Variable types and visualization types

.title[
# Variable types and visualization types
]
.subtitle[
## STAT 4380
]
.author[
### Dr. Katie Fitzgerald
]

---

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</div>

---

# ggplot ❤️ 🏐

---

## Data: Volleyball

NCAA women's volleyball season-level statistics for 2022-2023 season.

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

``` r
library(tidyverse)
volleyball <- read_csv("./data/volleyball_ncaa_div1_2022_23_clean.csv") 
glimpse(volleyball)
```

```
## Rows: 334
## Columns: 15
## $ team                 <chr> "Lafayette", "Delaware St.", "Yale…
## $ conference           <chr> "Patriot", "MEAC", "Ivy League", "…
## $ region               <chr> "East", "Southeast", "East", "Sout…
## $ aces_per_set         <dbl> 2.33, 2.20, 2.15, 2.15, 2.03, 1.98…
## $ assists_per_set      <dbl> 11.01, 11.45, 12.60, 10.56, 11.61,…
## $ team_attacks_per_set <dbl> 34.54, 29.98, 35.39, 32.52, 34.10,…
## $ blocks_per_set       <dbl> 1.31, 2.17, 1.82, 1.81, 1.83, 2.39…
## $ digs_per_set         <dbl> 13.60, 12.58, 15.29, 14.22, 14.27,…
## $ hitting_pctg         <dbl> 0.180, 0.250, 0.242, 0.194, 0.201,…
## $ kills_per_set        <dbl> 11.93, 12.12, 13.90, 11.54, 12.40,…
## $ opp_hitting_pctg     <dbl> 0.227, 0.137, 0.155, 0.170, 0.188,…
## $ w                    <dbl> 8, 24, 23, 23, 18, 17, 19, 16, 18,…
## $ l                    <dbl> 15, 7, 3, 11, 13, 13, 13, 13, 13, …
## $ win_pctg             <dbl> 0.348, 0.774, 0.885, 0.676, 0.581,…
## $ winning_season       <chr> "no", "yes", "yes", "yes", "yes", …
```
]

---

## 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.

![](img/variable_types.png)

---

# Visualizing 1 numeric variable

---

## 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

---

# 1 numeric variable: histogram

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-4-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_histogram(color = "white")
```
]
]

---

## Histograms and binwidth

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_histogram(color = "white", binwidth = 0.1)
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-5-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[binwidth = 0.5]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_histogram(color = "white", binwidth = 0.5)
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-6-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[binwidth = 1]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_histogram(color = "white", binwidth = 1)
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-7-1.png" width="50%" style="display: block; margin: auto;" />
]
]

---

## 1 numeric variable: Density plot

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_density()
```

---

## Density plots and adjusting bandwidth

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_density(adjust = 0.5)
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-9-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[adjust = 1]

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

<img src="04-viz-types_files/figure-html/unnamed-chunk-10-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[adjust = 2]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_density(adjust = 2)
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-11-1.png" width="50%" style="display: block; margin: auto;" />
]
]
---

## 1 numeric variable: Box plot

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_boxplot()
```

---

## Customizing box plots

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-13-1.png" width="60%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_boxplot() +
  labs(
    x = "Kills per set",
    y = NULL,
    title = "Kills per set is left-skewed with a median around 12.5"
  ) +
* theme(
*   axis.ticks.y = element_blank(),
*   axis.text.y = element_blank()
* )
```
]
]

---

# Visualizing 1 numeric + 1 categorical variable

---

# Faceted histograms

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-14-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = kills_per_set)) +
  geom_histogram(color = "white", binwidth = 1) +
  facet_wrap(~ region)
```
]
]

---

## Overlapping densities

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-15-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = kills_per_set, 
                       fill = region,
                       color = region)) +
  geom_density(alpha = 0.3)
```
]
]

---

## Ridge plots

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-16-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = kills_per_set, 
                       y = region)) +
  geom_density_ridges()
```
]
]

---

## Side-by-side Box plots

``` r
ggplot(volleyball, aes(x = kills_per_set, y = region)) +
  geom_boxplot()
```

---

## Side-by-side violin plots

``` r
ggplot(volleyball, aes(x = kills_per_set, y = region)) +
  geom_violin()
```

---

# Visualizing 1 categorical variable

---

# 1 categorical variable: bar plot

``` r
ggplot(volleyball, aes(y = conference)) +
  geom_bar(color = "navyblue")
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-19-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[fill]

``` r
ggplot(volleyball, aes(y = conference)) +
  geom_bar(fill = "navyblue")
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-20-1.png" width="50%" style="display: block; margin: auto;" />
]
.panel[.panel-name[both]

``` r
ggplot(volleyball, aes(y = conference)) +
  geom_bar(fill = "navyblue", color = "pink")
```

<img src="04-viz-types_files/figure-html/unnamed-chunk-21-1.png" width="50%" style="display: block; margin: auto;" />
]
]

---

# 2 categorical variables

---

# 2 categorical variables: stacked bar plot

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-22-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(y = conference, fill = winning_season)) +
  geom_bar()
```
]
]

---

# 2 categorical: standardized bar plot

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-23-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(y = conference, fill = winning_season)) +
  geom_bar(position = "fill") +
  labs(x = "proportion")
```
]
]

---

# 2 numeric variables

---

# 2 numeric variables: scatterplot

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-24-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = digs_per_set, y = kills_per_set)) +
  geom_point()
```
]
]

---

# 2 numeric variables: hexplot

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-25-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = digs_per_set, y = kills_per_set)) +
  geom_hex()
```
]
]

---

# More than 2: scatterplot w/ color

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-26-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = hitting_pctg, y = opp_hitting_pctg,
                        color = win_pctg)) +
  geom_point()
```

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

---

# More than 2: faceted scatterplot w/ color

.panelset[
.panel[.panel-name[Plot]
<img src="04-viz-types_files/figure-html/unnamed-chunk-27-1.png" width="70%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]

``` r
ggplot(volleyball, aes(x = hitting_pctg, y = opp_hitting_pctg,
                       color = win_pctg)) +
  geom_point() +
  facet_wrap(~region)
```

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

---

# And many more!

- [Directory of visualizations](https://clauswilke.com/dataviz/directory-of-visualizations.html)

- [The R Graph Gallery](https://r-graph-gallery.com)