A Very Incomplete Survey of Descriptive Statistics Commands in R

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# Packages needed and a Note about Icons

Please load up the `tidyverse` package

``` r
library(tidyverse)
```
<br>
You may come across the following icons. The table below lists what each means.

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:center;"> Icon </th>
   <th style="text-align:left;"> Description </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:center;width: 10em; "> &lt;svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4682b4;overflow:visible;position:relative;"&gt;&lt;path d="M52.5 440.6c-9.5 7.9-22.8 9.7-34.1 4.4S0 428.4 0 416V96C0 83.6 7.2 72.3 18.4 67s24.5-3.6 34.1 4.4L224 214.3V256v41.7L52.5 440.6zM256 352V256 128 96c0-12.4 7.2-23.7 18.4-29s24.5-3.6 34.1 4.4l192 160c7.3 6.1 11.5 15.1 11.5 24.6s-4.2 18.5-11.5 24.6l-192 160c-9.5 7.9-22.8 9.7-34.1 4.4s-18.4-16.6-18.4-29V352z"/&gt;&lt;/svg&gt; </td>
   <td style="text-align:left;width: 40em; "> Indicates that an example continues on the following slide. </td>
  </tr>
  <tr>
   <td style="text-align:center;width: 10em; "> &lt;svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#ff6347;overflow:visible;position:relative;"&gt;&lt;path d="M0 128C0 92.7 28.7 64 64 64H320c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128z"/&gt;&lt;/svg&gt; </td>
   <td style="text-align:left;width: 40em; "> Indicates that a section using common syntax has ended. </td>
  </tr>
  <tr>
   <td style="text-align:center;width: 10em; "> &lt;svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5cb85c;overflow:visible;position:relative;"&gt;&lt;path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/&gt;&lt;/svg&gt; </td>
   <td style="text-align:left;width: 40em; "> Indicates that there is an active hyperlink on the slide. </td>
  </tr>
  <tr>
   <td style="text-align:center;width: 10em; "> &lt;svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#faffbd;overflow:visible;position:relative;"&gt;&lt;path d="M0 48C0 21.5 21.5 0 48 0l0 48V441.4l130.1-92.9c8.3-6 19.6-6 27.9 0L336 441.4V48H48V0H336c26.5 0 48 21.5 48 48V488c0 9-5 17.2-13 21.3s-17.6 3.4-24.9-1.8L192 397.5 37.9 507.5c-7.3 5.2-16.9 5.9-24.9 1.8S0 497 0 488V48z"/&gt;&lt;/svg&gt; </td>
   <td style="text-align:left;width: 40em; "> Indicates that a section covering a concept has ended. </td>
  </tr>
</tbody>
</table>

---

# Descriptives

We're going to use the Star Wars data set that's included in `dplyr`

``` r
data(starwars)
```

``` r
starwars
```

```
## # A tibble: 87 × 14
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Luke Sk…    172    77 blond      fair       blue            19   male  mascu…
##  2 C-3PO       167    75 <NA>       gold       yellow         112   none  mascu…
##  3 R2-D2        96    32 <NA>       white, bl… red             33   none  mascu…
##  4 Darth V…    202   136 none       white      yellow          41.9 male  mascu…
##  5 Leia Or…    150    49 brown      light      brown           19   fema… femin…
##  6 Owen La…    178   120 brown, gr… light      blue            52   male  mascu…
##  7 Beru Wh…    165    75 brown      light      blue            47   fema… femin…
##  8 R5-D4        97    32 <NA>       white, red red             NA   none  mascu…
##  9 Biggs D…    183    84 black      light      brown           24   male  mascu…
## 10 Obi-Wan…    182    77 auburn, w… fair       blue-gray       57   male  mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```

---

##  View a Portion of the Data Set

``` r
head(starwars)
```

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

---

## Counts

**Total number of names**

tidy approach <br>

``` r
starwars %>%
  dplyr::select(name) %>%
  nrow()
```

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

Base R approach <br>

``` r
length(starwars$name)
```

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

.right[.footnote[<svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#ff6347;overflow:visible;position:relative;"><path d="M0 128C0 92.7 28.7 64 64 64H320c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128z"/></svg>]]

---

## Column Types

Using `str`

