Warm-up

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class: title-slide

# Warm-up

### Session 1

<div class="title-footer">
  <img src="images/academy-logo.png">
  <div> August 12th, 2024</div>
</div>

???

We're going to start off with some review of what we've learned for the past several weeks.

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class: inverse

# Go to **Conf - Warm-up**

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<img src="images/welcome/campsite_warmup.png" width="60%" style="display: block; margin: auto;" />

[rconf.posit.academy](https://rconf.posit.academy/)
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## 🚀 Warm-up

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![](images/welcome/your-turn-example.png)

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* __Work together__ with your neighbors

* There are often several different ways of getting to the right answer.

* After 1-2 minutes, we'll go over the answer together. And then move on to the next question.

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## Done

![](images/welcome/green-square.png)
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## Help

![](images/welcome/pink-square.png)
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???

You'll use the sticky system to signal that you're done or your need help

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background-position: center
background-size: cover

# Seattle Pet Licenses data

Look for `data/seattle_pets.csv` in your Files pane

Source: https://data.seattle.gov/Community/Seattle-Pet-Licenses/jguv-t9rb/about_data

???

This data was retrieved from Seattle's Open Data Portal. It was last updated in July 2024.

It contains a list of current Seattle pet licenses, including animal type (species), pet's name, breed and the owner's ZIP code.

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class: your-turn

# Your Turn 1

**Read in the data saved in `data/seattle_pets.csv` and explore it. Can you recreate output that looks like this?**

💡 Hint: What function from dplyr gives you a quick glimpse of your data?

```
## Rows: 43,683
## Columns: 7
## $ license_issue_date <chr> "December 18 2015", "June 14 2016", "August 04 2016…
## $ license_number     <chr> "S107948", "S116503", "S119301", "962273", "S133113…
## $ animal_name        <chr> "Zen", "Misty", "Lyra", "Veronica", "Spider", "Maxx…
## $ species            <chr> "Cat", "Cat", "Cat", "Cat", "Cat", "Cat", "Cat", "C…
## $ primary_breed      <chr> "Domestic Longhair", "Siberian", "Mix", "Domestic L…
## $ secondary_breed    <chr> "Mix", NA, NA, NA, NA, NA, "Mix", "Mix", "Mix", "Mi…
## $ zip_code           <dbl> 98117, 98117, 98121, 98107, 98115, 98125, 98103, 98…
```

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## Solution 1

``` r
library(tidyverse)

seattle_pets <- read_csv("data/seattle_pets.csv")

glimpse(seattle_pets)
```

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class: your-turn

# Your Turn 2

**How many different species are represented in `seattle_pets`? How many pets of each species are there?**

💡 Hint: What function from dplyr lets you count the unique values of one or more variables?

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# Solution 2

.pull-left[

``` r
seattle_pets |> 
  count(species)
```

or...

``` r
seattle_pets |> 
  group_by(species) |> 
  summarize(n = n())
```

]

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```
## # A tibble: 4 × 2
##   species     n
##   <chr>   <int>
## 1 Cat     13935
## 2 Dog     29729
## 3 Goat       16
## 4 Pig         3
```

]

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# Solution 2

Because I was curious... what does one name a pet pig?

``` r
seattle_pets |> 
  filter(species == "Pig") |> 
  pull(animal_name)
```

```
## [1] "Millie"                "Calvin"                "Waffles Olivia McHart"
```

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class: your-turn

# Your Turn 3

**What is the most popular pet name in this data set?**

💡 Hint: Try using `slice_max()` from dplyr in your solution. Look up the help docs with `?slice_max`.

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# Solution 3

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``` r
seattle_pets |> 
  count(animal_name) |> 
  slice_max(order_by = n)
```

or...

``` r
seattle_pets |> 
  count(animal_name, sort = TRUE) |> 
  head(1)
```

or...

``` r
seattle_pets |> 
  count(animal_name) |> 
  filter(n == max(n))
```

]

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```
## # A tibble: 1 × 2
##   animal_name     n
##   <chr>       <int>
## 1 Luna          410
```

]

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class: your-turn

# Your Turn 4

**What are the top 10 most popular primary dog breeds?**

💡 Hint: Try using `count()` and `slice_max()` again in your solution -- which argument to `slice_max()` specifies the number of rows to return?

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# Solution 4

