Data and visualisation

.title[
# Data and visualisation
]
.subtitle[
## STAT 7500
]
.author[
### Katie Fitzgerald, adpated from datasciencebox.org
]

---

layout: true
  
<div class="my-footer">
<span>
<a href="https://kgfitzgerald.github.io/stat-7500" target="_blank">kgfitzgerald.github.io/stat-7500</a>
</span>
</div>

---

# Data basics in R

---

# Creating objects

To create an object `x` in your Environment that has the value 5...

``` r
x <- 5 #preferred method

5 -> x #useful in some cases

x = 5 #works but discouraged
```

To print the object...

``` r
x #one of rare instances that doesn't need ()
```

```
## [1] 5
```

``` r
print(x) #works but unnecessary
```

```
## [1] 5
```

---

# Most manipulations in R are done using vectors

+ Vectors are used for (statistical) random variables
+ Vectors are created by combining values using the `c()` function

``` r
height <- c(59, 62, 55, 68)
height/12
```

```
## [1] 4.916667 5.166667 4.583333 5.666667
```

``` r
gender <- c("F","M","F","F")
```

+ For character data, you can use either single or double quotes, but you can’t mix them
+ REMINDER: everything in R, including variable names, is case sensitive

---

# Creating dataframes

Can combine multiple vectors into a data frame
.pull-left[

``` r
height <- c(59, 62, 55, 68)
gender <- c("F","M","F","F")

data <- data.frame(height, gender)
data
```

```
##   height gender
## 1     59      F
## 2     62      M
## 3     55      F
## 4     68      F
```
]

``` r
data2 <- data.frame(height = c(59, 62, 55, 68),
                    gender = c("F","M","F","F"))
data2
```

```
##   height gender
## 1     59      F
## 2     62      M
## 3     55      F
## 4     68      F
```
]

---

# Useful commands for data entry

``` r
60:72
```

```
##  [1] 60 61 62 63 64 65 66 67 68 69 70 71 72
```

``` r
seq(60, 72, by = 2)   
```

```
## [1] 60 62 64 66 68 70 72
```

``` r
seq(60, 70, length = 3) 
```

```
## [1] 60 65 70
```

---

# Extracting data using []

``` r
height <- c(59, 62, 55, 68)
gender <- c("F","M","F","F")
data <- data.frame(height, gender)

height[2]
```

```
## [1] 62
```

``` r
data[2,]
```

```
##   height gender
## 2     62      M
```

``` r
data[,2]
```

```
## [1] "F" "M" "F" "F"
```

---

# Useful commands for data entry

``` r
rep(60, 5)
```

```
## [1] 60 60 60 60 60
```

``` r
rep(c(60, 72), 3)   
```

```
## [1] 60 72 60 72 60 72
```

``` r
rep(c(60, 66, 72), c(1, 2, 3))  
```

```
## [1] 60 66 66 72 72 72
```

---

# Missing data

+ Missing data in R is denoted by NA
+ Some functions produce NA when any data are missing

``` r
mean(c(1, 2, NA))
```

```
## [1] NA
```

``` r
mean(c(1, 2, NA), na.rm = TRUE)
```

```
## [1] 1.5
```

---

# Summary statistics

``` r
summary(data)
```

```
##      height        gender         
##  Min.   :55.0   Length:4          
##  1st Qu.:58.0   Class :character  
##  Median :60.5   Mode  :character  
##  Mean   :61.0                     
##  3rd Qu.:63.5                     
##  Max.   :68.0
```

``` r
data$gender <- factor(data$gender)
summary(data)
```

```
##      height     gender
##  Min.   :55.0   F:3   
##  1st Qu.:58.0   M:1   
##  Median :60.5         
##  Mean   :61.0         
##  3rd Qu.:63.5         
##  Max.   :68.0
```

]

.pull-right[
+ `mean()`
+ `var()`
+ `sd()`
+ `min()`
+ `max()`
+ `length()`
+ `quantile(x, c(0.25, 0.75))`

``` r
table(data$gender)
```

```
## 
## F M 
## 3 1
```

]
---

# Statistical inference

``` r
t.test() #one and two-sample t-tests
chisq.test() #Chi-squared GOF or Indep 
wilcox.test() #signed rank or rank sum test
binom.test() #exact test for binomial/bernoulli
aov() #ANOVA
lm() #linear models
glm() #generalized linear models
lmer() #linear mixed effects models 
```

---

# Your turn!

Week 02 R Practice (Part 1)

