---
title: "Lab 1.2 - The Seattle Pets Dataset"
author: "Author Goes Here"
date: "Semester Goes Here"
output:
  html_document:
    code_download: true
---

This RMarkdown document presents a preliminary analysis of Seattle's [pet licenses dataset](https://data.seattle.gov/Community/Seattle-Pet-Licenses/jguv-t9rb). It interleaves text, code, and code output. 

## Loading the Dataset

The following code *chunk* loads the tidyverse library, reads the CSV file containing the Seattle pet license dataset, and saves that dataset under the name `seattle_pets`. The code assumes that the CSV file is loaded in a `data` directory at the root of the course.

```{r message=FALSE}
library(tidyverse)
library(here)

seattle_pets <- read_csv(here("data/Seattle_Pet_Licenses.csv"))
```

The `here()` function from the [here library](https://here.r-lib.org/) creates a file path relative to the root of the project folder. This makes it easier for us to store datasets in a single, shared folder at the root of our course folders. As an alternative, we could hard-code the shared data path using `../data` (where `..` is interpreted as going up one level of the folder hierarchy).

## Viewing the Structure of the Dataset

We can now view the dataset as it is stored in R.

```{r}
seattle_pets
```

Based on the information provided in the previous section, we can see that the pets dataset contains how many of the following:

- Pets (i.e., rows, a.k.a. *records*):  🚧 ??
- Variables (i.e., columns, a.k.a. *fields*):  🚧 ??

## Analyzing the Dataset

We can now count the number of each species using the `count()` function.

```{r}
count(seattle_pets, Species, sort=TRUE)
```

🚧 Replace this line with a description of what the output of the last code chunk tells us.

🚧 Finally, add one more code chunk that computes the most popular names in the dataset and describes the results.