``` r
str(starwars)
```

```
## tibble [87 × 14] (S3: tbl_df/tbl/data.frame)
##  $ name      : chr [1:87] "Luke Skywalker" "C-3PO" "R2-D2" "Darth Vader" ...
##  $ height    : int [1:87] 172 167 96 202 150 178 165 97 183 182 ...
##  $ mass      : num [1:87] 77 75 32 136 49 120 75 32 84 77 ...
##  $ hair_color: chr [1:87] "blond" NA NA "none" ...
##  $ skin_color: chr [1:87] "fair" "gold" "white, blue" "white" ...
##  $ eye_color : chr [1:87] "blue" "yellow" "red" "yellow" ...
##  $ birth_year: num [1:87] 19 112 33 41.9 19 52 47 NA 24 57 ...
##  $ sex       : chr [1:87] "male" "none" "none" "male" ...
##  $ gender    : chr [1:87] "masculine" "masculine" "masculine" "masculine" ...
##  $ homeworld : chr [1:87] "Tatooine" "Tatooine" "Naboo" "Tatooine" ...
##  $ species   : chr [1:87] "Human" "Droid" "Droid" "Human" ...
##  $ films     :List of 87
##   ..$ : chr [1:5] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "Revenge of the Sith" ...
##   ..$ : chr [1:6] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "The Phantom Menace" ...
##   ..$ : chr [1:7] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "The Phantom Menace" ...
##   ..$ : chr [1:4] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "Revenge of the Sith"
##   ..$ : chr [1:5] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "Revenge of the Sith" ...
##   ..$ : chr [1:3] "A New Hope" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:3] "A New Hope" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "A New Hope"
##   ..$ : chr "A New Hope"
##   ..$ : chr [1:6] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "The Phantom Menace" ...
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "A New Hope" "Revenge of the Sith"
##   ..$ : chr [1:5] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "Revenge of the Sith" ...
##   ..$ : chr [1:4] "A New Hope" "The Empire Strikes Back" "Return of the Jedi" "The Force Awakens"
##   ..$ : chr "A New Hope"
##   ..$ : chr [1:3] "A New Hope" "Return of the Jedi" "The Phantom Menace"
##   ..$ : chr [1:3] "A New Hope" "The Empire Strikes Back" "Return of the Jedi"
##   ..$ : chr "A New Hope"
##   ..$ : chr [1:5] "The Empire Strikes Back" "Return of the Jedi" "The Phantom Menace" "Attack of the Clones" ...
##   ..$ : chr [1:5] "The Empire Strikes Back" "Return of the Jedi" "The Phantom Menace" "Attack of the Clones" ...
##   ..$ : chr [1:3] "The Empire Strikes Back" "Return of the Jedi" "Attack of the Clones"
##   ..$ : chr "The Empire Strikes Back"
##   ..$ : chr "The Empire Strikes Back"
##   ..$ : chr [1:2] "The Empire Strikes Back" "Return of the Jedi"
##   ..$ : chr "The Empire Strikes Back"
##   ..$ : chr [1:2] "Return of the Jedi" "The Force Awakens"
##   ..$ : chr "Return of the Jedi"
##   ..$ : chr "Return of the Jedi"
##   ..$ : chr "Return of the Jedi"
##   ..$ : chr "Return of the Jedi"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "The Phantom Menace" "Attack of the Clones"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:2] "The Phantom Menace" "Attack of the Clones"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:2] "The Phantom Menace" "Attack of the Clones"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "Return of the Jedi"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "The Phantom Menace" "Revenge of the Sith"
##   ..$ : chr [1:2] "The Phantom Menace" "Revenge of the Sith"
##   ..$ : chr [1:2] "The Phantom Menace" "Revenge of the Sith"
##   ..$ : chr "The Phantom Menace"
##   ..$ : chr [1:3] "The Phantom Menace" "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "The Phantom Menace" "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr "Attack of the Clones"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "Revenge of the Sith"
##   ..$ : chr "Revenge of the Sith"
##   ..$ : chr [1:2] "A New Hope" "Revenge of the Sith"
##   ..$ : chr [1:2] "Attack of the Clones" "Revenge of the Sith"
##   ..$ : chr "Revenge of the Sith"
##   ..$ : chr "The Force Awakens"
##   ..$ : chr "The Force Awakens"
##   ..$ : chr "The Force Awakens"
##   ..$ : chr "The Force Awakens"
##   ..$ : chr "The Force Awakens"
##  $ vehicles  :List of 87
##   ..$ : chr [1:2] "Snowspeeder" "Imperial Speeder Bike"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Imperial Speeder Bike"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Tribubble bongo"
##   ..$ : chr [1:2] "Zephyr-G swoop bike" "XJ-6 airspeeder"
##   ..$ : chr(0) 
##   ..$ : chr "AT-ST"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Snowspeeder"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Tribubble bongo"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Sith speeder"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Flitknot speeder"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Koro-2 Exodrive airspeeder"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Tsmeu-6 personal wheel bike"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##  $ starships :List of 87
##   ..$ : chr [1:2] "X-wing" "Imperial shuttle"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "TIE Advanced x1"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "X-wing"
##   ..$ : chr [1:5] "Jedi starfighter" "Trade Federation cruiser" "Naboo star skiff" "Jedi Interceptor" ...
##   ..$ : chr [1:3] "Naboo fighter" "Trade Federation cruiser" "Jedi Interceptor"
##   ..$ : chr(0) 
##   ..$ : chr [1:2] "Millennium Falcon" "Imperial shuttle"
##   ..$ : chr [1:2] "Millennium Falcon" "Imperial shuttle"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "X-wing"
##   ..$ : chr "X-wing"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Slave 1"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Millennium Falcon"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "A-wing"
##   ..$ : chr(0) 
##   ..$ : chr "Millennium Falcon"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr [1:3] "Naboo fighter" "H-type Nubian yacht" "Naboo star skiff"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Naboo Royal Starship"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Scimitar"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Jedi starfighter"
##   ..$ : chr(0) 
##   ..$ : chr "Naboo fighter"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "Belbullab-22 starfighter"
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr(0) 
##   ..$ : chr "X-wing"
##   ..$ : chr(0) 
##   ..$ : chr(0)
```

or....

---

Using `glimpse`

``` r
glimpse(starwars)
```

```
## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
## $ sex        <chr> "male", "none", "none", "male", "female", "male", "female",…
## $ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini…
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
## $ films      <list> <"A New Hope", "The Empire Strikes Back", "Return of the J…
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…
```

---

## Frequencies

``` r
*starwars
```
]
 
.panel2-sw1-auto[

---
count: false

``` r
starwars %>%
* count(sex)
```
]
 
.panel2-sw1-auto[

```
## # A tibble: 5 × 2
##   sex                n
##   <chr>          <int>
## 1 female            16
## 2 hermaphroditic     1
## 3 male              60
## 4 none               6
## 5 <NA>               4
```
]

---

## Measures of Central Tendency
**Mean**

``` r
*starwars
```
]
 
.panel2-sw2-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw2-auto[

```
## # A tibble: 87 × 14
## # Groups:   species [38]
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Luke Sk…    172    77 blond      fair       blue            19   male  mascu…
##  2 C-3PO       167    75 <NA>       gold       yellow         112   none  mascu…
##  3 R2-D2        96    32 <NA>       white, bl… red             33   none  mascu…
##  4 Darth V…    202   136 none       white      yellow          41.9 male  mascu…
##  5 Leia Or…    150    49 brown      light      brown           19   fema… femin…
##  6 Owen La…    178   120 brown, gr… light      blue            52   male  mascu…
##  7 Beru Wh…    165    75 brown      light      blue            47   fema… femin…
##  8 R5-D4        97    32 <NA>       white, red red             NA   none  mascu…
##  9 Biggs D…    183    84 black      light      brown           24   male  mascu…
## 10 Obi-Wan…    182    77 auburn, w… fair       blue-gray       57   male  mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw2-auto[

```
## # A tibble: 29 × 14
## # Groups:   species [11]
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Luke Sk…    172    77 blond      fair       blue            19   male  mascu…
##  2 Darth V…    202   136 none       white      yellow          41.9 male  mascu…
##  3 Leia Or…    150    49 brown      light      brown           19   fema… femin…
##  4 Owen La…    178   120 brown, gr… light      blue            52   male  mascu…
##  5 Beru Wh…    165    75 brown      light      blue            47   fema… femin…
##  6 Biggs D…    183    84 black      light      brown           24   male  mascu…
##  7 Obi-Wan…    182    77 auburn, w… fair       blue-gray       57   male  mascu…
##  8 Anakin …    188    84 blond      fair       blue            41.9 male  mascu…
##  9 Chewbac…    228   112 brown      unknown    blue           200   male  mascu…
## 10 Han Solo    180    80 brown      fair       brown           29   male  mascu…
## # ℹ 19 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(mean(birth_year))
```
]
 
.panel2-sw2-auto[

```
## # A tibble: 11 × 2
##    species      `mean(birth_year)`
##    <chr>                     <dbl>
##  1 Cerean                     92  
##  2 Ewok                        8  
##  3 Gungan                     52  
##  4 Human                      45.5
##  5 Kel Dor                    22  
##  6 Mirialan                   49  
##  7 Mon Calamari               41  
##  8 Trandoshan                 53  
##  9 Twi'lek                    48  
## 10 Wookiee                   200  
## 11 Zabrak                     54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(mean(birth_year)) %>%
* rename(`mean age by species` =
*          `mean(birth_year)`)
```
]
 
.panel2-sw2-auto[

```
## # A tibble: 11 × 2
##    species      `mean age by species`
##    <chr>                        <dbl>
##  1 Cerean                        92  
##  2 Ewok                           8  
##  3 Gungan                        52  
##  4 Human                         45.5
##  5 Kel Dor                       22  
##  6 Mirialan                      49  
##  7 Mon Calamari                  41  
##  8 Trandoshan                    53  
##  9 Twi'lek                       48  
## 10 Wookiee                      200  
## 11 Zabrak                        54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(mean(birth_year)) %>%
  rename(`mean age by species` =
           `mean(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw2-auto[

---

### Side Note: Using Base R vs. tidy

Either is fine but think about the outcome and what you're going to do with it. Let's take the `mean` again with a fake data set from taste test ratings using two varieties of bananas: cavendish and ice cream.