Remember you can use the `?` help to read documentation on any 
function you are unfamiliar with.

``` r
?t.test
```

---

# What is in a dataset?

---

## Data frames / Tibbles in R

- Each row is an **observation**
- Each column is a **variable**

``` r
starwars
```

```
## # A tibble: 87 × 14
##   name    height  mass hair_color skin_color eye_color birth_year
##   <chr>    <int> <dbl> <chr>      <chr>      <chr>          <dbl>
## 1 Luke S…    172    77 blond      fair       blue            19  
## 2 C-3PO      167    75 <NA>       gold       yellow         112  
## 3 R2-D2       96    32 <NA>       white, bl… red             33  
## 4 Darth …    202   136 none       white      yellow          41.9
## 5 Leia O…    150    49 brown      light      brown           19  
## 6 Owen L…    178   120 brown, gr… light      blue            52  
## # ℹ 81 more rows
## # ℹ 7 more variables: sex <chr>, gender <chr>, homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>,
## #   starships <list>
```

]

---

## Luke Skywalker

![luke-skywalker](img/luke-skywalker.png)

---

## What's in the Star Wars data?

Take a `glimpse` at the data:

``` r
glimpse(starwars)
```

```
## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth V…
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 1…
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, …
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, gr…
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "lig…
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", …
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, N…
## $ sex        <chr> "male", "none", "none", "male", "female", "m…
## $ gender     <chr> "masculine", "masculine", "masculine", "masc…
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine",…
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human",…
## $ films      <list> <"A New Hope", "The Empire Strikes Back", "…
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <…
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TI…
```

---

.question[
How many rows and columns does this dataset have?
What does each row represent?
What does each column represent?
]

``` r
?starwars
```

---

``` r
nrow(starwars) # number of rows
```

```
## [1] 87
```

``` r
ncol(starwars) # number of columns
```

```
## [1] 14
```

``` r
dim(starwars)  # dimensions (row column)
```

```
## [1] 87 14
```
]

---

# Exploratory data analysis

---

## What is EDA?

- Exploratory data analysis (EDA) is an approach to analysing data sets to summarize its main characteristics
- Often, this is visual -- this is what we'll focus on first
- But we might also calculate summary statistics and perform data wrangling/manipulation/transformation at (or before) this stage of the analysis -- this is what we'll focus on next

---

## Mass vs. height

.question[ 
How would you describe the relationship between mass and height of Starwars characters?
What other variables would help us understand data points that don't follow the overall trend?
Who is the not so tall but really chubby character?
]

---

## Jabba!

---

# Data visualization

---

## Data visualization

> *"The simple graph has brought more information to the data analyst's mind than any other device." --- John Tukey*

- Data visualization is the creation and study of the visual representation of data
- Many tools for visualizing data -- R is one of them
- Many approaches/systems within R for making data visualizations -- **ggplot2** is one of them, and that's what we're going to use

---

## ggplot2 `$\in$` tidyverse

.pull-left[
<img src="img/ggplot2-part-of-tidyverse.png" width="80%" style="display: block; margin: auto;" />
] 
.pull-right[ 
- **ggplot2** is tidyverse's data visualization package 
- `gg` in "ggplot2" stands for Grammar of Graphics 
- Inspired by the book **Grammar of Graphics** by Leland Wilkinson
]

---

## Grammar of Graphics

.pull-left-narrow[
A grammar of graphics is a tool that enables us to concisely describe the components of a graphic
]
.pull-right-wide[
<img src="img/grammar-of-graphics.png" width="100%" style="display: block; margin: auto;" />
]

.footnote[ Source: [BloggoType](http://bloggotype.blogspot.com/2016/08/holiday-notes2-grammar-of-graphics.html)]

---

## Mass vs. height

``` r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

```
## Warning: Removed 28 rows containing missing values or values outside the
## scale range (`geom_point()`).
```

---

.question[ 
- What are the functions doing the plotting?
- What is the dataset being plotted?
- Which variables map to which features (aesthetics) of the plot?
- What does the warning mean?<sup>+</sup>
]

``` r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

```
## Warning: Removed 28 rows containing missing values or values outside the
## scale range (`geom_point()`).
```

---

## Hello ggplot2!

.pull-left-wide[
- `ggplot()` is the main function in ggplot2
- Plots are constructed in layers
- Structure of the code for plots can be summarized as

``` r
ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   other options
```

