Data Visualization

]

# Week 10: .fancy[Data Visualization]

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8.1-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zm128-96c-17.7 0-32-14.3-32-32s14.3-32 32-32s32 14.3 32 32s-14.3 32-32 32z"/></svg> EMSE 4571: Intro to Programming for Analytics
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M272 304h-96C78.8 304 0 382.8 0 480c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32C448 382.8 369.2 304 272 304zM48.99 464C56.89 400.9 110.8 352 176 352h96c65.16 0 119.1 48.95 127 112H48.99zM224 256c70.69 0 128-57.31 128-128c0-70.69-57.31-128-128-128S96 57.31 96 128C96 198.7 153.3 256 224 256zM224 48c44.11 0 80 35.89 80 80c0 44.11-35.89 80-80 80S144 172.1 144 128C144 83.89 179.9 48 224 48z"/></svg> John Paul Helveston
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M152 64H296V24C296 10.75 306.7 0 320 0C333.3 0 344 10.75 344 24V64H384C419.3 64 448 92.65 448 128V448C448 483.3 419.3 512 384 512H64C28.65 512 0 483.3 0 448V128C0 92.65 28.65 64 64 64H104V24C104 10.75 114.7 0 128 0C141.3 0 152 10.75 152 24V64zM48 448C48 456.8 55.16 464 64 464H384C392.8 464 400 456.8 400 448V192H48V448z"/></svg> April 13, 2023

]

---

# Week 10: .fancy[Data Visualization]

### 1. Plotting with Base R
### 2. Plotting with ggplot2: Part 1

### BREAK

### 3. Plotting with ggplot2: Part 2
### 4. Tweaking your ggplot

---

# Week 10: .fancy[Data Visualization]

### 1. .orange[Plotting with Base R]
### 2. Plotting with ggplot2: Part 1

### BREAK

### 3. Plotting with ggplot2: Part 2
### 4. Tweaking your ggplot

---

# Today's data:

## Bear attacks in North America

Explore the `bears` data frame:

```r
glimpse(bears)
head(bears)
```

---

## Two basic plots in R

### Scatterplots

]

### Histograms

]

---

# Scatterplots with `plot()`

### Plot relationship between two variables

General syntax:

```r
plot(x = x_vector, y = y_vector)
```

]

---

# Scatterplots with `plot()`

### Plot relationship between two variables

General syntax:

```r
plot(x = x_vector, y = y_vector)
```

Example:

```r
var1 <- bears$year
var2 <- bears$age
plot(x = var1, y = var2)
```

]

]

---

# Scatterplots with `plot()`

### `x` and `y` must have the same length!

```r
var2 <- var2[-1]
```

```r
length(var1) == length(var2)
```

```
#> [1] FALSE
```

```r
plot(x = var1, y = var2)
```

```
#> Error in xy.coords(x, y, xlabel, ylabel, log): 'x' and 'y' lengths differ
```

---

# Making `plot()` pretty

```r
plot(
  x    = bears$year,
  y    = bears$age,
  col  = 'darkblue', # Point color
  pch  = 19, # Point shape
  main = "Age of victims over time",
  xlab = "Year",
  ylab = "Age of victim"
)
```

]]

]

---

## Your turn: `plot()`

Does the annual number of bird impacts appear to be changing over time?

Make a plot using the `birds` data frame to justify your answer.

<br>

Hint: You may need to create a _summary_ data frame to answer this question!

**Bonus**: Make your plot pretty!

---

# Histograms with `hist()`

### Plot the _distribution_ of a single variable

General syntax:

```r
hist(x = x_vector)
```

]

---

# Histograms with `hist()`

### Plot the _distribution_ of a single variable

General syntax:

```r
hist(x = x_vector)
```

Example:

```r
hist(bears$month)
```

]

]

---

# Making `hist()` pretty

```r
hist(
  x      = bears$month,
  breaks = 12,
  col    = 'darkred',
  main   = "Bear killings by month",
  xlab   = "Month",
  ylab   = "Count"
)
```

]]

]

---

## Your turn: `hist()`

Make plots using the `birds` data frame to answer these questions

1. Which months have the highest and lowest number of bird impacts in the dataset?
2. Which aircrafts experience more impacts: 2-engine, 3-engine, or 4-engine?
3. At what height do most impacts occur?