``` r
banana_data <- 
  tibble(
    id = c(1,2,3,4,5),
    cav_cat = c("Excellent", 
                "Above Average", 
                "Very Poor", 
                "Average", 
                "Excellent"),
    cav_code = c(5,4,1,3,5),
    ic_cat = c("Excellent", 
               "Excellent", 
               "Above Average", 
               "Excellent", 
               "Excellent"), 
    ic_code = c(5,5,4,5,5)
    )
```

.right[.footnote[<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#4682b4;overflow:visible;position:relative;"><path d="M52.5 440.6c-9.5 7.9-22.8 9.7-34.1 4.4S0 428.4 0 416V96C0 83.6 7.2 72.3 18.4 67s24.5-3.6 34.1 4.4L224 214.3V256v41.7L52.5 440.6zM256 352V256 128 96c0-12.4 7.2-23.7 18.4-29s24.5-3.6 34.1 4.4l192 160c7.3 6.1 11.5 15.1 11.5 24.6s-4.2 18.5-11.5 24.6l-192 160c-9.5 7.9-22.8 9.7-34.1 4.4s-18.4-16.6-18.4-29V352z"/></svg>]]

.pull-right[.footnote[If you are wondering, the blue java - aka the [ice cream banana](https://www.abc15.com/news/state/planet-arizona-blue-bananas-exist-and-they-taste-like-vanilla-ice-cream) is real!]]

---

``` r
banana_data
```

```
## # A tibble: 5 × 5
##      id cav_cat       cav_code ic_cat        ic_code
##   <dbl> <chr>            <dbl> <chr>           <dbl>
## 1     1 Excellent            5 Excellent           5
## 2     2 Above Average        4 Excellent           5
## 3     3 Very Poor            1 Above Average       4
## 4     4 Average              3 Excellent           5
## 5     5 Excellent            5 Excellent           5
```

---

If we just wanted to find the means, then the Base R method is likely simpler

``` r
mean(banana_data$cav_code)
```

```
## [1] 3.6
```

``` r
mean(banana_data$ic_code)
```

```
## [1] 4.8
```

---

but if we wanted to pass that output along say to find the range of the means, the tidy way is a simpler and more efficient approach

``` r
*banana_data
```
]
 
.panel2-sw3-auto[

---
count: false

``` r
banana_data %>%
* summarise(mean_cav = mean(cav_code),
*           mean_ic = mean(ic_code))
```
]
 
.panel2-sw3-auto[

```
## # A tibble: 1 × 2
##   mean_cav mean_ic
##      <dbl>   <dbl>
## 1      3.6     4.8
```
]

---
count: false

``` r
banana_data %>%
  summarise(mean_cav = mean(cav_code),
            mean_ic = mean(ic_code)) %>%
* mutate(range_means = mean_ic - mean_cav)
```
]
 
.panel2-sw3-auto[

```
## # A tibble: 1 × 3
##   mean_cav mean_ic range_means
##      <dbl>   <dbl>       <dbl>
## 1      3.6     4.8         1.2
```
]

---

**Median**

``` r
*starwars
```
]
 
.panel2-sw4-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw4-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw4-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(median(birth_year))
```
]
 
.panel2-sw4-auto[

```
## # A tibble: 11 × 2
##    species      `median(birth_year)`
##    <chr>                       <dbl>
##  1 Cerean                       92  
##  2 Ewok                          8  
##  3 Gungan                       52  
##  4 Human                        41.9
##  5 Kel Dor                      22  
##  6 Mirialan                     49  
##  7 Mon Calamari                 41  
##  8 Trandoshan                   53  
##  9 Twi'lek                      48  
## 10 Wookiee                     200  
## 11 Zabrak                       54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(median(birth_year)) %>%
* rename(`median age by species` =
*          `median(birth_year)`)
```
]
 
.panel2-sw4-auto[

```
## # A tibble: 11 × 2
##    species      `median age by species`
##    <chr>                          <dbl>
##  1 Cerean                          92  
##  2 Ewok                             8  
##  3 Gungan                          52  
##  4 Human                           41.9
##  5 Kel Dor                         22  
##  6 Mirialan                        49  
##  7 Mon Calamari                    41  
##  8 Trandoshan                      53  
##  9 Twi'lek                         48  
## 10 Wookiee                        200  
## 11 Zabrak                          54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(median(birth_year)) %>%
  rename(`median age by species` =
           `median(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw4-auto[

---

**Mode** <br><br>
Remember that `mode` means something else in R. Instead first run the chunk below

``` r
Mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}
```

---

Then you can find the `Mode`

``` r
*starwars
```
]
 
.panel2-sw5-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw5-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw5-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(Mode(birth_year))
```
]
 
.panel2-sw5-auto[

```
## # A tibble: 11 × 2
##    species      `Mode(birth_year)`
##    <chr>                     <dbl>
##  1 Cerean                       92
##  2 Ewok                          8
##  3 Gungan                       52
##  4 Human                        19
##  5 Kel Dor                      22
##  6 Mirialan                     58
##  7 Mon Calamari                 41
##  8 Trandoshan                   53
##  9 Twi'lek                      48
## 10 Wookiee                     200
## 11 Zabrak                       54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(Mode(birth_year)) %>%
* rename(`mode age by species` =
*          `Mode(birth_year)`)
```
]
 
.panel2-sw5-auto[

```
## # A tibble: 11 × 2
##    species      `mode age by species`
##    <chr>                        <dbl>
##  1 Cerean                          92
##  2 Ewok                             8
##  3 Gungan                          52
##  4 Human                           19
##  5 Kel Dor                         22
##  6 Mirialan                        58
##  7 Mon Calamari                    41
##  8 Trandoshan                      53
##  9 Twi'lek                         48
## 10 Wookiee                        200
## 11 Zabrak                          54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(Mode(birth_year)) %>%
  rename(`mode age by species` =
           `Mode(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw5-auto[

---

**Mean by Value**
Sometimes you need to find a measure for a particular subgroup. In this example, we only want to find the birth year for the female classification of each species. One way to accomplish this is to use the `filter` command to get what we want.

