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	<title>Curving Normality &#187; GSS</title>
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	<description>&#34;The extra-ordinary lies within the curve of normality&#34;</description>
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		<title>R-Sessions 30: Visualizing missing values</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-30-visualizing-missing-values/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-30-visualizing-missing-values/#comments</comments>
		<pubDate>Thu, 08 Jan 2009 10:00:39 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[GSS]]></category>
		<category><![CDATA[GSS cumulative file]]></category>
		<category><![CDATA[Missing values]]></category>
		<category><![CDATA[R-Project]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=872</guid>
		<description><![CDATA[<a href="http://www.rensenieuwenhuis.nl/archive/category/r-project/r-sessions/"><img title="R-Sessions" src="http://www.rensenieuwenhuis.nl/wp-content/uploads/2008/07/r-sessions.jpg" alt="" width="470" /></a> 

It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find.

I found a nice way to plot missing values, using R. Basically, I thought it would be nice to calculate the percentage of missings on each variable, and do so for each year represented in the data. These numbers could be visualized using a levelplot(), which resulted in the graph below.]]></description>
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