**Bonus**: Make your plots pretty!

---

# Week 10: .fancy[Data Visualization]

### 1. Plotting with Base R
### 2. .orange[Plotting with ggplot2: Part 1]

### BREAK

### 3. Plotting with ggplot2: Part 2
### 4. Tweaking your ggplot

---

## Better figures with `ggplot2`

<center>
<img src="images/horst_monsters_ggplot2.png" width=600>
</center>Art by [Allison Horst](https://www.allisonhorst.com/)

---

]

# "Grammar of Graphics"

Concept developed by Leland Wilkinson (1999)

**ggplot2** package developed by Hadley Wickham (2005)

]

---

# Making plot layers with ggplot2

<br>

### 1. The data (we'll use `bears`)
### 2. The aesthetic mapping (what goes on the axes?)
### 3. The geometries (points? bars? etc.)

---

# Layer 1: The data

The `ggplot()` function initializes the plot with whatever data you're using

```r
ggplot(data = bears)
```

]

]]

---

# Layer 2: The aesthetic mapping

The `aes()` function determines which variables will be _mapped_ to the geometries<br>(e.g. the axes)

```r
ggplot(
  data = bears,
* mapping = aes(x = year, y = age))
```
]

]]

---

# Layer 3: The geometries

Use `+` to add geometries (e.g. points)

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
* geom_point()
```

]

]]

---

# Other common geometries

- `geom_point()`: scatter plots
- `geom_line()`: lines connecting data points
- `geom_col()`: bar charts
- `geom_boxplot()`: boxes for boxplots

---

# Scatterplots with `geom_point()`

Add points:

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
* geom_point()
```

]

]]

---

# Scatterplots with `geom_point()`

Change the color of all points:

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
* geom_point(color = 'blue')
```

]

]]

---

# Scatterplots with `geom_point()`

Map the point color to a **variable**:

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
* geom_point(aes(color = gender))
```

Note that `color = gender` is _inside_ `aes()`

]

]]

---

# Scatterplots with `geom_point()`

Adjust labels with `labs()` layer:

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point(aes(color = gender)) +
  labs(
*   x = "Year",
*   y = "Age",
*   title = "Bear victim age over time",
*   color = "Gender")
```

]

]]

---

## Your turn: `geom_point()`

Use the `birds` data frame to create the following plots

]

]

---

# .fancy[Break]

---

# Week 10: .fancy[Data Visualization]

### 1. Plotting with Base R
### 2. Plotting with ggplot2: Part 1

### BREAK

### 3. .orange[Plotting with ggplot2: Part 2]
### 4. Tweaking your ggplot

---

## Make bar charts with `geom_col()`

With bar charts, you'll often need to create summary variables to plot

Step 1: Summarize the data

```r
bear_months <- bears %>%
* count(month)
```

Step 2: Make the plot

```r
ggplot(data = bear_months) +
* geom_col(aes(x = month, y = n))
```

]

Example: count of attacks by month

]

---

## Make bar charts with `geom_col()`

Alternative approach: piping directly into ggplot

```r
bears %>%
* count(month) %>%  # Pipe into ggplot
  ggplot() +
  geom_col(aes(x = month, y = n))
```

]

]

---

## Be careful with `geom_col()` vs. `geom_bar()`

### .center[`geom_col()`]

Map both `x` and `y`

```r
bears %>%
* count(month) %>%
  ggplot() +
* geom_col(aes(x = month, y = n))
```

]

### .center[`geom_bar()`]

Only map `x` (`y` is computed)

