created content for pull request for TT - Fragrance Dataset

README.md

@@ -81,6 +81,7 @@ If you are using TidyTuesday to teach data-related skills, [please let us know](
 | 47 | `2024-11-19` | [Bob's Burgers Episodes](data/2024/2024-11-19/readme.md) | [bobsburgersR R Package](https://github.com/poncest/bobsburgersR) | [Bob’s Burgers Episode Fingerprints by Season](https://stevenponce.netlify.app/projects/standalone_visualizations/sa_2024-11-11.html) | 
 | 48 | `2024-11-26` | [U.S. Customs and Border Protection (CBP) Encounter Data](data/2024/2024-11-26/readme.md) | [U.S. Customs and Border Protection](https://www.cbp.gov/) | [U.S. Border Patrol Encounters](https://gdatascience.github.io/us_border_patrol_encounters/us_border_patrol_encounters.html) | 
 | 49 | `2024-12-03` | [National Highways Traffic Flow](data/2024/2024-12-03/readme.md) | [WebTRIS Traffic Flow API](https://webtris.nationalhighways.co.uk/api/swagger/ui/index) | [National Highways](https://webtris.nationalhighways.co.uk/) | 
+| 50 | `2024-12-10` | [The Scent of Data - Exploring the Parfumo Fragrance Dataset](data/2024/2024-12-10/readme.md) | [Parfumo Fragrance Dataset](https://www.kaggle.com/datasets/olgagmiufana1/parfumo-fragrance-dataset) | [Perfumes at parfumo](https://www.parfumo.com/Perfumes) | 
 
 ***  
 

data/2024/2024-12-10/meta.yaml

@@ -0,0 +1,18 @@
+title: "The Scent of Data - Exploring the Parfumo Fragrance Dataset"
+article:
+  title: "Perfumes at parfumo"
+  url: "https://www.parfumo.com/Perfumes"
+data_source:
+  title: "Parfumo Fragrance Dataset"
+  url: "https://www.kaggle.com/datasets/olgagmiufana1/parfumo-fragrance-dataset"
+images:
+- file: "parfumo_data_scatter.png"
+  alt: >
+    A scatter plot showing the relationship between the number of ratings (x-axis) and the average perfume rating (y-axis) from the Parfumo Fragrance Dataset. The color gradient indicates the rating, ranging from blue (low ratings) to orange (high ratings). Most data points cluster near the top-left, suggesting higher ratings with fewer reviews, while ratings spread out with increasing review counts. The title reads "Relationship Between Rating Count and Actual Rating," and a legend is provided to represent the color-coded ratings. The footer includes links to the creator's LinkedIn and GitHub profiles.
+- file: "parfumo_data_bar.png"
+  alt: > 
+    A bar plot illustrating the evolution of fragrance ratings from 1900 to the present, based on the Parfumo Fragrance Dataset. The x-axis represents the year of release, while the y-axis shows the number of releases (Release Count). Bars are color-coded by ratings count, with a gradient ranging from blue (lower ratings count) to orange (higher ratings count). The plot highlights a significant increase in releases starting around the mid-20th century, peaking in the early 2000s. The title reads "Evolution of Fragrance Ratings Years 1900 - Present," with a legend representing the ratings count gradient. The footer includes LinkedIn and GitHub links to the creator's profiles.
+credit:
+  post: "Nicolas Foss, Ed.D., MS | Bureau of Emergency Medical and Trauma Services > Iowa HHS"
+  linkedin: "https://www.linkedin.com/in/nicolas-foss"
+  github: "https://github.com/nicolasfoss"

