Visualising categorical data

# Visualising categorical data
## <br><br> College of the Atlantic
### <a href="https://coa-dataviz.netlify.app/">https://coa-dataviz.netlify.app/</a>

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# Recap

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## 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

```r
library(openintro)
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, ~
```

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# Bar plot

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## Bar plot

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

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## Segmented bar plot

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

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## Segmented bar plot

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

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

.pull-left[
<img src="u2-d04-viz-cat_files/figure-html/unnamed-chunk-6-1.png" width="100%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="u2-d04-viz-cat_files/figure-html/unnamed-chunk-7-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Customizing bar plots

.panelset[
.panel[.panel-name[Plot]
<img src="u2-d04-viz-cat_files/figure-html/unnamed-chunk-8-1.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"
* )
```
]
]

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# Relationships between numerical and categorical variables

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## Already talked about...

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

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## 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)
```

---
## Acknowledgements

* This course builds on the materials from [Data Science in a Box](https://datasciencebox.org/) developed by Mine Çetinkaya-Rundel and are adapted under the [Creative Commons Attribution Share Alike 4.0 International](https://github.com/rstudio-education/datascience-box/blob/master/LICENSE.md)