``` r
*starwars
```
]
 
.panel2-sw6-auto[

---
count: false

``` r
starwars %>%
* filter(gender == "feminine")
```
]
 
.panel2-sw6-auto[

```
## # A tibble: 17 × 14
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Leia Or…    150  49   brown      light      brown             19 fema… femin…
##  2 Beru Wh…    165  75   brown      light      blue              47 fema… femin…
##  3 Mon Mot…    150  NA   auburn     fair       blue              48 fema… femin…
##  4 Padmé A…    185  45   brown      light      brown             46 fema… femin…
##  5 Shmi Sk…    163  NA   black      fair       brown             72 fema… femin…
##  6 Ayla Se…    178  55   none       blue       hazel             48 fema… femin…
##  7 Adi Gal…    184  50   none       dark       blue              NA fema… femin…
##  8 Luminar…    170  56.2 black      yellow     blue              58 fema… femin…
##  9 Barriss…    166  50   black      yellow     blue              40 fema… femin…
## 10 Dormé       165  NA   brown      light      brown             NA fema… femin…
## 11 Zam Wes…    168  55   blonde     fair, gre… yellow            NA fema… femin…
## 12 Taun We     213  NA   none       grey       black             NA fema… femin…
## 13 Jocasta…    167  NA   white      fair       blue              NA fema… femin…
## 14 R4-P17       96  NA   none       silver, r… red, blue         NA none  femin…
## 15 Shaak Ti    178  57   none       red, blue… black             NA fema… femin…
## 16 Rey          NA  NA   brown      light      hazel             NA fema… femin…
## 17 Captain…     NA  NA   none       none       unknown           NA fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```
]

---
count: false

``` r
starwars %>%
  filter(gender == "feminine") %>%
* group_by(species)
```
]
 
.panel2-sw6-auto[

```
## # A tibble: 17 × 14
## # Groups:   species [8]
##    name     height  mass hair_color skin_color eye_color birth_year sex   gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
##  1 Leia Or…    150  49   brown      light      brown             19 fema… femin…
##  2 Beru Wh…    165  75   brown      light      blue              47 fema… femin…
##  3 Mon Mot…    150  NA   auburn     fair       blue              48 fema… femin…
##  4 Padmé A…    185  45   brown      light      brown             46 fema… femin…
##  5 Shmi Sk…    163  NA   black      fair       brown             72 fema… femin…
##  6 Ayla Se…    178  55   none       blue       hazel             48 fema… femin…
##  7 Adi Gal…    184  50   none       dark       blue              NA fema… femin…
##  8 Luminar…    170  56.2 black      yellow     blue              58 fema… femin…
##  9 Barriss…    166  50   black      yellow     blue              40 fema… femin…
## 10 Dormé       165  NA   brown      light      brown             NA fema… femin…
## 11 Zam Wes…    168  55   blonde     fair, gre… yellow            NA fema… femin…
## 12 Taun We     213  NA   none       grey       black             NA fema… femin…
## 13 Jocasta…    167  NA   white      fair       blue              NA fema… femin…
## 14 R4-P17       96  NA   none       silver, r… red, blue         NA none  femin…
## 15 Shaak Ti    178  57   none       red, blue… black             NA fema… femin…
## 16 Rey          NA  NA   brown      light      hazel             NA fema… femin…
## 17 Captain…     NA  NA   none       none       unknown           NA fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```
]

---
count: false

``` r
starwars %>%
  filter(gender == "feminine") %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw6-auto[

```
## # A tibble: 6 × 14
## # Groups:   species [3]
##   name      height  mass hair_color skin_color eye_color birth_year sex   gender
##   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
## 1 Leia Org…    150  49   brown      light      brown             19 fema… femin…
## 2 Beru Whi…    165  75   brown      light      blue              47 fema… femin…
## 3 Padmé Am…    185  45   brown      light      brown             46 fema… femin…
## 4 Ayla Sec…    178  55   none       blue       hazel             48 fema… femin…
## 5 Luminara…    170  56.2 black      yellow     blue              58 fema… femin…
## 6 Barriss …    166  50   black      yellow     blue              40 fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```
]

---
count: false

``` r
starwars %>%
  filter(gender == "feminine") %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(mean(birth_year))
```
]
 
.panel2-sw6-auto[

```
## # A tibble: 3 × 2
##   species  `mean(birth_year)`
##   <chr>                 <dbl>
## 1 Human                  37.3
## 2 Mirialan               49  
## 3 Twi'lek                48
```
]

---
count: false

``` r
starwars %>%
  filter(gender == "feminine") %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(mean(birth_year)) %>%
* rename(`female mean age by species` =
*          `mean(birth_year)`)
```
]
 
.panel2-sw6-auto[

```
## # A tibble: 3 × 2
##   species  `female mean age by species`
##   <chr>                           <dbl>
## 1 Human                            37.3
## 2 Mirialan                         49  
## 3 Twi'lek                          48
```
]

---
count: false

``` r
starwars %>%
  filter(gender == "feminine") %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(mean(birth_year)) %>%
  rename(`female mean age by species` =
           `mean(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw6-auto[

---
count: false

---

## Measures of Spread
**Sample Standard Deviation**

``` r
*starwars
```
]
 
.panel2-sw7-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw7-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw7-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(sd(birth_year))
```
]
 
.panel2-sw7-auto[

```
## # A tibble: 11 × 2
##    species      `sd(birth_year)`
##    <chr>                   <dbl>
##  1 Cerean                   NA  
##  2 Ewok                     NA  
##  3 Gungan                   NA  
##  4 Human                    23.1
##  5 Kel Dor                  NA  
##  6 Mirialan                 12.7
##  7 Mon Calamari             NA  
##  8 Trandoshan               NA  
##  9 Twi'lek                  NA  
## 10 Wookiee                  NA  
## 11 Zabrak                   NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(sd(birth_year)) %>%
* rename(`age standard deviation by species` =
*          `sd(birth_year)`)
```
]
 
.panel2-sw7-auto[

```
## # A tibble: 11 × 2
##    species      `age standard deviation by species`
##    <chr>                                      <dbl>
##  1 Cerean                                      NA  
##  2 Ewok                                        NA  
##  3 Gungan                                      NA  
##  4 Human                                       23.1
##  5 Kel Dor                                     NA  
##  6 Mirialan                                    12.7
##  7 Mon Calamari                                NA  
##  8 Trandoshan                                  NA  
##  9 Twi'lek                                     NA  
## 10 Wookiee                                     NA  
## 11 Zabrak                                      NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(sd(birth_year)) %>%
  rename(`age standard deviation by species` =
           `sd(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw7-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(sd(birth_year)) %>%
  rename(`age standard deviation by species` =
           `sd(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw7-auto[

```
## # A tibble: 2 × 2
##   species  `age standard deviation by species`
##   <chr>                                  <dbl>
## 1 Human                                   23.1
## 2 Mirialan                                12.7
```
]

---

**Population Standard Deviation**<br><br>
Reflecting common assumptions and practice, most descriptive statistics in R do not assume that you have an entire population. So you should ***always assume that you have a sample unless the description explicitly says otherwise***. When you do come across with a population, run the following first

``` r
pop_sd <- function(x) sd(x) * (length(x)-1) / length(x)
```

...