- The ggplot2 package comes with the tidyverse

``` r
library(tidyverse)
```

- For help with ggplot2, see [ggplot2.tidyverse.org](http://ggplot2.tidyverse.org/)
]

---

# Why do we visualize?

---

## Anscombe's quartet

```
##    set  x     y
## 1    I 10  8.04
## 2    I  8  6.95
## 3    I 13  7.58
## 4    I  9  8.81
## 5    I 11  8.33
## 6    I 14  9.96
## 7    I  6  7.24
## 8    I  4  4.26
## 9    I 12 10.84
## 10   I  7  4.82
## 11   I  5  5.68
## 12  II 10  9.14
## 13  II  8  8.14
## 14  II 13  8.74
## 15  II  9  8.77
## 16  II 11  9.26
## 17  II 14  8.10
## 18  II  6  6.13
## 19  II  4  3.10
## 20  II 12  9.13
## 21  II  7  7.26
## 22  II  5  4.74
```
] 
.pull-right[

```
##    set  x     y
## 23 III 10  7.46
## 24 III  8  6.77
## 25 III 13 12.74
## 26 III  9  7.11
## 27 III 11  7.81
## 28 III 14  8.84
## 29 III  6  6.08
## 30 III  4  5.39
## 31 III 12  8.15
## 32 III  7  6.42
## 33 III  5  5.73
## 34  IV  8  6.58
## 35  IV  8  5.76
## 36  IV  8  7.71
## 37  IV  8  8.84
## 38  IV  8  8.47
## 39  IV  8  7.04
## 40  IV  8  5.25
## 41  IV 19 12.50
## 42  IV  8  5.56
## 43  IV  8  7.91
## 44  IV  8  6.89
```
]

---

## Summarising Anscombe's quartet

``` r
quartet |>
  group_by(set) |>
  summarise(
    mean_x = mean(x), 
    mean_y = mean(y),
    sd_x = sd(x),
    sd_y = sd(y),
    r = cor(x, y)
  )
```

```
## # A tibble: 4 × 6
##   set   mean_x mean_y  sd_x  sd_y     r
##   <fct>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 I          9   7.50  3.32  2.03 0.816
## 2 II         9   7.50  3.32  2.03 0.816
## 3 III        9   7.5   3.32  2.03 0.816
## 4 IV         9   7.50  3.32  2.03 0.817
```

---

## Visualizing Anscombe's quartet

---

## Data: Palmer Penguins

Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

.pull-left-narrow[
<img src="img/penguins.png" width="80%" style="display: block; margin: auto;" />
]
.pull-right-wide[

``` r
library(palmerpenguins)
penguins <- palmerpenguins::penguins
glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adeli…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torg…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.…
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 362…
## $ sex               <fct> male, female, female, NA, female, mal…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2…
```
]

---

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-29-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

```
## Warning: Removed 2 rows containing missing values or values outside the
## scale range (`geom_point()`).
```
]
]

---

# Coding out loud

---

``` r
*ggplot(data = penguins)
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-30-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
*      mapping = aes(x = bill_depth_mm))
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-31-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> **and map bill length to the y-axis.**
]

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
*                    y = bill_length_mm))
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-32-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> **Represent each observation with a point**
]

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm)) + 
* geom_point()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-33-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> Represent each observation with a point
> **and map species to the colour of each point.**
]

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
*                    colour = species)) +
  geom_point()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-34-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
* labs(title = "Bill depth and length")
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-35-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
*      subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins")
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-36-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
*      x = "Bill depth (mm)", y = "Bill length (mm)")
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-37-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.midi[
> Start with the `penguins` data frame,
> map bill depth to the x-axis
> and map bill length to the y-axis. 
> Represent each observation with a point
> and map species to the colour of each point.
> Title the plot "Bill depth and length", 
> add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins", 
> label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
> **label the legend "Species"**
]

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
*      colour = "Species")
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-38-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       colour = "Species",
*      caption = "Source: Palmer Station LTER / palmerpenguins package")
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-39-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       colour = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
* scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-40-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-41-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       colour = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_colour_viridis_d()
```

```
## Warning: Removed 2 rows containing missing values or values outside the
## scale range (`geom_point()`).
```
]
.panel[.panel-name[Narrative]
.pull-left-wide[
.midi[
Start with the `penguins` data frame,
map bill depth to the x-axis
and map bill length to the y-axis.

Represent each observation with a point
and map species to the colour of each point.

