Visualizing categorical data 🐱

---

layout: true
  
<div class="my-footer">
<span>
<a href="https://DataScience4Psych.github.io/DataScience4Psych/" target="_blank">Data Science for Psychologists</a>
</span>
</div>

---

# Recap

---

## 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.

---

### Remember this Data?

``` r
library(tidyverse)
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 …    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 …    150    49 brown      light      brown           19  
##  6 Owen …    178   120 brown, gr… light      blue            52  
##  7 Beru …    165    75 brown      light      blue            47  
##  8 R5-D4      97    32 <NA>       white, red red             NA  
##  9 Biggs…    183    84 black      light      brown           24  
## 10 Obi-W…    182    77 auburn, w… fair       blue-gray       57  
## # ℹ 77 more rows
## # ℹ 7 more variables: sex <chr>, gender <chr>, homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>,
## #   starships <list>
```

---
### Perhaps now?

``` 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…
```

---
#### Recode hair color

``` r
starwars <- starwars %>%
  mutate(hair_color2 =
           fct_other(hair_color,
                     keep = c("black", "brown", "brown", "blond")
           )
  )
```
---

# Bar plot

---

## Bar plot

``` r
ggplot(data = starwars, mapping = aes(x = gender)) +
  geom_bar()
```

---

## Segmented bar plot: counts

``` r
ggplot(data = starwars, mapping = aes(x = gender, 
*                 fill = hair_color))+
  geom_bar()
```

---

## Segmented bar plots

``` r
ggplot(data = starwars, mapping = aes(x = gender, 
*   fill = hair_color2))+
  geom_bar()
```

---

## Segmented bar plots

``` r
ggplot(data = starwars, mapping = aes(x = gender, 
*   fill = hair_color2))+
* geom_bar()+
  coord_flip()
```

---

## Segmented bar plots: proportions

``` r
ggplot(data = starwars,
       mapping = aes(x = gender, fill = hair_color2)) +
  geom_bar(position = "fill") +
  coord_flip()
```

``` r
labs(y = "proportion")
```

```
## $y
## [1] "proportion"
## 
## attr(,"class")
## [1] "labels"
```

---

.question[
    Which bar plot is a more useful representation for visualizing the relationship between gender and hair color?
  ]

.pull-left[
<img src="d06_vizcat_files/figure-html/unnamed-chunk-10-1.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="d06_vizcat_files/figure-html/unnamed-chunk-11-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Customizing bar plots

.pull-left[
<img src="d06_vizcat_files/figure-html/unnamed-chunk-12-1.png" width="60%" style="display: block; margin: auto;" />
]
.pull-right[.small[

``` r
*ggplot(starwars, aes(y = gender,
                  fill = hair_color2)) +
  geom_bar(position = "fill") +
* labs(
*   x = "Proportion",
*   y = "Gender",
*   fill = "Hair Color",
*   title = "Hair Colors of Starwars",
*   subtitle = "by gender"
* )
```
]

---

Time to actually play around with the Star Wars dataset!

- Go to class git repo ([github.com/DataScience4Psych](https://github.com/DataScience4Psych)) and start `AE 03 - StarWars + Data visualization`.
    - Open the R Markdown document and complete the exercise (and if time allows, the stretch goal exercise).]

---

# Relationships between numerical and categorical variables

---

## Already talked about...

- Coloring 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)
```

---

# Sources

- Mine Çetinkaya-Rundel's Data Science in a Box ([link](https://datasciencebox.org/))

---

# Wrapping Up...