---

... and then calculate the population standard deviation

``` r
*starwars
```
]
 
.panel2-sw8-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw8-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw8-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(pop_sd(birth_year))
```
]
 
.panel2-sw8-auto[

```
## # A tibble: 11 × 2
##    species      `pop_sd(birth_year)`
##    <chr>                       <dbl>
##  1 Cerean                      NA   
##  2 Ewok                        NA   
##  3 Gungan                      NA   
##  4 Human                       21.8 
##  5 Kel Dor                     NA   
##  6 Mirialan                     6.36
##  7 Mon Calamari                NA   
##  8 Trandoshan                  NA   
##  9 Twi'lek                     NA   
## 10 Wookiee                     NA   
## 11 Zabrak                      NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_sd(birth_year)) %>%
* rename(`age standard deviation by species` =
*          `pop_sd(birth_year)`)
```
]
 
.panel2-sw8-auto[

```
## # A tibble: 11 × 2
##    species      `age standard deviation by species`
##    <chr>                                      <dbl>
##  1 Cerean                                     NA   
##  2 Ewok                                       NA   
##  3 Gungan                                     NA   
##  4 Human                                      21.8 
##  5 Kel Dor                                    NA   
##  6 Mirialan                                    6.36
##  7 Mon Calamari                               NA   
##  8 Trandoshan                                 NA   
##  9 Twi'lek                                    NA   
## 10 Wookiee                                    NA   
## 11 Zabrak                                     NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_sd(birth_year)) %>%
  rename(`age standard deviation by species` =
           `pop_sd(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw8-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_sd(birth_year)) %>%
  rename(`age standard deviation by species` =
           `pop_sd(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw8-auto[

```
## # A tibble: 2 × 2
##   species  `age standard deviation by species`
##   <chr>                                  <dbl>
## 1 Human                                  21.8 
## 2 Mirialan                                6.36
```
]

---

**Sample Variance**

``` r
*starwars
```
]
 
.panel2-sw9-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw9-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw9-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(var(birth_year))
```
]
 
.panel2-sw9-auto[

```
## # A tibble: 11 × 2
##    species      `var(birth_year)`
##    <chr>                    <dbl>
##  1 Cerean                     NA 
##  2 Ewok                       NA 
##  3 Gungan                     NA 
##  4 Human                     533.
##  5 Kel Dor                    NA 
##  6 Mirialan                  162 
##  7 Mon Calamari               NA 
##  8 Trandoshan                 NA 
##  9 Twi'lek                    NA 
## 10 Wookiee                    NA 
## 11 Zabrak                     NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(var(birth_year)) %>%
* rename(`age variance by species` =
*          `var(birth_year)`)
```
]
 
.panel2-sw9-auto[

```
## # A tibble: 11 × 2
##    species      `age variance by species`
##    <chr>                            <dbl>
##  1 Cerean                             NA 
##  2 Ewok                               NA 
##  3 Gungan                             NA 
##  4 Human                             533.
##  5 Kel Dor                            NA 
##  6 Mirialan                          162 
##  7 Mon Calamari                       NA 
##  8 Trandoshan                         NA 
##  9 Twi'lek                            NA 
## 10 Wookiee                            NA 
## 11 Zabrak                             NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(var(birth_year)) %>%
  rename(`age variance by species` =
           `var(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw9-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(var(birth_year)) %>%
  rename(`age variance by species` =
           `var(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw9-auto[

```
## # A tibble: 2 × 2
##   species  `age variance by species`
##   <chr>                        <dbl>
## 1 Human                         533.
## 2 Mirialan                      162
```
]

<br>
.pull-right[.footnote[You can find a list of commands that may be used with `summarise` [here](https://www.guru99.com/r-aggregate-function.html)]]

---

**Population Variance**<br><br>
Paralleling the arguement given about the population standard deviation, when you have a known population and want to find the variance, first run

``` r
pop_var <- function(x) var(x) * (length(x)-1) / length(x)
```

...

---

... and then you can calculate the population variance

``` r
*starwars
```
]
 
.panel2-sw10-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw10-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw10-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(pop_var(birth_year))
```
]
 
.panel2-sw10-auto[

```
## # A tibble: 11 × 2
##    species      `pop_var(birth_year)`
##    <chr>                        <dbl>
##  1 Cerean                         NA 
##  2 Ewok                           NA 
##  3 Gungan                         NA 
##  4 Human                         504.
##  5 Kel Dor                        NA 
##  6 Mirialan                       81 
##  7 Mon Calamari                   NA 
##  8 Trandoshan                     NA 
##  9 Twi'lek                        NA 
## 10 Wookiee                        NA 
## 11 Zabrak                         NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_var(birth_year)) %>%
* rename(`age variance by species` =
*          `pop_var(birth_year)`)
```
]
 
.panel2-sw10-auto[

```
## # A tibble: 11 × 2
##    species      `age variance by species`
##    <chr>                            <dbl>
##  1 Cerean                             NA 
##  2 Ewok                               NA 
##  3 Gungan                             NA 
##  4 Human                             504.
##  5 Kel Dor                            NA 
##  6 Mirialan                           81 
##  7 Mon Calamari                       NA 
##  8 Trandoshan                         NA 
##  9 Twi'lek                            NA 
## 10 Wookiee                            NA 
## 11 Zabrak                             NA
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_var(birth_year)) %>%
  rename(`age variance by species` =
           `pop_var(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw10-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(pop_var(birth_year)) %>%
  rename(`age variance by species` =
           `pop_var(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw10-auto[

```
## # A tibble: 2 × 2
##   species  `age variance by species`
##   <chr>                        <dbl>
## 1 Human                         504.
## 2 Mirialan                       81
```
]

---

### Side Note: More about Summarise

You can find a list of commands that may be used with `summarise` [here](https://www.guru99.com/r-aggregate-function.html)

.right[.footnote[<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5cb85c;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg>]]

---

# Range
**Maximum**

``` r
*starwars
```
]
 
.panel2-sw11-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw11-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw11-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(max(birth_year))
```
]
 
.panel2-sw11-auto[

```
## # A tibble: 11 × 2
##    species      `max(birth_year)`
##    <chr>                    <dbl>
##  1 Cerean                      92
##  2 Ewok                         8
##  3 Gungan                      52
##  4 Human                      102
##  5 Kel Dor                     22
##  6 Mirialan                    58
##  7 Mon Calamari                41
##  8 Trandoshan                  53
##  9 Twi'lek                     48
## 10 Wookiee                    200
## 11 Zabrak                      54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year)) %>%
* rename(`maximum age by species` =
*          `max(birth_year)`)
```
]
 
.panel2-sw11-auto[

