The notable exception is Stef van Buuren’s authoritative R-based textbook, Flexible Imputation of Missing Data which he has graciously made available online. It is also the case that it is not easy to find good introductory material on the subject. So, unless you are a na.omit() kind of guy/gal (a data scientist?) coming to grips with NAs may involve a subtle inferential task embedded in the usual data wrangling effort. Missing data problems are among the most vexing in statistics, and the newer techniques for tackling these problems are relatively sophisticated. The downside that may curb one’s enthusiasm, is that mastering missing data techniques requires some serious study. The Exploration of missing data section contains a number of tools that should be useful in any data wrangling effort like this plot diagnostic plot from the dlookr package. But, the task view is not just devoted to esoterica. This single page not only describes what R has to offer with respect to coping with missing data, it is probably the world’s most complete index of statistical knowledge on the subject.
Even though I did some research on R packages for a post on missing values a couple of years ago, I was dumbfounded by the number of packages included in the new Task View. This week the r-miss-tastic team: Julie Josse, Nicholas Tierney and Nathalie Vialaneix launched the Missing Data Task View. It is a relatively rare event, and cause for celebration, when CRAN gets a new Task View.