readme.md

Cheese

The data this week comes from cheese.com, inspired by the examples in the {polite} package.

Cheese is nutritious food made mostly from the milk of cows but also other mammals, including sheep, goats, buffalo, reindeer, camels and yaks. Around 4000 years ago people have started to breed animals and process their milk. That's when the cheese was born.

Explore this site to find out about different kinds of cheese from all around the world.

248 cheeses have listed fat content. Is there a relationship between fat content and cheese type? What about texture, flavor, or aroma?

The Data

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

tuesdata <- tidytuesdayR::tt_load('2024-06-04')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 23)

cheeses <- tuesdata$cheeses

# Option 2: Read directly from GitHub

cheeses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-06-04/cheeses.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

cheeses.csv

variable	class	description
cheese	character	Name of the cheese.
url	character	Location of the cheese's description at cheese.com
milk	character	The type of milk used for the cheese, when known.
country	character	The country or countries of origin of the cheese.
region	character	The region in which the cheese is produced, either within the country of origin, or as a wider description of multiple countries.
family	character	The family to which the cheese belongs, if any.
type	character	The broad type or types to describe the cheese.
fat_content	character	The fat content of the cheese, as a percent or range of percents.
calcium_content	character	The calcium content of the cheese, when known. Values include units.
texture	character	The texture of the cheese.
rind	character	The type of rind used in producing the cheese.
color	character	The color of the cheese.
flavor	character	Characteristic(s) of the taste of the cheese.
aroma	character	Characteristic(s) of the smell of the cheese.
vegetarian	logical	Whether cheese.com considers the cheese to be vegetarian.
vegan	logical	Whether cheese.com considers the cheese to be vegan.
synonyms	character	Alternative names of the cheese.
alt_spellings	character	Alternative spellings of the name of the cheese (likely overlaps with synonyms).
producers	character	Known producers of the cheese.

Cleaning Script

library(tidyverse)
library(here)
library(fs)
library(rvest)
library(polite)
library(glue)

working_dir <- here::here("data", "2024", "2024-06-04")
session <- polite::bow("https://www.cheese.com/")

# Get the full list of cheeses. ------------------------------------------------
cheeses <- purrr::map(
  letters,
  \(letter) {
    this_path <- glue::glue("alphabetical/{letter}")
    session <- polite::nod(session, this_path)
    pages <- polite::scrape(session, query = list(per_page = 100)) |>
      rvest::html_elements(".page-link") |>
      rvest::html_text2() |>
      readr::parse_integer()
    purrr::map(
      pages,
      \(page) {
        cheeses <- polite::scrape(session, query = list(per_page = 100, page = page)) |>
          rvest::html_elements(".cheese-item h3 a")
        tibble::tibble(
          cheese = rvest::html_text2(cheeses),
          url = rvest::html_attr(cheeses, "href") |>
            rvest::url_absolute(base = session$url)
        )
      }
    ) |>
      purrr::list_rbind()
  }
) |>
  purrr::list_rbind()

# Functions for two types of cleaning. -----------------------------------------
fetch_summary_item <- function(summary_block, css) {
  summary_block |> 
    rvest::html_element(css) |> 
    rvest::html_text2()
}

fetch_summary_items <- function(summary_block, css) {
  summary_block |> 
    rvest::html_elements(css) |> 
    rvest::html_text2() |> 
    stringr::str_flatten_comma(na.rm = TRUE)
}

# Fetch details page-by-page ---------------------------------------------------
# This took a very long time to process.
cheese_details <- purrr::map(
  cheeses$url,
  \(cheese_url) {
    session <- polite::nod(session, cheese_url)
    summary_block <- polite::scrape(session) |> 
      rvest::html_elements(".summary-points")
    tibble::tibble(
      url = cheese_url,
      milk = summary_block |> 
        fetch_summary_items(".summary_milk a"),
      country = summary_block |>
        fetch_summary_items(".summary_country a"),
      region = summary_block |> 
        fetch_summary_item(".summary_region"),
      family = summary_block |> 
        fetch_summary_item(".summary_family"),
      type = summary_block |> 
        fetch_summary_item(".summary_moisture_and_type"),
      fat_content = summary_block |> 
        fetch_summary_item(".summary_fat"),
      calcium_content = summary_block |> 
        fetch_summary_item(".summary_calcium"),
      texture = summary_block |> 
        fetch_summary_items(".summary_texture a"),
      rind = summary_block |> 
        fetch_summary_item(".summary_rind"),
      color = summary_block |> 
        fetch_summary_item(".summary_tint"),
      flavor = summary_block |> 
        fetch_summary_item(".summary_taste"),
      aroma = summary_block |> 
        fetch_summary_item(".summary_smell"),
      vegetarian = summary_block |> 
        fetch_summary_item(".summary_vegetarian"),
      vegan = summary_block |> 
        fetch_summary_item(".summary_vegan"),
      synonyms = summary_block |> 
        fetch_summary_item(".summary_synonym"),
      alt_spellings = summary_block |> 
        fetch_summary_item(".summary_alt_spelling"),
      producers = summary_block |> 
        fetch_summary_item(".summary_producer")
    )
  }
) |> 
  purrr::list_rbind()

cheese_details <- cheese_details |> 
  dplyr::mutate(
    dplyr::across(
      c(region:calcium_content, rind:producers),
      ~ stringr::str_remove(.x, "^[^:]+: ")
    ),
    dplyr::across(
      c(vegetarian, vegan),
      ~ dplyr::case_match(
        .x,
        "no" ~ FALSE,
        "yes" ~ TRUE,
        .default = NA
      )
    )
  )

cheeses <- cheeses |> 
  dplyr::left_join(cheese_details, by = dplyr::join_by(url))

# Save -------------------------------------------------------------------------
readr::write_csv(
  cheeses,
  fs::path(working_dir, "cheeses.csv")
)