Lähteenä taulukot 1. ja 2. Botelho J, Machado V, Leira Y, Proença L, Chambrone L, Mendes JJ. Economic burden of periodontitis in the United States and Europe: An update estimation. Journal of Periodontology. 2022;93(3):373–9.

Parodontiitin kustannukset\n (BKT %)

Parodontiitin kustannukset\n (% kaikista terveydenhuollon kuluista)

Spotify’s vast streaming catalog offers an ideal sandbox for probing how musical style interacts with commercial success. This technical report chronicles the assembly of an analysis-ready dataset that merges two complementary slices—“High Popularity” and “Low Popularity”—from the Kaggle Spotify Music Data repository. After appending a categorical popularity label, we condense over thirty playlist-level genre tags into six headline genres to streamline visualisation and modelling. Duplicate records are systematically flagged at the track-artist-playlist axis using flexible separator rules that detect comma, ampersand, and semicolon delimiters within multi-artist fields. Because Spotify sometimes assigns the borderline popularity score of sixty-eight to both classes, ambiguous low-popularity rows are excluded to guarantee mutually exclusive groups. The resulting table contains 13,342 unique tracks spanning electronic, pop, Latin, hip-hop, ambient, rock, and an aggregated “others” bucket. Every transformation is fully scripted in R and encapsulated within a Quarto document, enabling frictionless reruns on fresh dumps or forked datasets. The cleaned corpus supports subsequent inquiries into genre-specific audience reach, collaboration networks, and the quantitative drivers of streaming attention. Practitioners can reuse the workflow as a drop-in template for playlist analytics, while researchers gain a transparent foundation for reproducible music-industry studies. The code is concise, documented, and easily shareable.

This notebook enriches a cleaned Spotify track list with artist-country metadata to support geographic analyses of streaming popularity. Starting from spotify_data_rmdup.csv—a 13 k-row table of unique tracks and curated audio-features—we query the public MusicBrainz API in 1 000-row batches, assigning one or more ISO-3166 country codes to every artist credited on each track. A lightweight in-memory cache plus an on-disk RDS file prevent redundant requests, while an adjustable one-second delay respects MusicBrainz rate limits. All steps—chunk creation, safe API calls, duplicate handling, progress reporting, and final export—are fully scripted in R and wrapped in a Quarto document for reproducibility. The resulting file, dat_with_country_all.rds, adds a pipe-delimited country field that can be merged back into genre or popularity studies, enabling questions such as “Which countries dominate high-popularity electronic playlists?” or “How do cross-border collaborations affect reach?” Researchers can reuse or extend the workflow with minimal edits.

Spotify’s vast streaming catalog offers an ideal sandbox for probing how musical style interacts with commercial success. This technical report chronicles the assembly of an analysis-ready dataset that merges two complementary slices—“High Popularity” and “Low Popularity”—from the Kaggle Spotify Music Data repository. After appending a categorical popularity label, we condense over thirty playlist-level genre tags into six headline genres to streamline visualisation and modelling. Duplicate records are systematically flagged at the track-artist-playlist axis using flexible separator rules that detect comma, ampersand, and semicolon delimiters within multi-artist fields. Because Spotify sometimes assigns the borderline popularity score of sixty-eight to both classes, ambiguous low-popularity rows are excluded to guarantee mutually exclusive groups. The resulting table contains 13,342 unique tracks spanning electronic, pop, Latin, hip-hop, ambient, rock, and an aggregated “others” bucket. Every transformation is fully scripted in R and encapsulated within a Quarto document, enabling frictionless reruns on fresh dumps or forked datasets. The cleaned corpus supports subsequent inquiries into genre-specific audience reach, collaboration networks, and the quantitative drivers of streaming attention. Practitioners can reuse the workflow as a drop-in template for playlist analytics, while researchers gain a transparent foundation for reproducible music-industry studies. The code is concise, documented, and easily shareable.

Analysis of severe weather event impacts on health and economy in the US using NOAA data.