Data wrangling: relational data and factors

.title[
# Data wrangling: relational data and factors
]
.author[
### INFO 5940 <br /> Cornell University
]

---

# Relational data structures

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## Introduction to relational data

* Multiple tables of data that when combined together answer research questions
* Relations define the important element, not just the individual tables
* Relations are defined between a pair of tables
* Relational verbs
    * Mutating joins
    * Filtering joins

---

---

.pull-left[
  <table>
<caption>Superheroes</caption>
 <thead>
  <tr>
   <th style="text-align:left;"> name </th>
   <th style="text-align:left;"> alignment </th>
   <th style="text-align:left;"> gender </th>
   <th style="text-align:left;"> publisher </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Magneto </td>
   <td style="text-align:left;"> bad </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> Marvel </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Batman </td>
   <td style="text-align:left;"> good </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> DC </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Sabrina </td>
   <td style="text-align:left;"> good </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> Archie Comics </td>
  </tr>
</tbody>
</table>

]

.pull-right[
  <table>
<caption>Publishers</caption>
 <thead>
  <tr>
   <th style="text-align:left;"> publisher </th>
   <th style="text-align:right;"> yr_founded </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> DC </td>
   <td style="text-align:right;"> 1934 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Marvel </td>
   <td style="text-align:right;"> 1939 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Image </td>
   <td style="text-align:right;"> 1992 </td>
  </tr>
</tbody>
</table>

]

---

# Mutating joins

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## `inner_join()`

---

## `inner_join()`

```r
inner_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 2 × 5
##   name    alignment gender publisher yr_founded
##   <chr>   <chr>     <chr>  <chr>          <dbl>
## 1 Magneto bad       male   Marvel          1939
## 2 Batman  good      male   DC              1934
```

---

## `left_join()`

---

## `left_join()`

```r
left_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 3 × 5
##   name    alignment gender publisher     yr_founded
##   <chr>   <chr>     <chr>  <chr>              <dbl>
## 1 Magneto bad       male   Marvel              1939
## 2 Batman  good      male   DC                  1934
## 3 Sabrina good      female Archie Comics         NA
```

---

## `right_join()`

---

## `right_join()`

```r
right_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 3 × 5
##   name    alignment gender publisher yr_founded
##   <chr>   <chr>     <chr>  <chr>          <dbl>
## 1 Magneto bad       male   Marvel          1939
## 2 Batman  good      male   DC              1934
## 3 <NA>    <NA>      <NA>   Image           1992
```

---

## `right_join()` reversed

---

## `full_join()`

---

## `full_join()`

```r
full_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 4 × 5
##   name    alignment gender publisher     yr_founded
##   <chr>   <chr>     <chr>  <chr>              <dbl>
## 1 Magneto bad       male   Marvel              1939
## 2 Batman  good      male   DC                  1934
## 3 Sabrina good      female Archie Comics         NA
## 4 <NA>    <NA>      <NA>   Image               1992
```

---

# Filtering joins

---

## `semi_join()`

---

## `semi_join()`

```r
semi_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 2 × 4
##   name    alignment gender publisher
##   <chr>   <chr>     <chr>  <chr>    
## 1 Magneto bad       male   Marvel   
## 2 Batman  good      male   DC
```

---

## `anti_join()`

---

## `anti_join()`

```r
anti_join(x = superheroes, y = publishers, by = "publisher")
```

```
## # A tibble: 1 × 4
##   name    alignment gender publisher    
##   <chr>   <chr>     <chr>  <chr>        
## 1 Sabrina good      female Archie Comics
```

---

## Gonna take pollution down to zero

---

# Factors

---

## Factors

* Used for categorical (discrete) variables
* Historically used for purposes of efficiency
* Not really necessary in modern R
* Best used to sort categorical variables other than alphabetically
* `forcats`

---

## Character vector

```r
(x1 <- c("Dec", "Apr", "Jan", "Mar"))
```

```
## [1] "Dec" "Apr" "Jan" "Mar"
```

```r
sort(x1)
```

```
## [1] "Apr" "Dec" "Jan" "Mar"
```

---

## Levels

```r
month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)
```

## Factor

```r
(y1 <- factor(x1, levels = month_levels))
```

```
## [1] Dec Apr Jan Mar
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
```

```r
parse_factor(x1, levels = month_levels)
```

```
## [1] Dec Apr Jan Mar
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
```

---

## Different levels/labels

```r
(x2 <- c(12, 4, 1, 3))
```

```
## [1] 12  4  1  3
```

```r
y2 <- factor(x2,
  levels = seq(from = 1, to = 12),
  labels = month_levels
)
y2
```

```
## [1] Dec Apr Jan Mar
## Levels: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
```

---