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. The objective of this work goes beyond comprehensively organizing materials, also covering the development of standardized analysis workflows, and providing a common reference for different communities. In this perspective, 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 estimation and prediction, while ensuring reproducibility of the analyses.