Title the plot "Bill depth and length", 
add the subtitle "Dimensions for Adelie, Chinstrap, and Gentoo Penguins", 
label the x and y axes as "Bill depth (mm)" and "Bill length (mm)", respectively,
label the legend "Species", 
and add a caption for the data source.

Finally, use a discrete colour scale that is designed to be perceived by viewers with common forms of colour blindness.
]
]
]
]

---

## Argument names

``` r
ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     colour = species)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  scale_colour_viridis_d()
```
]

---

# Aesthetics

---

## Aesthetics options

Commonly used characteristics of plotting characters that can be **mapped to a specific variable** in the data are

- `colour`
- `shape`
- `size`
- `alpha` (transparency)

---

## Colour

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
*          colour = species)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-42-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Shape

Mapped to a different variable than `colour`

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           colour = species,
*          shape = island)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-43-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Shape

Mapped to same variable as `colour`

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           colour = species,
*          shape = species)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-44-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Size

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           colour = species,
           shape = species,
*          size = body_mass_g)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-45-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Alpha

``` r
ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           colour = species,
           shape = species,
           size = body_mass_g,
*          alpha = flipper_length_mm)) +
  geom_point() +
  scale_colour_viridis_d()
```
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-46-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
*          size = body_mass_g,
*          alpha = flipper_length_mm)) +
  geom_point()
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-47-1.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[
**Setting**

``` r
ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm)) + 
* geom_point(size = 2, alpha = 0.5)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-48-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Mapping vs. setting

- **Mapping:** Determine the size, alpha, etc. of points based on the values of a variable in the data
  - goes into `aes()`

- **Setting:** Determine the size, alpha, etc. of points **not** based on the values of a variable in the data
  - goes into `geom_*()` (this was `geom_point()` in the previous example, but we'll learn about other geoms soon!)
  
---

# Faceting

---

## Faceting

- Smaller plots that display different subsets of the data
- Useful for exploring conditional relationships and large data

---

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-49-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_grid(species ~ island)
```

```
## Warning: Removed 2 rows containing missing values or values outside the
## scale range (`geom_point()`).
```
]
]

---

## Various ways to facet

.question[
In the next few slides describe what each plot displays. Think about how the code relates to the output.

**Note:** The plots in the next few slides do not have proper titles, axis labels, etc. because we want you to figure out what's happening in the plots.
But you should always label your plots!
]

---

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_grid(species ~ sex)
```

---

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_grid(sex ~ species)
```

---

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_wrap(~ species)
```

---

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_grid(. ~ species)
```

---

``` r
ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
* facet_wrap(~ species, ncol = 2)
```

---

## Faceting summary

- `facet_grid()`:
    - 2d grid
    - `rows ~ cols`
    - use `.` for no split
- `facet_wrap()`: 1d ribbon wrapped according to number of rows and columns specified or available plotting area

---

## Facet and color

``` r
ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
*     color = species)) +
  geom_point() +
  facet_grid(species ~ sex) +
* scale_color_viridis_d()
```
]
.pull-right-wide[
<img src="02-data-viz_files/figure-html/unnamed-chunk-55-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Facet and color, no legend

``` r
ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species)) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d() +
* guides(color = FALSE)
```
]

---

# Terminology

---

## Number of variables involved

- Univariate data analysis - distribution of single variable
- Bivariate data analysis - relationship between two variables
- Multivariate data analysis - relationship between many variables at once, usually focusing on the relationship between two while conditioning for others

---

## Types of variables

- **Numerical variables** can be classified as **continuous** or **discrete** based on whether or not the variable can take on an infinite number of values or only non-negative whole numbers, respectively.
- If the variable is **categorical**, we can determine if it is **ordinal** based on whether or not the levels have a natural ordering.

---

# Data

---

## Data: Lending Club

- Thousands of loans made through the Lending Club, which is a platform that allows individuals to lend to other individuals

- Not all loans are created equal -- ease of getting a loan depends on (apparent) ability to pay back the loan

- Data includes loans *made*, these are not loan applications
]
.pull-right-narrow[
<img src="img/lending-club.png" width="100%" style="display: block; margin: auto;" />
]

