A common task for programmers these days is writing code to analyze data from various sources and output information for use by non-coders or business executives. Although you can use any language for this type of analysis, I’ve found that R simplifies working with almost any modern data type, including XML, a popular choice for storing large amounts of complex data. In this article, I’ll step through the process of examining XML data in an R program, so you can see how easy R makes working with XML files.<\/p>\n
I first began analyzing XML data while I was working within the public health department at Cornell’s Medical School. We’ll use the same data source, PubMed, for this article. For this example, I pulled data about papers authored by Dr. Madhu Mazumdar in 2012 from PubMed’s API, which is called Entrez<\/a>.<\/p>\n NOTE<\/strong><\/p>\n To view or use the code examples in this article, download the code<\/a> file. The full code referenced in this article is included for the purposes of helping readers follow along in R. In the pubmed_sample.xml<\/tt> file, the data entries begin with the <PubmedArticle><\/tt> tag and end with <\/PubmedArticle><\/tt>.<\/p>\n<\/div>\n When dealing with XML data, the main package we’ll rely on is XML. However, for our analysis in this example, we’ll also need the plyr<\/tt>, ggplot2<\/tt>, and gridExtra<\/tt> packages:<\/p>\n