# Write a function called `get_state_data` that allows you to specify a state, # then saves a .csv file (`STATE_data.csv`) with observations from that state # This includes data about the state, as well as the counties in the state # You should use the full any.drinking dataset in this function (not just 2012)

# Demonstrate that you function works by passing "Utah" to the function state_Utah <- get_state_data(Utah)

############################ Binge drinking Dataset ############################

# In this section, you will ask a variety of questions regarding the # `binge_drinking.csv` dataset. More specifically, you will analyze a subset of # the observations of *just the counties* (exclude state/national estimates!). # You will store your answers in a *named list*, and at the end of the section, # Convert that list to a data frame, and write the data frame to a .csv file. # Pay close attention to the *names* to be used in the list.

# Create a dataframe with only the county level observations from the # `binge_driking.csv` dataset. You should (again) think of Washington D.C. as # a state, and therefore *exclude it here*. # However, you should include "county-like" areas such as parishes and boroughs county_data <- binge.drinking.csv %>% distinct(state)

# Create an empty list in which to store answers to the questions below.

# What is the average county level of binge drinking in 2012 for both sexes? # Store the number in your list as `avg_both_sexes`.

# What is the name of the county with the largest increase in male binge # drinking between 2002 and 2012? # Store the county name in your list as `largest_male_increase`.

# How many counties experienced an increase in male binge drinking between # 2002 and 2012? # Store the number in your list as `num_male_increase`.

# What fraction of counties experienced an increase in male binge drinking # between 2002 and 2012? # Store the fraction (num/total) in your list as `frac_male_increase`.

# How many counties experienced an increase in female binge drinking between # 2002 and 2012? # Store the number in your list as `num_female_increase`.

# What fraction of counties experienced an increase in female binge drinking # between 2002 and 2012? # Store the fraction (num/total) in your list as `frac_female_increase`.

# How many counties experienced a rise in female binge drinking *and* # a decline in male binge drinking? # Store the number in your list as `num_f_increase_m_decrease`.

# Convert your list to a data frame, and write the results # to the file `binge_info.csv`

# The next questions return *data frames as results*:

# What is the *minimum* level of binge drinking in each state in 2012 for # both sexes (across the counties)? Your answer should contain roughly 50 values # (one for each state), unless there are two counties in a state with the # same value. Your answer should be a *dataframe* with the location, state, and # 2012 binge drinking rate. Write this to a file called `min_binge.csv`.

# What is the *maximum* level of binge drinking in each state in 2012 for # both sexes (across the counties)? Your answer should contain roughly 50 values # (one for each state), unless there are two counties in a state with the # same value. Your answer should be a *dataframe* with the location, state, and # 2012 binge drinking rate. Write this to a file called `max_binge.csv`.

################################# Joining Data ################################# # You'll often have to join different datasets together in order to ask more # involved questions of your dataset. In order to join our datasets together, # you'll have to rename their columns to differentiate them.

# First, rename all prevalence columns in the any_drinking dataset to the # have prefix "any_" (i.e., `males_2002` should now be `any_males_2002`) # Hint: you can get (and set!) column names using the colnames function. # This may take multiple lines of code.

# Then, rename all prevalence columns in the binge_drinking dataset to the have # the prefix "binge_" (i.e., `males_2002` should now be `binge_males_2002`) # This may take multiple lines of code.

# Then, create a dataframe by joining together the both datasets. # Think carefully about the *type* of join you want to do, and what the # *identifying columns* are. You will use this (joined) data to answer the # questions below.

# Create a column `diff_2012` storing the difference between `any` and `binge` # drinking for both sexes in 2012

# Which location has the greatest *absolute* difference between `any` and # `binge` drinking? Your answer should be a one row data frame with the state, # location, and column of interest (diff_2012). # Write this dataframe to `biggest_abs_diff_2012.csv`.

# Which location has the smallest *absolute* difference between `any` and # `binge` drinking? Your answer should be a one row data frame with the state, # location, and column of interest (diff_2012). # Write this dataframe to `smallest_abs_diff_2012.csv`.

############## Write a function to ask your own question(s) #################### # Even in an entry level data analyst role, people are expected to come up with # their own questions of interest (not just answer the questions that other # people have). For this section, you should *write a function* that allows you # to ask the same question on different subsets of data. For example, you may # want to ask about the highest/lowest drinking level given a state or year. # The purpose of your function should be evident given the input parameters and # function name. After writing your function, *demonstrate* that the function # works by passing in different parameters to your function.

################################### Challenge ##################################

# Using your function from part 1 that wrote a .csv file given a state name, # write a separate file for each of the 51 states (including Washington D.C.) # The challenge is to do this in a *single line of (very concise) code*

# Write a function that allows you to pass in a *dataframe* (i.e., in the format # of binge_drinking or any_drinking) *year*, and *state* of interest. The # function should saves a .csv file with observations from that state's counties # (and the state itself). It should only write the columns `state`, `location`, # and data from the specified year. Before writing the .csv file, you should # *sort* the data.frame in descending order by the both_sexes drinking rate in # the specified year. The file name should have the format: # `DRINKING_STATE_YEAR.csv` (i.e. `any_Utah_2005.csv`). # To write this function, you will either have to use a combination of dplyr # and base R, or confront how dplyr uses *non-standard evaluation* # Hint: https://github.com/tidyverse/dplyr/blob/34423af89703b0772d59edcd0f3485295b629ab0/vignettes/nse.Rmd # Hint: https://www.r-bloggers.com/non-standard-evaluation-and-standard-evaluation-in-dplyr/

# Create the file `binge_Colorado_2007.csv` using your function.