```
## # A tibble: 11 × 2
##    species      `maximum age by species`
##    <chr>                           <dbl>
##  1 Cerean                             92
##  2 Ewok                                8
##  3 Gungan                             52
##  4 Human                             102
##  5 Kel Dor                            22
##  6 Mirialan                           58
##  7 Mon Calamari                       41
##  8 Trandoshan                         53
##  9 Twi'lek                            48
## 10 Wookiee                           200
## 11 Zabrak                             54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year)) %>%
  rename(`maximum age by species` =
           `max(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw11-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year)) %>%
  rename(`maximum age by species` =
           `max(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw11-auto[

---

**Minimum**

``` r
*starwars
```
]
 
.panel2-sw12-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw12-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw12-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(min(birth_year))
```
]
 
.panel2-sw12-auto[

```
## # A tibble: 11 × 2
##    species      `min(birth_year)`
##    <chr>                    <dbl>
##  1 Cerean                      92
##  2 Ewok                         8
##  3 Gungan                      52
##  4 Human                       19
##  5 Kel Dor                     22
##  6 Mirialan                    40
##  7 Mon Calamari                41
##  8 Trandoshan                  53
##  9 Twi'lek                     48
## 10 Wookiee                    200
## 11 Zabrak                      54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(min(birth_year)) %>%
* rename(`minimum age by species` =
*          `min(birth_year)`)
```
]
 
.panel2-sw12-auto[

```
## # A tibble: 11 × 2
##    species      `minimum age by species`
##    <chr>                           <dbl>
##  1 Cerean                             92
##  2 Ewok                                8
##  3 Gungan                             52
##  4 Human                              19
##  5 Kel Dor                            22
##  6 Mirialan                           40
##  7 Mon Calamari                       41
##  8 Trandoshan                         53
##  9 Twi'lek                            48
## 10 Wookiee                           200
## 11 Zabrak                             54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(min(birth_year)) %>%
  rename(`minimum age by species` =
           `min(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw12-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(min(birth_year)) %>%
  rename(`minimum age by species` =
           `min(birth_year)`) %>%
  ungroup() %>%
* na.omit()
```
]
 
.panel2-sw12-auto[

---

**Range**

``` r
*starwars
```
]
 
.panel2-sw13-auto[

---
count: false

``` r
starwars %>%
* group_by(species)
```
]
 
.panel2-sw13-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
* na.omit()
```
]
 
.panel2-sw13-auto[

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
* summarise(max(birth_year),
*           min(birth_year))
```
]
 
.panel2-sw13-auto[

```
## # A tibble: 11 × 3
##    species      `max(birth_year)` `min(birth_year)`
##    <chr>                    <dbl>             <dbl>
##  1 Cerean                      92                92
##  2 Ewok                         8                 8
##  3 Gungan                      52                52
##  4 Human                      102                19
##  5 Kel Dor                     22                22
##  6 Mirialan                    58                40
##  7 Mon Calamari                41                41
##  8 Trandoshan                  53                53
##  9 Twi'lek                     48                48
## 10 Wookiee                    200               200
## 11 Zabrak                      54                54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year),
            min(birth_year)) %>%
* rename(`max` = `max(birth_year)`)
```
]
 
.panel2-sw13-auto[

```
## # A tibble: 11 × 3
##    species        max `min(birth_year)`
##    <chr>        <dbl>             <dbl>
##  1 Cerean          92                92
##  2 Ewok             8                 8
##  3 Gungan          52                52
##  4 Human          102                19
##  5 Kel Dor         22                22
##  6 Mirialan        58                40
##  7 Mon Calamari    41                41
##  8 Trandoshan      53                53
##  9 Twi'lek         48                48
## 10 Wookiee        200               200
## 11 Zabrak          54                54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year),
            min(birth_year)) %>%
  rename(`max` = `max(birth_year)`) %>%
* rename(`min` = `min(birth_year)`)
```
]
 
.panel2-sw13-auto[

```
## # A tibble: 11 × 3
##    species        max   min
##    <chr>        <dbl> <dbl>
##  1 Cerean          92    92
##  2 Ewok             8     8
##  3 Gungan          52    52
##  4 Human          102    19
##  5 Kel Dor         22    22
##  6 Mirialan        58    40
##  7 Mon Calamari    41    41
##  8 Trandoshan      53    53
##  9 Twi'lek         48    48
## 10 Wookiee        200   200
## 11 Zabrak          54    54
```
]

---
count: false

``` r
starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(max(birth_year),
            min(birth_year)) %>%
  rename(`max` = `max(birth_year)`) %>%
  rename(`min` = `min(birth_year)`) %>%
* ungroup()
```
]
 
.panel2-sw13-auto[

---
count: false

---
count: false

```
## # A tibble: 11 × 4
##    species        max   min `age range by species`
##    <chr>        <dbl> <dbl>                  <dbl>
##  1 Cerean          92    92                      0
##  2 Ewok             8     8                      0
##  3 Gungan          52    52                      0
##  4 Human          102    19                     83
##  5 Kel Dor         22    22                      0
##  6 Mirialan        58    40                     18
##  7 Mon Calamari    41    41                      0
##  8 Trandoshan      53    53                      0
##  9 Twi'lek         48    48                      0
## 10 Wookiee        200   200                      0
## 11 Zabrak          54    54                      0
```
]

.left[<svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#5bc0de;overflow:visible;position:relative;"><path d="M0 48C0 21.5 21.5 0 48 0l0 48V441.4l130.1-92.9c8.3-6 19.6-6 27.9 0L336 441.4V48H48V0H336c26.5 0 48 21.5 48 48V488c0 9-5 17.2-13 21.3s-17.6 3.4-24.9-1.8L192 397.5 37.9 507.5c-7.3 5.2-16.9 5.9-24.9 1.8S0 497 0 488V48z"/></svg>]

---

# Plots

To start, we'll use the sample mean age for all of the species

``` r
starwars_by_species <- 
  starwars %>%
  group_by(species) %>%
  na.omit() %>%
  summarise(mean(birth_year)) %>%
  rename(`mean age` = 
           `mean(birth_year)`) %>%
  ungroup() %>%
  arrange(`mean age`)
```

---

### Side Note: R Graph Gallery

Want some inspiration or just want to copy code? Good! Head over to [The R Graph Gallery](https://www.r-graph-gallery.com/) to see some examples of basic visualizations and plots you can do in R right now.

---

## Bar Plot

``` r
*ggplot(starwars_by_species,
*      aes(x = species,
*          y = `mean age`,
*          fill = `mean age`))
```
]
 
.panel2-sw15-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw15_auto_01_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = species,
           y = `mean age`,
           fill = `mean age`)) +
* geom_bar(stat='identity')
```
]
 
.panel2-sw15-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw15_auto_02_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = species,
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
* scale_fill_gradient(low = "#d9534f",
*                     high = "#428bca")
```
]
 
.panel2-sw15-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw15_auto_03_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = species,
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
* theme_minimal()
```
]
 
.panel2-sw15-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw15_auto_04_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = species,
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
  theme_minimal() +
* theme(axis.text.x = element_text(angle = 45))
```
]
 
.panel2-sw15-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw15_auto_05_output-1.png)
]

---

## Ordered Bar Plot 
Use `reorder` to order the bars from least to greatest.