```r
bears %>%
  ggplot() +
* geom_bar(aes(x = month))
```

]

---

## Make bar charts with `geom_col()`

Another example:<br>Mean age of victim in each year

```r
bears %>%
  filter(!is.na(age)) %>%
  group_by(year) %>%
* summarise(meanAge = mean(age)) %>%
  ggplot() +
* geom_col(aes(x = year, y = meanAge))
```

]

]]

---

### Change bar width:  `width`
### Change bar color: `fill`
### Change bar outline: `color`

```r
bears %>%
  count(month) %>%
  ggplot() +
  geom_col(
    mapping = aes(x = month, y = n),
*   width = 0.7,
*   fill = "blue",
*   color = "red"
  )
```

]

]

---

### Map the `fill` to `bearType`

```r
*bears %>%
* count(month, bearType) %>%
  ggplot() +
  geom_col(
    mapping = aes(
      x = month, y = n, fill = bearType)
  ) 
```

]

Note that I had to summarize the count by both `month` and `bearType`

```r
bears %>%
  count(month, bearType)
```

]

```
#> # A tibble: 27 × 3
#>    month bearType     n
#>    <dbl> <chr>    <int>
#>  1     1 Brown        1
#>  2     1 Polar        2
#>  3     2 Brown        1
#>  4     3 Brown        1
#>  5     4 Black        1
#>  6     4 Brown        3
#>  7     5 Black       15
#>  8     5 Brown        2
#>  9     5 Polar        1
#> 10     6 Black       10
#> # … with 17 more rows
```

]]

]

---

# "Factors" = Categorical variables

By default, R makes numeric variables _continuous_

```r
bears %>%
  count(month) %>%
  ggplot() +
  geom_col(aes(x = month, y = n))
```
**The variable `month` is a _number_**

]

]]

---

# "Factors" = Categorical variables

You can make a continuous variable _categorical_ using `as.factor()`

```r
bears %>%
  count(month) %>%
  ggplot() +
  geom_col(
    mapping = aes(
*     x = as.factor(month),
      y = n)
  )
```

]

**The variable `month` is a _factor_**

]

]]

---

## Your turn: `geom_col()`

Use the `bears` and `birds` data frame to create the following plots

]

]

---

# Week 10: .fancy[Data Visualization]

### 1. Plotting with Base R
### 2. Plotting with ggplot2: Part 1

### BREAK

### 3. Plotting with ggplot2: Part 2
### 4. .orange[Tweaking your ggplot]

---

# Working with themes

Themes change _global_ features of your plot, like the background color, grid lines, etc.

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point()
```

]

]

---

# Working with themes

Themes change _global_ features of your plot, like the background color, grid lines, etc.

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_bw()
```

]

]

---

### Common themes

`theme_bw()`

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_bw()
```

]

`theme_minimal()`

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_minimal()
```

]

---

### Common themes

`theme_classic()`

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_classic()
```

]

`theme_void()`

```r
ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_void()
```

]

---

### Other themes: [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes)

```r
*library(hrbrthemes)

ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_ipsum()
```

]

```r
*library(hrbrthemes)

ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_ft_rc()
```

]

---

### Other themes: **ggthemes**

```r
*library(ggthemes)

ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_economist()
```

]

```r
*library(ggthemes)

ggplot(
  data = bears,
  mapping = aes(x = year, y = age)) +
  geom_point() +
* theme_economist_white()
```

]

---

# Save figures with `ggsave()`

First, assign the plot to an object name:

```r
scatterPlot <- ggplot(data = bears) +
    geom_point(aes(x = year, y = age))
```

Then use `ggsave()` to save the plot:

```r
ggsave(
    filename = here('plots', 'scatterPlot.png'),
    plot   = scatterPlot,
    width  = 6, # inches
    height = 4)
```

---

# Extra practice 1

Use the `mtcars` data frame to create the following plots

]

]

]

---

# Extra practice 2

Use the `mpg` data frame to create the following plot