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Missing values are unavoidable when working with data. Their occurrence isexacerbated as more data from different sources become available. However, moststatistical models and visualization methods require complete data, andimproper handling of missing data results in information loss or biasedanalyses. Since the seminal work of Rubin (1976), a burgeoning literature onmissing values has arisen, with heterogeneous aims and motivations. This led tothe development of various methods, formalizations, and tools. Forpractitioners, it remains nevertheless challenging to decide which method ismost suited for their problem, partially due to a lack of systematic coveringof this topic in statistics or data science curricula.  To help address this challenge, we have launched the "R-miss-tastic"platform, which aims to provide an overview of standard missing valuesproblems, methods, and relevant implementations of methodologies. Beyondgathering and organizing a large majority of the material on missing data(bibliography, courses, tutorials, implementations), "R-miss-tastic" covers thedevelopment of standardized analysis workflows. Indeed, we have developedseveral pipelines in R and Python to allow for hands-on illustration of andrecommendations on missing values handling in various statistical tasks such asmatrix completion, estimation and prediction, while ensuring reproducibility ofthe analyses. Finally, the platform is dedicated to users who analyzeincomplete data, researchers who want to compare their methods and search foran up-to-date bibliography, and also teachers who are looking for didacticmaterials (notebooks, video, slides).

R-miss-tastic: a unified platform for missing values methods and workflows