---

## Take a peek at data

``` r
library(openintro)
glimpse(loans_full_schema)
```

```
## Rows: 10,000
## Columns: 55
## $ emp_title                        <chr> "global config enginee…
## $ emp_length                       <dbl> 3, 10, 3, 1, 10, NA, 1…
## $ state                            <fct> NJ, HI, WI, PA, CA, KY…
## $ homeownership                    <fct> MORTGAGE, RENT, RENT, …
## $ annual_income                    <dbl> 90000, 40000, 40000, 3…
## $ verified_income                  <fct> Verified, Not Verified…
## $ debt_to_income                   <dbl> 18.01, 5.04, 21.15, 10…
## $ annual_income_joint              <dbl> NA, NA, NA, NA, 57000,…
## $ verification_income_joint        <fct> , , , , Verified, , No…
## $ debt_to_income_joint             <dbl> NA, NA, NA, NA, 37.66,…
## $ delinq_2y                        <int> 0, 0, 0, 0, 0, 1, 0, 1…
## $ months_since_last_delinq         <int> 38, NA, 28, NA, NA, 3,…
## $ earliest_credit_line             <dbl> 2001, 1996, 2006, 2007…
## $ inquiries_last_12m               <int> 6, 1, 4, 0, 7, 6, 1, 1…
## $ total_credit_lines               <int> 28, 30, 31, 4, 22, 32,…
## $ open_credit_lines                <int> 10, 14, 10, 4, 16, 12,…
...
```

---

## Selected variables

``` r
loans <- loans_full_schema |>
  select(loan_amount, interest_rate, term, grade, 
         state, annual_income, homeownership, debt_to_income)
glimpse(loans)
```

```
## Rows: 10,000
## Columns: 8
## $ loan_amount    <int> 28000, 5000, 2000, 21600, 23000, 5000, 2…
## $ interest_rate  <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.72, …
## $ term           <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 36, …
## $ grade          <ord> C, C, D, A, C, A, C, B, C, A, C, B, C, B…
## $ state          <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, IL, …
## $ annual_income  <dbl> 90000, 40000, 40000, 30000, 35000, 34000…
## $ homeownership  <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN, M…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.46, …
```

---

## Selected variables

<br>

.midi[
variable        | description
----------------|-------------
`loan_amount`   |	Amount of the loan received, in US dollars
`interest_rate` |	Interest rate on the loan, in an annual percentage
`term`	        | The length of the loan, which is always set as a whole number of months
`grade`	        | Loan grade, which takes a values A through G and represents the quality of the loan and its likelihood of being repaid
`state`         |	US state where the borrower resides
`annual_income` |	Borrower’s annual income, including any second income, in US dollars
`homeownership`	| Indicates whether the person owns, owns but has a mortgage, or rents
`debt_to_income` | Debt-to-income ratio
]

---

## Variable types

<br>

variable        | type
----------------|-------------
`loan_amount`   |	numerical, continuous
`interest_rate` |	numerical, continuous
`term`	        | numerical, discrete
`grade`	        | categorical, ordinal
`state`         |	categorical, not ordinal
`annual_income` |	numerical, continuous
`homeownership`	| categorical, not ordinal
`debt_to_income` | numerical, continuous

---

# Visualizing numerical data

---

## Describing shapes of numerical distributions

- shape:
    - skewness: right-skewed, left-skewed, symmetric (skew is to the side of the longer tail)
    - modality: unimodal, bimodal, multimodal, uniform
- center: mean (`mean`), median (`median`), mode (not always useful)
- spread: range (`range`), standard deviation (`sd`), inter-quartile range (`IQR`)
- unusual observations

---

# Histogram

---

## Histogram

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_histogram()
```

```
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
```

---

## Histograms and binwidth

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_histogram(binwidth = 1000)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-60-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[binwidth = 5000]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_histogram(binwidth = 5000)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-61-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[binwidth = 20000]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_histogram(binwidth = 20000)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-62-1.png" width="50%" style="display: block; margin: auto;" />
]
]

---

## Customizing histograms

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-63-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_histogram(binwidth = 5000,
                 color = "white") +
* labs(
*   x = "Loan amount ($)",
*   y = "Frequency",
*   title = "Amounts of Lending Club loans"
* )
```
]
]

---

## Fill with a categorical variable

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-64-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = loan_amount, 
*                 fill = homeownership)) +
  geom_histogram(binwidth = 5000,
*                alpha = 0.5) +
  labs(
    x = "Loan amount ($)",
    y = "Frequency",
    title = "Amounts of Lending Club loans"
  )
```
]
]

---

## Facet with a categorical variable

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-65-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = loan_amount, fill = homeownership)) + 
  geom_histogram(binwidth = 5000) +
  labs(
    x = "Loan amount ($)",
    y = "Frequency",
    title = "Amounts of Lending Club loans"
  ) +
* facet_wrap(~ homeownership, nrow = 3)
```
]
]