``` r
seattle_pets |> 
  filter(species == "Dog") |> 
  count(primary_breed) |> 
  slice_max(order_by = n, n = 10)
```

```
## # A tibble: 10 × 2
##    primary_breed                                      n
##    <chr>                                          <int>
##  1 Retriever, Labrador                             3025
##  2 Retriever, Golden                               1498
##  3 Chihuahua, Short Coat                           1485
##  4 German Shepherd                                  989
##  5 Poodle, Miniature                                889
##  6 Poodle, Standard                                 818
##  7 Terrier                                          814
##  8 Mixed Breed, Medium (up to 44 lbs fully grown)   787
##  9 Australian Shepherd                              726
## 10 Mixed Breed, Large (over 44 lbs fully grown)     717
```

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class: your-turn

# Your Turn 5 (last one!)

**Visualize the top 10 dog breeds, re-creating the plot below.**

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💡 Hint: Pay close attention to the x and y axes

💡 Hint: Start with your code from the previous exercise, and pipe this code to `ggplot()`:

``` r
seattle_pets |> 
  filter(species == "Dog") |> 
  count(primary_breed) |> 
  slice_max(order_by = n, n = 10) |> 
  ____ # add code here
```

]

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<img src="warm-up_files/figure-html/unnamed-chunk-16-1.png" width="80%" />

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# Solution 5

.pull-left[

``` r
seattle_pets |> 
  filter(species == "Dog") |> 
  count(primary_breed) |> 
  slice_max(order_by = n, n = 10) |> 
  ggplot(aes(x = n, y = primary_breed)) + 
  geom_col()
```

or ...

``` r
seattle_pets |> 
  filter(species == "Dog") |> 
  count(primary_breed) |> 
  slice_max(order_by = n, n = 10) |> 
  ggplot(aes(x = primary_breed, y = n)) + 
  geom_col() + 
  coord_flip()
```

]

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<img src="warm-up_files/figure-html/unnamed-chunk-19-1.png" width="80%" />

]

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# Nice work!

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# 🤔 Exploring `seattle_pets` further...

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What if we wanted visualize popular dog breeds in descending order?

We would need to handle **factors** (categorical variables).
]

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<img src="warm-up_files/figure-html/unnamed-chunk-20-1.png" width="90%" />

]

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# 🤔 Exploring `seattle_pets` further...

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What if we wanted visualize trends in number of new pet licences by month?

We would need to handle **dates**.
]

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]

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# 🤔 Exploring `seattle_pets` further...

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What if we wanted to explore pet names with a particular pattern?

e.g. Pets with "Sir" somewhere in their name.

We would need to handle **strings**.
]

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```
## # A tibble: 22 × 2
##    animal_name                          species
##    <chr>                                <chr>  
##  1 Sir Pounce                           Cat    
##  2 Sir Furcifer                         Cat    
##  3 Sir Thomas Sharpe                    Cat    
##  4 Sir Digby Chicken Caesar             Cat    
##  5 Sir Herlock Sholmes                  Cat    
##  6 Sir Daniel                           Cat    
##  7 Sir Dapplesox                        Cat    
##  8 Sir Robin Cashmoney Pouncealot       Cat    
##  9 Sir Mill                             Cat    
## 10 Sir Tuna                             Cat    
## 11 Sir Loafsalot                        Cat    
## 12 Sir Tater Tot                        Dog    
## 13 Sir CottonBall                       Dog    
## 14 Sir Walter Leroy Phillips            Dog    
## 15 Sir Waggleton                        Dog    
## 16 Ravindale's Sir Tristan              Dog    
## 17 Sir Francis                          Dog    
## 18 Sir Oliver Grayson                   Dog    
## 19 Sir Oliver                           Dog    
## 20 Sir Sammy Haralson Lawrence III Esq. Dog    
## 21 Sir Maximillion                      Dog    
## 22 Sir Roman Snoopy II                  Dog
```

]

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# Data types

Fortunately, the tidyverse provides us with tools to work with these different types of data...

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