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

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss, or biased analyses. Since the seminal work of Rubin (1976), there has been a burgeoning literature on missing values with heterogeneous aims and motivations. This has resulted in the development of various methods, formalizations, and tools (including a large number of R packages and Python modules). However, for practitioners, it remains challenging to decide which method is most suited for their problem, partially because handling missing data is still not a topic systematically covered in statistics or data science curricula. To help address this challenge, we have launched a unified platform: "R-miss-tastic", which aims to provide an overview of standard missing values problems, methods, how to handle them in analyses, and relevant implementations of methodologies. In the same perspective, we have also developed several pipelines in R and Python to allow for a hands-on illustration of how to handle missing values in various statistical tasks such as estimation and prediction, while ensuring reproducibility of the analyses. This will hopefully also provide some guidance on deciding which method to choose for a specific problem and data. The objective of this work is not only to comprehensively organize materials, but also to create standardized analysis workflows, and to provide a common ground for discussions among the community. This platform is thus suited for beginners, students, more advanced analysts and researchers.