``` r
*ggplot(starwars_by_species,
*      aes(x = reorder(species,
*                      `mean age`),
*          y = `mean age`,
*          fill = `mean age`))
```
]
 
.panel2-sw16-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw16_auto_01_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           fill = `mean age`)) +
* geom_bar(stat='identity')
```
]
 
.panel2-sw16-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw16_auto_02_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
* scale_fill_gradient(low = "#d9534f",
*                     high = "#428bca")
```
]
 
.panel2-sw16-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw16_auto_03_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
* theme_minimal()
```
]
 
.panel2-sw16-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw16_auto_04_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
  theme_minimal() +
* theme(axis.text.x = element_text(angle = 45))
```
]
 
.panel2-sw16-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw16_auto_05_output-1.png)
]

---

### Side Note: The Other Direction
Want greatest to least? Stick a `-` in front of `mean age by species` within `reorder()`.

``` r
*ggplot(starwars_by_species,
*      aes(x = reorder(species,
*                      -`mean age`),
*          y = `mean age`,
*          fill = `mean age`))
```
]
 
.panel2-sw17-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw17_auto_01_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       -`mean age`),
           y = `mean age`,
           fill = `mean age`)) +
* geom_bar(stat='identity')
```
]
 
.panel2-sw17-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw17_auto_02_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       -`mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
* scale_fill_gradient(low = "#d9534f",
*                     high = "#428bca")
```
]
 
.panel2-sw17-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw17_auto_03_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       -`mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
* theme_minimal()
```
]
 
.panel2-sw17-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw17_auto_04_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       -`mean age`),
           y = `mean age`,
           fill = `mean age`)) +
  geom_bar(stat='identity') +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
  theme_minimal() +
* theme(axis.text.x = element_text(angle = 45))
```
]
 
.panel2-sw17-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw17_auto_05_output-1.png)
]

---

## Line Plot

``` r
*ggplot(starwars_by_species,
*      aes(x = reorder(species,
*                      `mean age`),
*          y = `mean age`,
*          group = 1,
*          color = `mean age`))
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_01_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           group = 1,
           color = `mean age`)) +
* geom_line(size = 1.5,
*           color = "#cccccc")
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_02_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           group = 1,
           color = `mean age`)) +
  geom_line(size = 1.5,
            color = "#cccccc") +
* geom_point(size = 4)
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_03_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           group = 1,
           color = `mean age`)) +
  geom_line(size = 1.5,
            color = "#cccccc") +
  geom_point(size = 4) +
* scale_color_gradient(low = "#d9534f",
*                      high = "#428bca")
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_04_output-1.png)
]

---
count: false

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           group = 1,
           color = `mean age`)) +
  geom_line(size = 1.5,
            color = "#cccccc") +
  geom_point(size = 4) +
  scale_color_gradient(low = "#d9534f",
                       high = "#428bca") +
  theme_minimal() +
* theme(axis.text.x = element_text(angle = 45,
*                                  vjust = 0.6,
*                                  hjust = 0.5),
*       axis.title.y = element_blank())
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_06_output-1.png)
]

---
count: false

``` r
ggplot(starwars_by_species,
       aes(x = reorder(species,
                       `mean age`),
           y = `mean age`,
           group = 1,
           color = `mean age`)) +
  geom_line(size = 1.5,
            color = "#cccccc") +
  geom_point(size = 4) +
  scale_color_gradient(low = "#d9534f",
                       high = "#428bca") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45,
                                   vjust = 0.6,
                                   hjust = 0.5),
        axis.title.y = element_blank()) +
*  labs(x = "species")
```
]
 
.panel2-sw18-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw18_auto_07_output-1.png)
]

---

## Pie Chart (Useless)

``` r
*starwars_by_species
```
]
 
.panel2-sw19-auto[

```
## # A tibble: 11 × 2
##    species      `mean age`
##    <chr>             <dbl>
##  1 Ewok                8  
##  2 Kel Dor            22  
##  3 Mon Calamari       41  
##  4 Human              45.5
##  5 Twi'lek            48  
##  6 Mirialan           49  
##  7 Gungan             52  
##  8 Trandoshan         53  
##  9 Zabrak             54  
## 10 Cerean             92  
## 11 Wookiee           200
```
]

---
count: false

``` r
starwars_by_species %>%
* arrange(desc(`mean age`))
```
]
 
.panel2-sw19-auto[

```
## # A tibble: 11 × 2
##    species      `mean age`
##    <chr>             <dbl>
##  1 Wookiee           200  
##  2 Cerean             92  
##  3 Zabrak             54  
##  4 Trandoshan         53  
##  5 Gungan             52  
##  6 Mirialan           49  
##  7 Twi'lek            48  
##  8 Human              45.5
##  9 Mon Calamari       41  
## 10 Kel Dor            22  
## 11 Ewok                8
```
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
* mutate(prop = `mean age` /
*          sum(starwars_by_species$`mean age`) * 100)
```
]
 
.panel2-sw19-auto[

```
## # A tibble: 11 × 3
##    species      `mean age`  prop
##    <chr>             <dbl> <dbl>
##  1 Wookiee           200   30.1 
##  2 Cerean             92   13.8 
##  3 Zabrak             54    8.13
##  4 Trandoshan         53    7.98
##  5 Gungan             52    7.83
##  6 Mirialan           49    7.37
##  7 Twi'lek            48    7.22
##  8 Human              45.5  6.85
##  9 Mon Calamari       41    6.17
## 10 Kel Dor            22    3.31
## 11 Ewok                8    1.20
```
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
* mutate(ypos = cumsum(prop) - 0.5*prop)
```
]
 
.panel2-sw19-auto[

```
## # A tibble: 11 × 4
##    species      `mean age`  prop  ypos
##    <chr>             <dbl> <dbl> <dbl>
##  1 Wookiee           200   30.1   15.0
##  2 Cerean             92   13.8   37.0
##  3 Zabrak             54    8.13  48.0
##  4 Trandoshan         53    7.98  56.1
##  5 Gungan             52    7.83  64.0
##  6 Mirialan           49    7.37  71.6
##  7 Twi'lek            48    7.22  78.9
##  8 Human              45.5  6.85  85.9
##  9 Mon Calamari       41    6.17  92.4
## 10 Kel Dor            22    3.31  97.1
## 11 Ewok                8    1.20  99.4
```
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
  mutate(ypos = cumsum(prop) - 0.5*prop) %>%
*ggplot(aes(x = "",
*          y = prop,
*          fill = `mean age`))
```
]
 
.panel2-sw19-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw19_auto_05_output-1.png)
]

---
count: false

---
count: false

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
  mutate(ypos = cumsum(prop) - 0.5*prop) %>%
ggplot(aes(x = "",
           y = prop,
           fill = `mean age`)) +
  geom_bar(stat = "identity",
           width = 1,
           color = "white")  +
  coord_polar("y", start = 0) +
* geom_label(aes(y = ypos,
*                label = `species`),
*            color = "white",
*            size = 6,
*            show.legend = FALSE)
```
]
 
