Plotting I

# Plotting I
### Introduction to R <a href='https://therbootcamp.github.io'> Bern R Bootcamp </a> <a href='https://dwulff.github.io/Intro2R_Unibe/'> </a>  <a href='https://therbootcamp.github.io'> </a>  <a href='mailto:therbootcamp@gmail.com'> </a>  <a href='https://www.linkedin.com/company/basel-r-bootcamp/'> </a>
### June 2020

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 R Bootcamp Bern | June 2020
 
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.pull-left4[
 
> ### As good as R is for statistics, it's as good if not better for data visualisation
> ### Nathaniel D. Phillips
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<img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/ggplotgallery.png" width="100%" style="display: block; margin: auto;" />

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# Base R Plotting

The <high>classic framework</high> of plotting.

Contains separate <high>function for each 'type'</high> of plot.

E.g. `barplot()` for a bar plot, `boxplot()` for a box plot, and `plot()` for a scatterplot.

```r
# Histogram in base R
hist(x = baselers$age,
     xlab = "Age",
     ylab = "Frequency",
     main = "Baselers Age")
```

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<img src="PlottingI_files/figure-html/unnamed-chunk-5-1.png" style="display: block; margin: auto;" />

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# Base R Plotting

The <high>classic framework</high> of plotting.

Contains separate <high>function for each 'type'</high> of plot.

E.g. `barplot()` for a bar plot, `boxplot()` for a box plot, and `plot()` for a scatterplot.

```r
# Boxplot in base R
boxplot(formula = height ~ sex,
        data = baselers,
        xlab = "Sex",
        ylab = "Height",
        main = "Box plot")
```

]

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<img src="PlottingI_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" />

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---

# Base R Plotting

The <high>classic framework</high> of plotting.

Contains separate <high>function for each 'type'</high> of plot.

E.g. `barplot()` for a bar plot, `boxplot()` for a box plot, and `plot()` for a scatterplot.

```r
# Scatterplot in base R
plot(x = baselers$height,
     y = baselers$income,
     xlab = "Height",
     ylab = "Income",
     main = "Scatterplot")
```

]

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<img src="PlottingI_files/figure-html/unnamed-chunk-9-1.png" style="display: block; margin: auto;" />

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---

# Problems with Base R plotting

- Default plots look pretty <high>outdated</high>.

- Plots can quickly require a <high>LOT of code</high>.

- Can't store plots as <high>objects</high> to reference and update later

<high>Solution: `ggplot2` </high>

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This plot would take <high>a lot of code in Base R</high> but <high>just 10 lines of code</high>, 5 of which controlling the labels, in `ggplot2`.

]

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# Grammar of Graphics in `ggplot2`

The <high>Grammar of graphics</high> breaks down plots into several key pieces:

| Aesthetics| Description|
|:------|:----|
| Data| What dataframe contains the data?|
| axes| What does the x-axis, y-axis, color (etc) represent?|
| color| What does color represent? |
| size | What does size represent? |
| geometries| What kind of geometric object do you want to plot?|
| facets| Should there be groups of plots?|

]

]

---

# Our goal: Creating this plot

- Use the `mpg` tibble

<high>Aesthetics</high>

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

<high>Geometric objects</high>

- Show data as points
- Add a regression line

<high>Labels and themes</high>

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

]

]

---

# `ggplot()`

To <high>create a ggplot2 object</high>, use the `ggplot()` function

`ggplot()` has two main arguments:

- `data` - A data frame (aka `tibble`)
- `mapping` - A call to `aes()`

]

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

]

---

# `ggplot()`

An <high>aesthetic mapping</high> is a visual property of the objects in your plot.

Use `aes()` to assign columns in your dataframe to properties in your plot.

Common aesthetics are...

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

]

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

]

---

# Adding elements to plots with `+`

Once you have specified the `data` argument, and global aesthetics with `mapping = aes()`, <high>add additional elements to the plot with `+`</high>.

