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Published in:
2019 | OriginalPaper | Chapter
9. Missing Data
Authors : Matt Wiley, Joshua F. Wiley
Published in: Advanced R Statistical Programming and Data Models
Publisher: Apress
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Abstract
Missing data is common in nearly all real-world analysis. This chapter introduces the concept of missing data formally including common ways of describing missingness. Then we discuss some of the potential ways missing data can be addressed in analysis. The main package we will use in this chapter is the
mice package, one package that offers robust features for handling missing data and minimizing the impact of missing data on analysis results [95].
library(checkpoint)
checkpoint("2018-09-28", R.version = "3.5.1",
project = book_directory,
checkpointLocation = checkpoint_directory,
scanForPackages = FALSE,
scan.rnw.with.knitr = TRUE, use.knitr = TRUE)
library(knitr)
library(ggplot2)
library(cowplot)
library(lattice)
library(viridis)
library(VIM)
library(mice)
library(micemd)
library(parallel)
library(data.table)
library(xtable)
library(JWileymisc) # has data
options(width = 70, digits = 2)