Journal
JOURNAL OF STATISTICS AND DATA SCIENCE EDUCATION
Volume 30, Issue 3, Pages 289-303Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/26939169.2022.2094300
Keywords
Data cleaning; Data tidying; Initial data analysis; Longitudinal data; NLSY79; Reproducible workflow
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This article discusses the difficulties and challenges of refreshing textbook datasets and provides a reproducible workflow and code for others to follow. Refreshing datasets improves the timeliness and relevance of teaching materials.
Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online.
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