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Journal Articles The R Journal Year : 2022

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

Abstract

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).
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HowToImpute_R_new.pdf (5.93 Ko) Télécharger le fichier
HowToImpute_table.pdf (27.18 Ko) Télécharger le fichier
MAR_decathlon.pdf (5.62 Ko) Télécharger le fichier
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howtoimpute_res.pdf (22.58 Ko) Télécharger le fichier
local-bibliography-1.pdf (877.27 Ko) Télécharger le fichier
local-bibliography.pdf (56.17 Ko) Télécharger le fichier
local-data.pdf (111.16 Ko) Télécharger le fichier
local-lectures-1.pdf (114.7 Ko) Télécharger le fichier
local-lectures.pdf (50.07 Ko) Télécharger le fichier
local-packages-1.pdf (86.73 Ko) Télécharger le fichier
local-packages.pdf (129.87 Ko) Télécharger le fichier
predict3.pdf (16.82 Ko) Télécharger le fichier
predict_appendix_HGBoost.pdf (12.19 Ko) Télécharger le fichier
predict_appendix_LinReg.pdf (11.9 Ko) Télécharger le fichier
predict_appendix_RandomForest.pdf (11.47 Ko) Télécharger le fichier
table_rmiss.pdf (229.1 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)

Dates and versions

hal-02879337 , version 1 (30-07-2020)
hal-02879337 , version 2 (07-01-2022)
hal-02879337 , version 3 (21-07-2022)

Identifiers

Cite

Imke Mayer, Aude Sportisse, Nicholas Tierney, Nathalie Vialaneix, Julie Josse. R-miss-tastic: a unified platform for missing values methods and workflows. The R Journal, 2022, 14 (2), pp.244-266. ⟨10.32614/RJ-2022-040⟩. ⟨hal-02879337v3⟩
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