The `+` operator works just like the pipe `%>%` in `dplyr`. <high>It just means "and then..."</high>

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

]

]

---

# Geometric objects (`geom`)

A <high>`geom`</high> 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 are...

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

]

]

---

## `geom_boxplot()`

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

]

## `geom_violin()`

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

]

---

## `geom_bar()`

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

]

## `geom_count()`

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

]

---

# `aes()`

<high>`color`</high> geoms according to a variable.

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

`mpg`

| displ| hwy|class   | year|
|-----:|---:|:-------|----:|
|   3.0|  26|compact | 1999|
|   4.7|  12|suv     | 2008|
|   2.8|  26|compact | 1999|
|   4.7|  15|suv     | 1999|
|   4.0|  19|suv     | 1999|

]

]

---

# What's next?

]

]

---

# `geom_smooth()`

`geom_smooth()` adds a <high>smoothed line</high>.

Change how the line is created with `method` (e.g., `method = lm`).

Color the line with `col`.

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

]

]

---

# `geom_smooth()`

`geom_smooth()` adds a <high>smoothed line</high>.

Change how the line is created with `method` (e.g., `method = lm`).

Color the line with `col`

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

]

]

---

# Overriding aesthetics

If you add additional plotting aesthetics, they will <high>override</high> the general plotting aesthetics.

This is what happens, when you don't override...

```r
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy,
                     col = class)) +
  geom_point() + 
  geom_smooth() # no overriding
```

]

]

---

# What's next?

]

]

---

# `labs()`

You can add <high>labels</high> to a plot with the `labs()` function

`labs()` arguments are ...

- `title` - Main title
- `subtitle` - Subtitle
- `caption` - Caption below

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

]

]

---

# What's next?

]

]

---

# Themes with `theme_XX()`

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

Themes built into `ggplot2`

`theme_bw()` 
`theme_minimal()` 
`theme_classic()` 
`theme_light()` 
`theme_void()`

Themes from the `ggthemes` package

`theme_excel()` 
`theme_economist()` 
etc.

]

```r
ggplot(...) + 
  theme_gray() # The Default theme
```

]

---

# Themes with `theme_XX()`

Themes built into `ggplot2`

`theme_bw()` 
`theme_minimal()` 
`theme_classic()` 
`theme_light()` 
`theme_void()`

Themes from the `ggthemes` package

`theme_excel()` 
`theme_economist()` 
etc.

]

```r
ggplot(...) + 
  theme_light()
```

]

---

# Themes with `theme_XX()`

Themes built into `ggplot2`

`theme_bw()` 
`theme_minimal()` 
`theme_classic()` 
`theme_light()` 
`theme_void()`

Themes from the `ggthemes` package

`theme_excel()` 
`theme_economist()` 
etc.

]

```r
ggplot(...) + 
  theme_void()
```

]

---

# Themes with `theme_XX()`

Themes built into `ggplot2`

`theme_bw()` 
`theme_minimal()` 
`theme_classic()` 
`theme_light()` 
`theme_void()`

Themes from the `ggthemes` package

`theme_excel()` 
`theme_economist()` 
etc.

]

```r
ggplot(...) + 
  theme_excel()
```

]

---

# Themes with `theme_XX()`

Themes built into `ggplot2`:

`theme_bw()` 
`theme_minimal()` 
`theme_classic()` 
`theme_light()` 
`theme_void()`

Themes from the `ggthemes` package

`theme_excel()` 
`theme_economist()` 
etc.

]

```r
ggplot(...) + 
  theme_economist()
```

]

---

# 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 Displ. in Liters",
       y = "Highway miles per gallon",
       title = "MPG data",
       subtitle = "Cars with higher...",
       caption = "Source: mpg data...") + 
  theme_bw()
```

]

]

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<h1><a href="https://dwulff.github.io/Intro2R_Unibe/_sessions/PlottingI/PlottingI_practical.html">Practical</a></h1>