data/2024/2024-12-10/readme.md

@@ -0,0 +1,230 @@
+# The Scent of Data - Exploring the Parfumo Fragrance Dataset
+
+This week we're diving into the fascinating world of fragrances with a dataset sourced from [Parfumo](https://www.parfumo.com/), a vibrant community of perfume enthusiasts.  [Olga G.](https://www.kaggle.com/olgagmiufana1) webscraped these data from the various fragrance sections on the Parfumo website. Here is a description from the author:
+
+> This dataset contains detailed information about perfumes sourced from Parfumo, obtained through web scraping. It includes data on perfume ratings, olfactory notes (top, middle, and base notes), perfumers, year of release and other relevant characteristics of the perfumes listed on the Parfumo website.
+
+> The data provides a comprehensive look at how various perfumes are rated, which families of scents they belong to, and detailed breakdowns of the key olfactory components that define their overall profile
+
+We’ll explore how perfumes are rated, uncover the scent families they belong to, and delve into the minds of the perfumers behind them. From the year of release to the delicate composition of each scent, this dataset offers a rich olfactory experience for anyone curious about the magic behind their favorite perfumes.
+
+Join us as we decode the stories within these perfumes, from the top notes that hit you first to the lasting base notes that linger in the air. Whether you're a fragrance aficionado or just curious about the data behind the scents, this exploration will open your eyes (and nose) to the artistry of perfume crafting. Ready to sniff out some data?
+
+* What factors most influence the rating of a perfume?
+* Are there distinct scent families that dominate the market, and how are they perceived by users?
+* Has the popularity of certain fragrance notes evolved over time?
+
+Thank you to [Nicolas Foss, Ed.D., MS | Bureau of Emergency Medical and Trauma Services > Iowa HHS](https://github.com/nicolasfoss) for curating this week's dataset.
+
+## The Data
+
+```r
+# Option 1: tidytuesdayR package 
+## install.packages("tidytuesdayR")
+
+tuesdata <- tidytuesdayR::tt_load('2024-12-10')
+## OR
+tuesdata <- tidytuesdayR::tt_load(2024, week = 50)
+
+parfumo_data_clean <- tuesdata$parfumo_data_clean
+
+# Option 2: Read directly from GitHub
+
+parfumo_data_clean <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-12-10/parfumo_data_clean.csv')
+```
+
+## How to Participate
+
+- [Explore the data](https://r4ds.hadley.nz/), 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](https://shiny.posit.co/), 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](../../../sharing.md) on social media with the #TidyTuesday hashtag.
+- [Submit your own dataset!](../../../.github/pr_instructions.md)
+
+### Data Dictionary
+
+# `parfumo_data_clean.csv`
+
+|variable      |class       |description                                                                      |
+|:-------------|:-----------|:--------------------------------------------------------------------------------|
+|Number        |character   |A unique identifier or number assigned to each perfume.                          |
+|Name          |character   |The name of the perfume or fragrance.                                            |
+|Brand         |character   |The brand or manufacturer of the fragrance.                                      |
+|Release_Year  |double      |The year the fragrance was released.                                             |
+|Concentration |character   |The concentration of the fragrance (e.g., Eau de Parfum, Eau de Toilette).       |
+|Rating_Value  |double      |The overall rating score given by users.                                         |
+|Rating_Count  |double      |The number of user ratings for the fragrance.                                    |
+|Main_Accords  |character   |The primary scent characteristics or accords of the fragrance.                   |
+|Top_Notes     |character   |The initial scent notes perceived after application.                             |
+|Middle_Notes  |character   |The heart or middle notes of the fragrance that emerge after the top notes fade. |
+|Base_Notes    |character   |The lasting, final scent notes that linger after the fragrance has dried down.   |
+|Perfumers     |character   |The creators or perfumers responsible for the fragrance composition.             |
+|URL           |character   |The link to the product page on Parfumo.com.                                     |
+
+### Cleaning Script
+
+```r
+###_____________________________________________________________________________
+### Parfumo Fragrance
+### Script to clean the data sourced from Kaggle
+### Thanks to Olga G. Miufana!
+###_____________________________________________________________________________
+
+# packages
+library(httr)
+library(tidyverse)
+library(jsonlite)
+library(glue)
+library(janitor)
+library(naniar)
+
+# Define the metadata URL and fetch it
+metadata_url <- "https://www.kaggle.com/datasets/olgagmiufana1/parfumo-fragrance-dataset/croissant/download"
+response <- httr::GET(metadata_url)
+
+# Ensure the request succeeded
+if (httr::http_status(response)$category != "Success") {
+  stop("Failed to fetch metadata.")
+}
+
+# Parse the metadata
+metadata <- httr::content(response, as = "parsed", type = "application/json")
+
+# Locate the ZIP file URL
+distribution <- metadata$distribution
+zip_url <- NULL
+
+for (file in distribution) {
+  if (file$encodingFormat == "application/zip") {
+    zip_url <- file$contentUrl
+    break
+  }
+}
+
+if (is.null(zip_url)) {
+  stop("No ZIP file URL found in the metadata.")
+}
+
+# Download the ZIP file
+temp_file <- base::tempfile(fileext = ".zip")
+utils::download.file(zip_url, temp_file, mode = "wb")
+
+# Unzip and read the CSV
+unzip_dir <- base::tempdir()
+utils::unzip(temp_file, exdir = unzip_dir)
+
+# Locate the CSV file within the extracted contents
+csv_file <- list.files(unzip_dir, pattern = "\\.csv$", full.names = TRUE)
+
+if (length(csv_file) == 0) {
+  stop("No CSV file found in the unzipped contents.")
+}
+
+# Read the CSV into a dataframe
+parfumo_data <- read_csv(csv_file)
+
+# Step 5: Explore the data
+glimpse(parfumo_data)
+
+###_____________________________________________________________________________
+# Problems with the Data!
+
+# Some of the columns utilize "N/A" instead of a missing value
+# deal with these values efficiently using {tidyselect} and {dplyr}
+# additionally, the perfume names include the brand and sometimes the year
+# along with a perfume number
+# clean the perfume names so we have only the names as there are separate
+# variables for the brand and release year
+# use the perfume number as an additional column
+# there are some numeric columns that are encoded as character due to the "N/A"
+# values, we will deal with those
+# variables that could be numeric are encoded as strings due to the use of "N/A"
+# versus a true missing
+# fix!
+
+###_____________________________________________________________________________
+
+# we will create a few different objects while we clean 
+# to inspect and avoid errors
+
+parfumo_data_prep <- parfumo_data |> 
+  dplyr::mutate(
+
+    # replace the "N/A" values with true missings
+    dplyr::across(
+
+      tidyselect::everything(), ~ dplyr::if_else(
+
+        grepl(pattern = "\\bN/A\\b", x = .), NA_character_, .
+
+        )
+
+      )) |> 
+  dplyr::mutate(
+
+    # new perfume # variable
+    Number = stringr::str_extract(Name, pattern = "^#?\\s?\\d*\\.?\\d*?"),
+
+    # remove the preceding optional pound sign and space
+    Number = stringr::str_remove(Number, pattern = "^#?\\s?"),
+    .before = Name
+
+    ) |> 
+
+  # replace Number blanks with NA, as some have no # value
+  naniar::replace_with_na(replace = list(Number = "")) |> 
+  dplyr::mutate(
+
+    # create a temporary pattern variable to pick up the year
+    # and brand names that appear in the Name string to build
+    # dynamic regex for each row
+    # leverage the {glue} package, here
+
+    dynamic_pattern = glue::glue("(?:\\s{Brand})*(?:\\s{`Release Year`})?$"),
+
+    # clean the perfume name
+    Clean_Name = stringr::str_remove_all(Name, pattern = "^#?\\s?\\d*\\.?\\d*\\s?-?\\s?"),
+
+    # again for the Brand and Release Year removal
+
+    Clean_Name = stringr::str_remove_all(Clean_Name, pattern = dynamic_pattern),
+    Clean_Name = str_squish(Clean_Name),
+    .before = Name
+
+  ) |> 
+
+  dplyr::select(-dynamic_pattern, -Name)
+
+# inspect the object after initial cleaning
+
+glimpse(parfumo_data_prep)
+
+# a new dplyr chain to continue cleaning,
+# break it up to mutate and inspect to avoid errors
+
+parfumo_data_clean <- parfumo_data_prep |> 
+
+  dplyr::rename(Name = Clean_Name) |> 
+
+  # the rating value column as the word "Ratings" in it, preventing the column from being
+  # numeric, fix
+  # release year, rating value, and rating count are still character strings instead of numeric values
+  # fix!
+
+  dplyr::mutate(
+    `Rating Count` = stringr::str_remove_all(`Rating Count`, pattern = "\\sRatings$"),
+    across(c(`Release Year`, `Rating Value`, `Rating Count`), ~ as.numeric(.))
+  ) |> 
+
+  # clean the names so that they are easier to use in the future
+
+  clean_names(case = "title", sep_out = "_") |> 
+  rename(URL = Url)
+
+# inspect the final object
+
+glimpse(parfumo_data_clean)
+
+###_____________________________________________________________________________
+### End
+###_____________________________________________________________________________
+```

