mapping = aes(x = displ, 
                     y = hwy, 
                     col = class)) + 
  geom_point()
```

]

.pull-right45[

Our goal

]

---
### Smoothed lines with `geom_smooth()`

.pull-left45[

To add a smoothed line to a plot, use geom_smooth()

Main geom_smooth() arguments

| Arguments| Description|
|:------|:----|
|     method|    What function does the line represent?|
|     col, size, ...|    Other plotting aesthetics|

If you add additional plotting aesthetics, they will override the general plotting aesthetics

]

.pull-right45[

method = "lm"

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point() + 
  geom_smooth(col = "blue")
```

]

---
### Smoothed lines with `geom_smooth()`

.pull-left45[

To add a smoothed line to a plot, use geom_smooth()

Main geom_smooth() arguments

| Arguments| Description|
|:------|:----|
|     method|    What function does the line represent?|
|     col, size, ...|    Other plotting aesthetics|

If you add additional plotting aesthetics, they will override the general plotting aesthetics

]

.pull-right45[

method = "lm"

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point() + 
  geom_smooth(col = "blue",
              method = "lm") # regression
```

]

---
## Overriding aesthetics

- You can include additional aesthetics, like color, shape, and size, in *any* geom.

- This will override the *global* aesthetics

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) + # Global aesthetics
  geom_point() + 
  geom_smooth(col = "blue") # geom_smooth IGNORES global col aesthetic
```

---
## Example A: No overriding

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) + # Global aesthetics
  geom_point() + # geom_point RESPECTS global col aesthetic (class)
  geom_smooth()  # geom_smooth RESPECTS global col aesthetic (class)
```

---
## Example B: Override col aesthetic in geom_smooth()

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) + # Global aesthetics
  geom_point() +            # 
  geom_smooth(col = "blue") # Overrides global col aesthetic (class)
```

---
## Example C: Override col aesthetic in geom_smooth() & geom_point()

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) + # Global aesthetics
  geom_point(col = "pink") + # Overrides global col aesthetic (class)
  geom_smooth(col = "blue")  # Overrides global col aesthetic (class)
```

---
## Example D: Override col aesthetic in geom_point()

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) + # Global aesthetics
  geom_point(col = "pink") + # Overrides global col aesthetic (class)
  geom_smooth()
```

---
## What we want

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point() +
  geom_smooth(col = "blue",  # Overrides global col aesthetic (class)
              method = "lm") # Use lm (linear model) smoothing line
```

---
### What's next?

.pull-left45[

Where we are at.

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, 
                     y = hwy, 
                     col = class)) +
  geom_point() +
  geom_smooth(col = "blue",  
              method = "lm")
```

]

.pull-right45[

Our goal

]

---
## Add labels with `labs()`

.pull-left45[

You can add labels to a plot with the `labs()` function

### `labs()` arguments

| Arguments| Description|
|:------|:----|
|     `title`|Main Title|
|     `subtitle`| Subtitle|
|     `caption`| Caption below plot|

]

.pull-right45[

```r
ggplot(...) +
  labs(x = "Engine Displ...",
       y = "Highway miles...",
       title = "MPG data",
       subtitle = "Cars with ...",
       caption = "Source...")
```

]

---
### What's next?

.pull-left6[

Where are are at

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point(size = 3, alpha = .9) + 
  geom_smooth(col = "blue", method = "lm")+
  labs(x = "Engine...",
       y = "Highway ...",
       title = "MPG data",
       subtitle = "Cars with...",
       caption = "Source:...")
```

]

.pull-right35[

Our goal

]

---
## Themes with `theme_XX()`

.pull-left45[

A plotting *theme* controls many aspects of its overall look, from the background, to the grid lines, to the label font to the spacing between plot labels and the plotting space.

### Common themes

| Themes| 
|:------|
|     `theme_bw()`| 
|     `theme_minimal()`| 
|     `theme_classic()`|
| `theme_light()`|
| `theme_dark()`|

- You can easily add a theme to a plot by including `+ theme_XX()`

]

.pull-right5[

No theme specified

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  geom_smooth(method = "lm")
```

]

---
## Themes with `theme_XX()`

.pull-left45[

A plotting *theme* controls many aspects of its overall look, from the background, to the grid lines, to the label font to the spacing between plot labels and the plotting space.

