readme.md

The Great American Coffee Taste Test

In October 2023, "world champion barista" James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Do you think participants in this survey are representative of Americans in general?

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-05-14')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 20)

coffee_survey <- tuesdata$coffee_survey


# Option 2: Read directly from GitHub

coffee_survey <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-05-14/coffee_survey.csv')

How to Participate

• Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
• Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
• Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

coffee_survey.csv

variable	class	description
submission_id	character	Submission ID
age	character	What is your age?
cups	character	How many cups of coffee do you typically drink per day?
where_drink	character	Where do you typically drink coffee?
brew	character	How do you brew coffee at home?
brew_other	character	How else do you brew coffee at home?
purchase	character	On the go, where do you typically purchase coffee?
purchase_other	character	Where else do you purchase coffee?
favorite	character	What is your favorite coffee drink?
favorite_specify	character	Please specify what your favorite coffee drink is
additions	character	Do you usually add anything to your coffee?
additions_other	character	What else do you add to your coffee?
dairy	character	What kind of dairy do you add?
sweetener	character	What kind of sugar or sweetener do you add?
style	character	Before today's tasting, which of the following best described what kind of coffee you like?
strength	character	How strong do you like your coffee?
roast_level	character	What roast level of coffee do you prefer?
caffeine	character	How much caffeine do you like in your coffee?
expertise	numeric	Lastly, how would you rate your own coffee expertise?
coffee_a_bitterness	numeric	Coffee A - Bitterness
coffee_a_acidity	numeric	Coffee A - Acidity
coffee_a_personal_preference	numeric	Coffee A - Personal Preference
coffee_a_notes	character	Coffee A - Notes
coffee_b_bitterness	numeric	Coffee B - Bitterness
coffee_b_acidity	numeric	Coffee B - Acidity
coffee_b_personal_preference	numeric	Coffee B - Personal Preference
coffee_b_notes	character	Coffee B - Notes
coffee_c_bitterness	numeric	Coffee C - Bitterness
coffee_c_acidity	numeric	Coffee C - Acidity
coffee_c_personal_preference	numeric	Coffee C - Personal Preference
coffee_c_notes	character	Coffee C - Notes
coffee_d_bitterness	numeric	Coffee D - Bitterness
coffee_d_acidity	numeric	Coffee D - Acidity
coffee_d_personal_preference	numeric	Coffee D - Personal Preference
coffee_d_notes	character	Coffee D - Notes
prefer_abc	character	Between Coffee A, Coffee B, and Coffee C which did you prefer?
prefer_ad	character	Between Coffee A and Coffee D, which did you prefer?
prefer_overall	character	Lastly, what was your favorite overall coffee?
wfh	character	Do you work from home or in person?
total_spend	character	In total, much money do you typically spend on coffee in a month?
why_drink	character	Why do you drink coffee?
why_drink_other	character	Other reason for drinking coffee
taste	character	Do you like the taste of coffee?
know_source	character	Do you know where your coffee comes from?
most_paid	character	What is the most you've ever paid for a cup of coffee?
most_willing	character	What is the most you'd ever be willing to pay for a cup of coffee?
value_cafe	character	Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
spent_equipment	character	Approximately how much have you spent on coffee equipment in the past 5 years?
value_equipment	character	Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
gender	character	Gender
gender_specify	character	Gender (please specify)
education_level	character	Education Level
ethnicity_race	character	Ethnicity/Race
ethnicity_race_specify	character	Ethnicity/Race (please specify)
employment_status	character	Employment Status
number_children	character	Number of Children
political_affiliation	character	Political Affiliation

Cleaning Script

library(tidyverse)
library(janitor)
library(here)
library(fs)

working_dir <- here::here("data", "2024", "2024-05-14")

url <- "https://bit.ly/gacttCSV"

coffee_survey_raw <- readr::read_csv(url)

# Grab the raw questions for the dictionary.
coffee_survey_raw |> 
  colnames() |> 
  cat(sep = "\n")

coffee_survey <- coffee_survey_raw |> 
  janitor::clean_names() |> 
  # Get rid of one-hot encoding; users can do that if they'd like. Also,
  # "flavorings" columns are empty.
  dplyr::select(
    submission_id,
    age = what_is_your_age,
    cups = how_many_cups_of_coffee_do_you_typically_drink_per_day,
    where_drink = where_do_you_typically_drink_coffee,
    brew = how_do_you_brew_coffee_at_home,
    brew_other = how_else_do_you_brew_coffee_at_home,
    purchase = on_the_go_where_do_you_typically_purchase_coffee,
    purchase_other = where_else_do_you_purchase_coffee,
    favorite = what_is_your_favorite_coffee_drink,
    favorite_specify = please_specify_what_your_favorite_coffee_drink_is,
    additions = do_you_usually_add_anything_to_your_coffee,
    additions_other = what_else_do_you_add_to_your_coffee,
    dairy = what_kind_of_dairy_do_you_add,
    sweetener = what_kind_of_sugar_or_sweetener_do_you_add,
    style = before_todays_tasting_which_of_the_following_best_described_what_kind_of_coffee_you_like,
    strength = how_strong_do_you_like_your_coffee,
    roast_level = what_roast_level_of_coffee_do_you_prefer,
    caffeine = how_much_caffeine_do_you_like_in_your_coffee,
    expertise = lastly_how_would_you_rate_your_own_coffee_expertise,
    starts_with("coffee"),
    prefer_abc = between_coffee_a_coffee_b_and_coffee_c_which_did_you_prefer,
    prefer_ad = between_coffee_a_and_coffee_d_which_did_you_prefer,
    prefer_overall = lastly_what_was_your_favorite_overall_coffee,
    wfh = do_you_work_from_home_or_in_person,
    total_spend = in_total_much_money_do_you_typically_spend_on_coffee_in_a_month,
    why_drink = why_do_you_drink_coffee,
    why_drink_other = other_reason_for_drinking_coffee,
    taste = do_you_like_the_taste_of_coffee,
    know_source = do_you_know_where_your_coffee_comes_from,
    most_paid = what_is_the_most_youve_ever_paid_for_a_cup_of_coffee,
    most_willing = what_is_the_most_youd_ever_be_willing_to_pay_for_a_cup_of_coffee,
    value_cafe = do_you_feel_like_you_re_getting_good_value_for_your_money_when_you_buy_coffee_at_a_cafe,
    spent_equipment = approximately_how_much_have_you_spent_on_coffee_equipment_in_the_past_5_years,
    value_equipment = do_you_feel_like_you_re_getting_good_value_for_your_money_with_regards_to_your_coffee_equipment,
    gender,
    gender_specify = gender_please_specify,
    education_level,
    ethnicity_race,
    ethnicity_race_specify = ethnicity_race_please_specify,
    employment_status,
    number_children = number_of_children,
    political_affiliation
  )

readr::write_csv(
  coffee_survey,
  fs::path(working_dir, "coffee_survey.csv")
)