---
title: "Optional exercises: data manipulation"
author: "INSERT YOUR NAME HERE"
date: "Due Date Here"
output: html_document
---

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As usual, all code below should follow the style guidelines from the lecture slides.

## Part 1. Data manipulation

Use the code below to load the building permits data. You may have to install the required packages.


```{r, results = F, message = F, warning = F}
library(readr)
library(dplyr)
library(tidyr)
permits <- read_csv("https://raw.githubusercontent.com/peteragao/STAT302-AUT2021/base/files/projects/01_data-visualization/Building_Permits_Clean.csv")
```

1. Use the ```filter``` function to create a new tibble with only permits issued in 2021.

2. Use the ```filter``` function to create a new tibble with only permits issued in 2021 for projects that add at least 1 housing unit.

3. Use the ```mutate``` function to create a new variable ```is_single_family``` that is ```TRUE``` if the permit is for a single family building.

4. Use the ```mutate``` function and ```ifelse``` function to create a new variable ```north_of_cut``` that has the value `"north"` if the permit is for a location in council districts 4, 5, 6, and the value `"south"` otherwise.

## Part 2. Summary statistics (split-apply-combine)

5. Calculate the number of permits by year using the `group_by` and `summarize` functions.

6. Calculate the number of housing units added by city council district. You may have to figure out what to do with `NA` values.

7. Use the ```filter``` function to create a new tibble with only permits issued in 2021 for projects that add at least 1 housing unit. Using your new tibble, calculate the average number of housing units added (for projects that add at least 1 housing unit) by city council district.