### Common themes

| Themes| 
|:------|
|     `theme_bw()`| 
|     `theme_minimal()`| 
|     `theme_classic()`|
| `theme_light()`|
| `theme_dark()`|

- You can easily add a theme to a plot by including `+ theme_XX()`

]

.pull-right5[

+ theme_bw()

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  theme_bw()   # Use the black and white theme
```

]

---
## Themes with `theme_XX()`

.pull-left45[

A plotting *theme* controls many aspects of its overall look, from the background, to the grid lines, to the label font to the spacing between plot labels and the plotting space.

### Common themes

| Themes| 
|:------|
|     `theme_bw()`| 
|     `theme_minimal()`| 
|     `theme_classic()`|
| `theme_light()`|
| `theme_dark()`|

- You can easily add a theme to a plot by including `+ theme_XX()`

]

.pull-right5[

+ theme_dark()

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  theme_dark()   # Use the dark theme
```

]

---
## Themes with `theme_XX()`

.pull-left45[

A plotting *theme* controls many aspects of its overall look, from the background, to the grid lines, to the label font to the spacing between plot labels and the plotting space.

### Common themes

| Themes| 
|:------|
|     `theme_bw()`| 
|     `theme_minimal()`| 
|     `theme_classic()`|
| `theme_light()`|
| `theme_dark()`|

- You can easily add a theme to a plot by including `+ theme_XX()`

]

.pull-right5[

+ theme_light()

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  theme_light()   # Use the light theme
```

]

---

.pull-left65[

## Themes

You can customise specific elements of themes in the `theme()` function

```r
ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  theme(plot.background = element_rect(fill = "skyblue"),
        panel.background = element_rect(fill = "skyblue"),
        title = element_text(colour = "white", size = 20),
        axis.title = element_text(colour = "white")) +
    labs(title = "White Title")
```

]

.pull-right3[

Look at the theme help menu with ?theme() to see all possible theme adjustments

]

---
## Set a global theme with theme_set()

.pull-left45[
 
You can set a global theme with the theme_set() function

```r
# Set global theme to minimal
theme_set(theme_bw())

# All fugure plots will now use theme_bw()!
```

When you do, all future plots will use that theme!

]

.pull-right5[

```r
#Set global theme to theme_dark()
theme_set(theme_dark())

ggplot(data = mpg,
       mapping = aes(x = displ, y = hwy)) +
  geom_point()
```

]

---
## Final result!

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, col = class)) +
  geom_point() + 
  geom_smooth(col = "blue", method = "lm")+
  labs(x = "Engine Displacement in Liters",
       y = "Highway miles per gallon",
       title = "MPG data",
       subtitle = "Cars with higher engine displacement tend to have lower highway mpg",
       caption = "Source: mpg data in ggplot2") + 
  theme_bw()
```

---
## Facetting with `facet_wrap()`

.pull-left4[
 
Facetting = Create different plots for different groups

To facet plots, use `facet_wrap()`

```r
# No faceting
ggplot(data = mpg, 
       mapping = aes(x = displ, 
                     y = hwy)) + 
  geom_point()
```

]

.pull-right55[

```r
# With faceting

ggplot(data = mpg, 
       mapping = aes(x = displ, 
                     y = hwy)) + 
  geom_point()  +
  facet_wrap(~ class)  # Tilde first!
```

]

---
## Assigning a ggplot to an object

.pull-left35[

ggplot returns an object of the class "gg"

You can assign the result of `ggplot` to an object.

Evaluating the object will show the plot

You can even edit existing `ggplot` objects

]

.pull-right6[

```r
# Create myplot
myplot <- ggplot(data = mpg,
 aes(x = cty, y = hwy)) + 
 geom_point() + 
 theme_bw()

class(myplot)
```

```
[1] "gg"     "ggplot"
```

```r
myplot # Evaluate myplot
```

]

---
## Assigning ggplot() to an object

.pull-left35[

```r
# Original plot
myplot
```

]

.pull-right6[

```r
# Take the existing plot and add elements
myplot + 
  facet_wrap(~trans) +
  theme_dark()
```

]

---
## Saving plots to image with ggsave()

.pull-left5[

To save plots to a file (e.g.; .jpg, .pdf, .png), use the ggsave() function

|Argument| Definition|
|:-------|:----------|
|`filename`|File name|
|`plot`|Plotting object|
|`device`|File type (e.g.; "pdf", "jpeg", "png")|
|`path`|File path to save plot|
|`width`|Plot width (inches)|
|`height`|Plot height (inches)|

]

.pull-right45[

Save ggplot object called myplot to a pdf file

```r
# Create myplot object
myplot <- ggplot(...)

# Create "myplot.pdf", from myplot
ggsave(filename = "myplot.pdf",
       plot = myplot,
       device = "pdf",
       path = "figures",
       width = 6, 
       height = 4)
```

]

---
## So much more

Check these links for more in depth ggplot2 guides

- ggplot2 main page: http://ggplot2.tidyverse.org/index.html
- Wickham's data visualization guide: http://r4ds.had.co.nz/data-visualisation.html

---

# Cheat Sheet!
https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

---

## Plotting Pratical

<a href="https://therbootcamp.github.io/BaselRBootcamp_2018April/_sessions/D3S2_PlottingI/PlottingI_practical.html">Link to Plotting practical</a>

---