Data Visualization

.leftcol30[
<center>
<img src="https://github.com/emse-p4a-gwu/emse-p4a-gwu.github.io/raw/master/images/p4a_hex_sticker.png" width=250>
</center>
]

### <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="M496 128v16a8 8 0 0 1-8 8h-24v12c0 6.627-5.373 12-12 12H60c-6.627 0-12-5.373-12-12v-12H24a8 8 0 0 1-8-8v-16a8 8 0 0 1 4.941-7.392l232-88a7.996 7.996 0 0 1 6.118 0l232 88A8 8 0 0 1 496 128zm-24 304H40c-13.255 0-24 10.745-24 24v16a8 8 0 0 0 8 8h464a8 8 0 0 0 8-8v-16c0-13.255-10.745-24-24-24zM96 192v192H60c-6.627 0-12 5.373-12 12v20h416v-20c0-6.627-5.373-12-12-12h-36V192h-64v192h-64V192h-64v192h-64V192H96z"/></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="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"/></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="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> April 07, 2022
]

---

# Quiz 6

## Go to `#class` channel in Slack for quiz link

## Open RStudio first!

## Rules:

- You may use your notes and RStudio
- You may **not** use any other resources (e.g. the internet, your classmates, etc.)

]

]

---

# Before we start

Make sure you have the "tidyverse" installed and loaded, and import these two data frames

```r
library(tidyverse)
library(here)

birds <- read_csv(here('data', 'wildlife_impacts.csv'))
bears <- read_csv(here('data', 'bear_killings.csv'))
```

(this is at the top of the `notes-blank.R` file)

---

# The Challenger disaster

On January 28, 1986 the space shuttle Challenger exploded

]

]

---

# The Challenger disaster

NASA Engineers had the data on temperature & o-ring failure

]

]

---

## What NASA was shown

]

Tufte, Edward R. (1997) _Visual Explanations: Images and Quantities, Evidence and Narrative_, Graphics Press, Cheshire, Connecticut.

]]

---

# What NASA _should_ have been shown

Tufte, Edward R. (1997) _Visual Explanations: Images and Quantities,<br> Evidence and Narrative_, Graphics Press, Cheshire, Connecticut.

]

---

# Week 11: .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 11: .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 <- seq(1, 5)
var2 <- 2*var1
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
```

---

# Scatterplots with `plot()`

### Plotting variables from a data frame:

Plot `year` vs. `age`:

```r
plot(x = bears$year, y = bears$age)
```
]

]

---

# 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 11: .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

---

## Advanced 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 11: .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 11: .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