\HeaderA{ggmissing}{Missing values plot}{ggmissing}
\keyword{hplot}{ggmissing}
\begin{Description}\relax
Create a plot to illustrate patterns of missing values
\end{Description}
\begin{Usage}
\begin{verbatim}ggmissing(data, avoid="stack", order=TRUE, missing.only = TRUE)\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{data}] data.frame
\item[\code{avoid}] whether missings should be stacked or dodged, see \code{\LinkA{geom\_bar}{geom.Rul.bar}} for more details
\item[\code{order}] whether variable should be ordered by number of missings
\item[\code{missing.only}] whether only variables containing some missing values should be shown
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The missing values plot is a useful tool to get a rapid
overview of the number of missings in a dataset.  It's strength
is much more apparent when used with interactive graphics, as you can
see in Mondrian (\url{http://rosuda.org/mondrian}) where this plot was
copied from.
\end{Details}
\begin{Author}\relax
Hadley Wickham <h.wickham@gmail.com>
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{ggstructure}{ggstructure}}, \code{\LinkA{ggorder}{ggorder}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}mmissing <- movies
mmissing[sample(nrow(movies), 1000), sample(ncol(movies), 5)] <- NA
ggmissing(mmissing)
ggmissing(mmissing, order=FALSE, missing.only = FALSE)
ggmissing(mmissing, avoid="dodge") + scale_y_sqrt()
ggmissing(mmissing) + scale_y_log10(limits=c(1, NA))\end{ExampleCode}
\end{Examples}

