Faster XML conversion to Data Frames

Problem Setup

I had noted in a previous post that I have been using the XML package in R to process an XML from an export of our database. I used xmlToDataFrame to change from an XML set to an R data.frame and I have found it to be remarkably slow. After some Googling, I found a link where the author states that xmlToDataFrame is a generic function and if you know the structure of the data, you can leverage that to speed up the function.

So, that's what I did for my data. I think this structure is applicable to similar data structures in XML, so I thought I'd share.

Data Structure

Let's look at the data structure. For my data, an example XML would be:

<?xml version="1.0" encoding="UTF-8"?>
<export date="13-Jan-2014 14:08 -0600" createdBy="John Muschelli" role="Data Manager">

    <field4 />
    <scale2 />
    <field4 />

which tells me a few things:

  1. I'm XML (first line). There are other pieces of information which can be extracted as tags, but we won't cover that here.
  2. This is part of a large field called export (a parent in XML talk I believe) (second line)
  3. Datasets are a child of export (it's nested in export). For example, we have dataset1 and dataset2 in this export.
  4. There are missing data points, either references by <tag></tag> or <tag />. Both are valid XML.
  5. Not all records of the datasets have all fields. The second record of dataset1 doesn't have fields field3 but has field4.

So I wrote this function to make my data.frames, which I found to be much faster for conversion for large datasets.

xmlToDF = function(doc, xpath, isXML = TRUE, usewhich = TRUE, verbose = TRUE) {

    if (!isXML) 
        doc = xmlParse(doc)
    #### get the records for that form
    nodeset <- getNodeSet(doc, xpath)

    ## get the field names
    var.names <- lapply(nodeset, names)

    ## get the total fields that are in any record
    fields = unique(unlist(var.names))

    ## extract the values from all fields
    dl = lapply(fields, function(x) {
        if (verbose) 
            print(paste0("  ", x))
        xpathSApply(proc, paste0(xpath, "/", x), xmlValue)

    ## make logical matrix whether each record had that field
    name.mat = t(sapply(var.names, function(x) fields %in% x))
    df = data.frame(matrix(NA, nrow = nrow(name.mat), ncol = ncol(name.mat)))
    names(df) = fields

    ## fill in that data.frame
    for (icol in 1:ncol(name.mat)) {
        rep.rows = name.mat[, icol]
        if (usewhich) 
            rep.rows = which(rep.rows)
        df[rep.rows, icol] = dl[[icol]]


Function Options

So how do I use this?:

  • You need the XML package.
  • doc is an parsed XML file. For example, run:
doc = xmlParse("xmlFile.xml")
  • xpath is an XPath expression extracting the dataset you want. For example if I wanted dataset1, I'd run:
doc = xmlParse("xmlFile.xml")
xmlToDF(doc, xpath = "/export/dataset1")
  • You can set isXML=FALSE and pass in a character string of the xml filename, which just parses it for you.
xmlToDF("xmlFile.xml", xpath = "/export/dataset1", isXML = FALSE)
  • usewhich just flags if you should use which for subsetting. It seems faster, and I'm trying to think of reasons logical subsetting would be faster. This doesn't change functionality really as long as which returns something of length > 1, which it should by construction, but maybe speed up the code for large datasets.
  • verbose – do you want things printed to screen?

Function Explanation

So what is this code doing?:

  1. Parses the document (if isXML = FALSE)
  2. Extracts the nodes that are for that specific dataset.
  3. Gets the variable names for each record (var.names)
  4. Takes the union of all those variable names (fields). This will be the variable names of the resultant dataset. If every record had all fields, then this would be redundant, but this is a safer way of getting the column/variable names.
  5. Extract all the values from each field for each record (dl, which is a list).
  6. For each record, a logical matrix is made to record if that record had that field represented in XML.
  7. A loop over each field then fills in the values to the data.frame.
  8. data.frame is returned.

Timing differences

Obviously, I wanted to use this because I think it'd be faster than xmlToDataFrame. First off, what was the size of the dataset I was converting?

# [1] 16824 161

So only 16824 rows and 161 columns. Let's see how long it took to convert using xmlToDataFrame:

    user   system  elapsed 
4194.900   93.590 4288.996 

Where each measurement is in seconds, so that's over 1 hour! I think this is pretty long, and don't know all the checks going on, so that may not be unreasonable to those who have used this package a lot. But I think that's unscalable for large datasets.

What about xmlToDF?

   user  system elapsed 
225.004   0.356 225.391 

which takes about 4 minutes. This is significantly faster, and makes it reasonable to parse the 150 or so datasets I have.


This function (xmlToDF) may be useful if you're converting XML to a data.frame with similar structure from XML. If you're data is different, then you may have to tweak it to make it fit your needs. I understand that the for loop is probably not the most efficient, but it was clearer in code to those I'm writing for (other collaborators) and the efficiency gains from using this function over the xmlToDataFrame were enough for our needs.

The code is hosted here. Also, you can use this function (and any updates that are made) by using the processVISION packagea:

install_github("processVISION", "muschellij2")

4 thoughts on “Faster XML conversion to Data Frames

  1. 2000




    Using your example I am able to create a dataframe like below by passing “\\item” as path element. It is correctly displaying all sale items
    pen 10
    pencil 20
    note 30
    book 40
    I wish to attach storeId which is a parent element to the \\Item to the same dataframe. Is it possible ?
    2000 pen 10
    2000 pencil 20
    2001 note 30
    2001 book 40

  2. I used the xmlToDF fiunction on my own xml file with similar structure, but I received a strange error that depends from the fact that only for 2 variables the number of TRUE in “rep.rows” are different from the number of rows in dl[[icol]], so that it is impossible to get “df[rep.rows, icol] = dl[[icol]]”

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