# Overview ----------------------------------------------------------------
	# Assignment 1: Analysis of the protest data from Crowd Love
	# For each question/prompt, write the necessary code to calculate the answer.
	# For grading, it's important that you store your answers in the variable names
	# listed with each question in `backtics`.
	# For each prompt marked `Reflection`, please write a response
	# in your `README.md` file.
	# Part 1:Set up -----------------------------------------------------------
	# In this section, you're loading the data and necessary packages.
	# Load the `stringr` package, which you'll use later.
	# Load the data from https://countlove.org/data/data.csv
	# into a variable called `protests`
	# How many protests are in the dataset? `num_protests`
	# How much information is available about each protest? `num_features`
	# Part 2: Attendees -------------------------------------------------------
	# In this section, you're exploring the number of attendees.
	# Extract the `Attendees` column into a variable called `num_attendees`
	# What is the lowest number of attendees? `min_attendees`
	# (hint for this and other calculations: you'll need to consider missing values)
	# What is the highest number of attendees? `max_attendees`
	# What is the mean number of attendees? `mean_attendees`
	# What is the median number of attendees? `median_attendees`
	# What is the difference between the mean and median number of attendees?
	# `mean_median_diff`
	# Reflection: What does the difference between the mean and the median
	# tell you about the *distribution* of the data? (if you're unfamiliar with
	# working with distibutions, feel free to ask your TA for clarification)
	# To further assess the distribution of values, create a boxplot of the number
	# of attendees using the `boxplot()` function.
	# Store the plot in a variable called `attendess_distribution`
	# (Note, we'll use much more refined plotting methods, and pay far
	# more attention to detail later in the course)
	# Create another boxplot of the *log* of the number of attendees.
	# Store the plot in a variable `log_attendees_distribution`.
	# (note, you will see a warning in the console, which is expected)
	# Part 3: Locations -------------------------------------------------------
	# In this section, you're exploring where protests happened.
	# Extract the `Location` column into a variable called `locations`
	# How many *unique* locations are in the dataset? `num_locations`
	# How many protests occured in Washington? `num_in_wa`
	# (hint: use a function from the stringr package to detect the letters "WA")
	# What proportion of protests occured in Washington? `prop_in_wa`
	# Reflection: Does the number of protests in Washington surprise you?
	# Why or why not?
	# Write a function `count_in_location()` that accepts (as a parameter)
	# a `location` name, and returns the sentence (note: spacing and punctuation):
	# "There were N protests in LOCATION.", where N is the number of
	# protests that occured in that location, and LOCATION is the parameter that
	# was provided into the function.
	# Note, you should count the number of locations that *match* the parameter
	# put into the function, so `Seattle` should be a match for "Seattle, WA"
	# Use your function above to describe the number of protests in "Washington, DC"
	# `dc_summary`
	# Use your function above to describe the number of protests in "Minneapolis"
	# `minneapolis_summary`
	# Create a new vector `states` which is the last two characters of each
	# value in the `locations` vector. Hint, you may want to again use the
	# `stringr` package
	# Create a vector of the unique states in your dataset. `uniq_states`
	# Create a summary sentence for each state by passing your `uniq_states`
	# variable and `count_in_location` variables to the `sapply()` function.
	# Store your results in `state_summary`
	# (don't miss how amazing this is! Very powerful to apply your function to an
	# entire vector *at once* with `sapply()`)
	# Create a summary table by passing your `states` variable to the `table()`
	# funciton, and storing the result in a variable `state_table`.
	# Optional: use the View() function to more easily read the table
	# Reflection: Looking at the `state_table` variable, what data quality issues
	# do you notice, and how would you use that to change your analysis (no need
	# to actually change your analysis)?
	# What was the maximum number of protests in a state? `max_in_state`
	# (hint: use your `state_table` variable)
	# Part 4: Dates -----------------------------------------------------------
	# In this section, you're exploring *when* protests happened.
	# Extract the `Date` column into a variable called `dates` by passing the
	# column to the `as.Date()` function (this will process the values as dates,
	# which are *luckily* already in an optimal format for parsing)
	# What is the most recent date in the dataset? `most_recent`
	# What is the earliest date in the dataset? `earliest`
	# What is the length of the timespan of the dataset? `time_span`
	# hint: R can do math with dates pretty well by default!
	# Create a vector of the dates that are in 2020 `in_2020`
	# Create a vector of the dates that are in 2019. `in_2019`
	# What is the ratio of the number of protests in 2020 comparted to 2019?
	# `ratio_2020_2019`
	# Reflection: Does the change in the number of protests from 2019 to 2020
	# surprise you? Why or why not?
	# Write a function `count_on_date()` that accecpts as a parameter a `date`,
	# and returns the sentence:
	# "There were N protests on DATE.", where N is the number of protests on that
	# date, and DATE is the date provided
	# Using your function you just wrote, how many protests were there on
	# May 24th, 2020? `num_may_24`
	# Using your function you just wrote, how many protests were there on
	# May 31th, 2020? `num_on_may_31`
	# For more on this timeline, see:
	# https://www.nytimes.com/article/george-floyd-protests-timeline.html
	# How many protests occured each month in 2020? `by_month_table`
	# Hint: use the `months()` function, your `in_2020` dates, and the `table()`
	# Function. If you like, you can do this in multiple different steps.
	# As a comparison, let's assess the change between July 2019 and July 2020.
	# What is the *difference* in the number of protests between July 2020 and
	# July 2019? You'll want to do this in multiple steps as you see fit, though
	# your answer should be stored in the variable `change_july_protests`.
	# Reflection: do a bit of research. Find at least *two specific policies* that
	# have been changed as a result of protests in 2020. These may be at the
	# city, state, or University level. Please provide a basic summary, as well as a
	# link to each article.
	# Part 5: Protest Purpose -------------------------------------------------
	# In this section, you're exploring *why* protests happened
	# Extract the `Event..legacy..see.tags.` column into a variable called `purpose`
	# How many different purposes are listed in the dataset? `num_purposes`
	# That's quite a few -- if you look at -- View() -- the vector, you'll notice
	# a common pattern for each purpose. It's listed as:
	# SOME_PURPOSE (additiona_detail)
	# To get a higher level summary, create a variable `high_level_purpse` by
	# extracting *everything before the first parenthesis* in each value
	# in the vector. For example, from "Civil Rights (Black Women's March)"
	# you would extract "Civil Rights". You'll also have to *remove the space*
	# before the first parenthasis.
	# Hint: this will take a little bit of googling // trial and error. Be patient!
	# How many "high level" purposes have you identified? `num_high_level`
	# Create a table that counts the number of protests for each high level purpose
	# `high_level_table`
	# Reflection: Take a look (`View()`) your `high_level_table` variable. What
	# picture does this paint of the U.S.?
	# Part 6: Independent Exploration -----------------------------------------
	# As a last step, you should write your own function that allows you to
	# quickly ask questions of the dataset. For example, in the above sections,
	# you wrote functions to ask the same question about different months, or
	# locations. If you need any guidance here, feel free to ask!