Visualizing Missing Data

November 17, 2012

(This article was first published on Blog - R, and kindly contributed to R-bloggers)

There are several graphics available for visualizing missing data including the VIM package. However, I wanted a plot specifically for looking at the nature of missingness across variables and a clustering variable of interest to support data preparation in multilevel propensity score models (see the multilevelPSA package). The following examples uses data from the Programme of International Student Assessment (PISA; see pisa package).

The required packages can be downloaded from github. Note that the pisa package is approximately 75mb.

> require(devtools)
> install_github('multilevelPSA', 'jbryer')
> install_github('pisa', 'jbryer')

The following will setup the data to be plotted. There is a pisa.setup.R script included in the multilevelPSA package that is included to assist with a demo there. Among many things, it creates a vector psa.cols that defines the variables of interest in performing a propensity score analysis. These are the variables where missingness needs to be addressed.

> require(multilevelPSA)
> require(pisa)
> data(pisa.student)
> pkgdir = system.file(package='multilevelPSA')
> source(paste(pkgdir, '/pisa/pisa.setup.R', sep=''))
> student = pisa.student[,psa.cols]
> student$CNT = as.character(student$CNT)

And finally, to create the graphic use the plot.missing command.

> plot.missing(student[,c(4:48)], student$CNT)

Missing Plot for 2009 PISA

To leave a comment for the author, please follow the link and comment on their blog: Blog - R. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)