data/2024/readme.md

@@ -53,3 +53,4 @@ Archive of datasets and articles from the 2024 series of `#TidyTuesday` events.
 | 47 | `2024-11-19` | [Bob's Burgers Episodes](2024-11-19/readme.md) | [bobsburgersR R Package](https://github.com/poncest/bobsburgersR) | [Bob’s Burgers Episode Fingerprints by Season](https://stevenponce.netlify.app/projects/standalone_visualizations/sa_2024-11-11.html) |  
 | 48 | `2024-11-26` | [U.S. Customs and Border Protection (CBP) Encounter Data](2024-11-26/readme.md) | [U.S. Customs and Border Protection](https://www.cbp.gov/) | [U.S. Border Patrol Encounters](https://gdatascience.github.io/us_border_patrol_encounters/us_border_patrol_encounters.html) |  
 | 49 | `2024-12-03` | [National Highways Traffic Flow](2024-12-03/readme.md) | [WebTRIS Traffic Flow API](https://webtris.nationalhighways.co.uk/api/swagger/ui/index) | [National Highways](https://webtris.nationalhighways.co.uk/) |  
+| 50 | `2024-12-10` | [The Scent of Data - Exploring the Parfumo Fragrance Dataset](2024-12-10/readme.md) | [Parfumo Fragrance Dataset](https://www.kaggle.com/datasets/olgagmiufana1/parfumo-fragrance-dataset) | [Perfumes at parfumo](https://www.parfumo.com/Perfumes) |  

static/tt_data_type.csv

@@ -1,4 +1,5 @@
 Week,Date,year,data_files,data_type,delim
+50,2024-12-10,2024,parfumo_data_clean.csv,csv,","
 49,2024-12-03,2024,A64_traffic.csv,csv,","
 48,2024-11-26,2024,cbp_resp.csv,csv,","
 48,2024-11-26,2024,cbp_state.csv,csv,","