LexisNexisTools. My first `R` package

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My PhD supervisor once told me that everyone doing newspaper analysis starts by writing code to read in files from the ‘LexisNexis’ newspaper archive. However, while I do recommend this exercise, not everyone has the time.

These are the first words of the introduction to my first R package, LexisNexisTools. My PhD supervisor was also my supervisor for my master dissertation and he said these words before he gave me my very first book about R. After getting the initial bits of code from him, I worked with files from LexisNexis first for my dissertation, then in a project where we used LexisNexis data and since I’m also using newspaper data for my PhD project, I knew I would be using the code again.

At some point, I decided that I would spare others the hassle of figuring out all the quirks of files that come from LexisNexis. I made the decision to write an R package instead of just sharing the code because of two reasons: 1. I wanted that others who stumble across my work have a chance to report any issues and bugs they find. This was at first a scary thought: my work would be scrutinised by others on the internet! But after some deliberation I concluded that someone calling out my sloppy code is much better than if I find an error myself months down the line when it means that I have to re-do all the work to make it right. 2. I admire people who write good packages since they make my own life much easier. I wanted to give something back to the community.

One might think that the process of reading files into R which are in the txt format is not harder than using the command readLines(). Yet, while this gets you a nice character object, LexisNexis puts up to 500 articles into one file, together with the meta data of each article. To get this into a spreadsheet like format and to get all the data types correctly can be a great hassle and definitely caused me some headache. Especially since LexisNexis seems to have changed the file structure a couple of times over the years. They use different structures for different sources and on top of that are inconsistent with the order of meta data.

The task was thus to find the structure in the files. For those who haven’t seen a file from LexisNexis, here is a small example:


After the cover sheet, the first article starts with 1 of 87 DOCUMENTS. The 87 can of course change but the format is always the same, which means the beginning of an article is always (at least for the English version) \\d+ of \\d+ DOCUMENTS$. This information can be used to chop up the file into individual articles.

beginnings <- which(stringi::stri_detect_regex(articles.v, "\\d+ of \\d+ DOCUMENTS$"))
articles.l <- lapply(seq_along(beginnings), function(n) {
  if (n < length(beginnings)) {
    articles.v[beginnings[n]:(beginnings[n + 1] - 1)]
  } else {

articles.v here is simply the the read-in file as a character vector. The lapply() loop goes through the instances in beginnings and constructs a list where each entry starts at the line with \\d+ of \\d+ DOCUMENTS$ and ends one line before the next article begins. At the last iteration of the loop, where n is equal to length(beginnings), the list entry will contain the text from the last \\d+ of \\d+ DOCUMENTS$ until the end of the file.

Each entry is then further divided at a second crucial keyword: LENGTH. This marks the end of the metadata and hence can be use to separate article text from meta information provided by LexisNexis.

Then a number of commands perform some Regex magic or use experience about the position of certain information to put the pieces in columns.

But users don’t have to worry about any of this any more. They can simply use:


You can check out a quick walk-through at github.com/JBGruber/LexisNexisTools.

I’m really proud of my first package. Much of the code already existed before I first posted it online but seeing and hearing that others find it useful gave me a major boost of confidence. So far nobody has complained about the code base and I’m happy all the work I put into it does not just sit on my own computer but others can profit.

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