.panel2-sw19-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw19_auto_08_output-1.png)
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
  mutate(ypos = cumsum(prop) - 0.5*prop) %>%
ggplot(aes(x = "",
           y = prop,
           fill = `mean age`)) +
  geom_bar(stat = "identity",
           width = 1,
           color = "white")  +
  coord_polar("y", start = 0) +
  geom_label(aes(y = ypos,
                 label = `species`),
             color = "white",
             size = 6,
             show.legend = FALSE) +
* scale_fill_gradient(low = "#d9534f",
*                     high = "#428bca")
```
]
 
.panel2-sw19-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw19_auto_09_output-1.png)
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
  mutate(ypos = cumsum(prop) - 0.5*prop) %>%
ggplot(aes(x = "",
           y = prop,
           fill = `mean age`)) +
  geom_bar(stat = "identity",
           width = 1,
           color = "white")  +
  coord_polar("y", start = 0) +
  geom_label(aes(y = ypos,
                 label = `species`),
             color = "white",
             size = 6,
             show.legend = FALSE) +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
* theme_void()
```
]
 
.panel2-sw19-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw19_auto_10_output-1.png)
]

---
count: false

``` r
starwars_by_species %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) * 100) %>%
  mutate(ypos = cumsum(prop) - 0.5*prop) %>%
ggplot(aes(x = "",
           y = prop,
           fill = `mean age`)) +
  geom_bar(stat = "identity",
           width = 1,
           color = "white")  +
  coord_polar("y", start = 0) +
  geom_label(aes(y = ypos,
                 label = `species`),
             color = "white",
             size = 6,
             show.legend = FALSE) +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
  theme_void()
```
]
 
.panel2-sw19-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw19_auto_11_output-1.png)
]

---

## Filtered Pie Chart (A Little Less Useless)

``` r
*starwars_by_species
```
]
 
.panel2-sw20-auto[

---
count: false

``` r
starwars_by_species %>%
* filter(species %in% c("Human",
*                       "Twi'lek",
*                       "Mon Calamari",
*                       "Wookiee"))
```
]
 
.panel2-sw20-auto[

```
## # A tibble: 4 × 2
##   species      `mean age`
##   <chr>             <dbl>
## 1 Mon Calamari       41  
## 2 Human              45.5
## 3 Twi'lek            48  
## 4 Wookiee           200
```
]

---
count: false

``` r
starwars_by_species %>%
  filter(species %in% c("Human",
                        "Twi'lek",
                        "Mon Calamari",
                        "Wookiee")) %>%
* arrange(desc(`mean age`))
```
]
 
.panel2-sw20-auto[

```
## # A tibble: 4 × 2
##   species      `mean age`
##   <chr>             <dbl>
## 1 Wookiee           200  
## 2 Twi'lek            48  
## 3 Human              45.5
## 4 Mon Calamari       41
```
]

---
count: false

``` r
starwars_by_species %>%
  filter(species %in% c("Human",
                        "Twi'lek",
                        "Mon Calamari",
                        "Wookiee")) %>%
  arrange(desc(`mean age`)) %>%
* mutate(prop = `mean age` /
*          sum(starwars_by_species$`mean age`) *100)
```
]
 
.panel2-sw20-auto[

```
## # A tibble: 4 × 3
##   species      `mean age`  prop
##   <chr>             <dbl> <dbl>
## 1 Wookiee           200   30.1 
## 2 Twi'lek            48    7.22
## 3 Human              45.5  6.85
## 4 Mon Calamari       41    6.17
```
]

---
count: false

```
## # A tibble: 4 × 4
##   species      `mean age`  prop  ypos
##   <chr>             <dbl> <dbl> <dbl>
## 1 Wookiee           200   30.1   15.0
## 2 Twi'lek            48    7.22  33.7
## 3 Human              45.5  6.85  40.7
## 4 Mon Calamari       41    6.17  47.3
```
]

---
count: false

---
count: false

---
count: false

---
count: false

``` r
starwars_by_species %>%
  filter(species %in% c("Human",
                        "Twi'lek",
                        "Mon Calamari",
                        "Wookiee")) %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop) %>%
 ggplot(aes(x = "",
            y = prop)) +
  geom_bar(aes(fill = `mean age`),
           stat = "identity",
           width = 1,
           color = "white") +
  coord_polar("y", start = 0) +
* geom_label(aes(y = ypos,
*                label = paste0(`species`,
*                               " (", round(prop, 0), "%)"),
*            color = "white",
*            size = 6),
*            show.legend = FALSE)
```
]
 
.panel2-sw20-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw20_auto_09_output-1.png)
]

---
count: false

``` r
starwars_by_species %>%
  filter(species %in% c("Human",
                        "Twi'lek",
                        "Mon Calamari",
                        "Wookiee")) %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop) %>%
 ggplot(aes(x = "",
            y = prop)) +
  geom_bar(aes(fill = `mean age`),
           stat = "identity",
           width = 1,
           color = "white") +
  coord_polar("y", start = 0) +
  geom_label(aes(y = ypos,
                 label = paste0(`species`,
                                " (", round(prop, 0), "%)"),
             color = "white",
             size = 6),
             show.legend = FALSE) +
* scale_fill_gradient(low = "#d9534f",
*                     high = "#428bca")
```
]
 
.panel2-sw20-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw20_auto_10_output-1.png)
]

---
count: false

``` r
starwars_by_species %>%
  filter(species %in% c("Human",
                        "Twi'lek",
                        "Mon Calamari",
                        "Wookiee")) %>%
  arrange(desc(`mean age`)) %>%
  mutate(prop = `mean age` /
           sum(starwars_by_species$`mean age`) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop) %>%
 ggplot(aes(x = "",
            y = prop)) +
  geom_bar(aes(fill = `mean age`),
           stat = "identity",
           width = 1,
           color = "white") +
  coord_polar("y", start = 0) +
  geom_label(aes(y = ypos,
                 label = paste0(`species`,
                                " (", round(prop, 0), "%)"),
             color = "white",
             size = 6),
             show.legend = FALSE) +
  scale_fill_gradient(low = "#d9534f",
                      high = "#428bca") +
* theme_void()
```
]
 
.panel2-sw20-auto[
![](Descriptive-statistics-in-R_files/figure-html/sw20_auto_11_output-1.png)
]

---

## Thats it!