---

# Density plot

---

## Density plot

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_density()
```

---

## Density plots and adjusting bandwidth

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_density(adjust = 0.5)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-67-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[adjust = 1]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_density(adjust = 1) # default bandwidth
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-68-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[adjust = 2]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_density(adjust = 2)
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-69-1.png" width="50%" style="display: block; margin: auto;" />
]
]

---

## Customizing density plots

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-70-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = loan_amount)) +
  geom_density(adjust = 2) +
* labs(
*   x = "Loan amount ($)",
*   y = "Density",
*   title = "Amounts of Lending Club loans"
* )
```
]
]

---

## Adding a categorical variable

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-71-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = loan_amount, 
*                 fill = homeownership)) +
  geom_density(adjust = 2, 
*              alpha = 0.5) +
  labs(
    x = "Loan amount ($)",
    y = "Density",
    title = "Amounts of Lending Club loans", 
*   fill = "Homeownership"
  )
```
]
]

---

# Box plot

---

## Box plot

``` r
ggplot(loans, aes(x = interest_rate)) +
  geom_boxplot()
```

---

## Box plot and outliers

``` r
ggplot(loans, aes(x = annual_income)) +
  geom_boxplot()
```

---

## Customizing box plots

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-74-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = interest_rate)) +
  geom_boxplot() +
  labs(
    x = "Interest rate (%)",
    y = NULL,
    title = "Interest rates of Lending Club loans"
  ) +
* theme(
*   axis.ticks.y = element_blank(),
*   axis.text.y = element_blank()
* )
```
]
]

---

## Adding a categorical variable

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-75-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(loans, aes(x = interest_rate,
*                 y = grade)) +
  geom_boxplot() +
  labs(
    x = "Interest rate (%)",
    y = "Grade",
    title = "Interest rates of Lending Club loans",
*   subtitle = "by grade of loan"
  )
```
]
]

---

# Relationships numerical variables

---

## Scatterplot

``` r
ggplot(loans, aes(x = debt_to_income, y = interest_rate)) +
  geom_point()
```

---

## Hex plot

``` r
ggplot(loans, aes(x = debt_to_income, y = interest_rate)) +
  geom_hex()
```

---

## Hex plot

``` r
ggplot(loans |> filter(debt_to_income < 100), 
       aes(x = debt_to_income, y = interest_rate)) +
  geom_hex()
```

<img src="02-data-viz_files/figure-html/unnamed-chunk-78-1.png" width="60%" style="display: block; margin: auto;" />
.pull-right-wide[
<img src="02-data-viz_files/figure-html/unnamed-chunk-79-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Plotting Categorical data

---

### Data

``` r
library(openintro)
loans <- loans_full_schema |>
  select(loan_amount, interest_rate, term, grade, 
         state, annual_income, homeownership, debt_to_income)
glimpse(loans)
```

---

# Bar plot

---

## Bar plot

``` r
ggplot(loans, aes(x = homeownership)) +
  geom_bar()
```

---

## Segmented bar plot

``` r
ggplot(loans, aes(x = homeownership, 
*                 fill = grade)) +
  geom_bar()
```

---

## Segmented bar plot

``` r
ggplot(loans, aes(x = homeownership, fill = grade)) +
* geom_bar(position = "fill")
```

---

.question[
Which bar plot is a more useful representation for visualizing the relationship between homeownership and grade?
]

.pull-left[
<img src="02-data-viz_files/figure-html/unnamed-chunk-84-1.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="02-data-viz_files/figure-html/unnamed-chunk-85-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Customizing bar plots

.panelset[
.panel[.panel-name[Plot]
<img src="02-data-viz_files/figure-html/unnamed-chunk-86-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
*ggplot(loans, aes(y = homeownership,
                  fill = grade)) +
  geom_bar(position = "fill") +
* labs(
*   x = "Proportion",
*   y = "Homeownership",
*   fill = "Grade",
*   title = "Grades of Lending Club loans",
*   subtitle = "and homeownership of lendee"
* )
```
]
]

---

# Relationships between numerical and categorical variables

---

## Already talked about...

- Colouring and faceting histograms and density plots
- Side-by-side box plots

---

## Violin plots

``` r
ggplot(loans, aes(x = homeownership, y = loan_amount)) +
  geom_violin()
```

---

## Ridge plots

``` r
library(ggridges)
ggplot(loans, aes(x = loan_amount, y = grade, fill = grade, color = grade)) + 
  geom_density_ridges(alpha = 0.5)
```

---