  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), a burgeoning literature on
missing values has arisen, with heterogeneous aims and motivations. This led to
the development of various methods, formalizations, and tools. For
practitioners, it remains nevertheless challenging to decide which method is
most suited for their problem, partially due to a lack of systematic covering
of 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 values
problems, methods, and relevant implementations of methodologies. Beyond
gathering and organizing a large majority of the material on missing data
(bibliography, courses, tutorials, implementations), "R-miss-tastic" covers the
development of standardized analysis workflows. Indeed, we have developed
several pipelines in R and Python to allow for hands-on illustration of and
recommendations on missing values handling in various statistical tasks such as
matrix completion, estimation and prediction, while ensuring reproducibility of
the analyses. Finally, the platform is dedicated to users who analyze
incomplete data, researchers who want to compare their methods and search for
an up-to-date bibliography, and also teachers who are looking for didactic
materials (notebooks, video, slides).
