### Overview

The tei2r package is designed for humanities scholars and students interested in using R for text analysis. Its creation was prompted by the recent public release of thousands of documents by the Text Creation Partnership, including Early English Books Online, Evans, and Eighteenth-Century Collections Online. tei2r has built-in functions that allow users easily to access and download TCP files from Github, https://github.com/textcreationpartnership, where they are freely available.

You don’t need to be an experienced R programmer to use tei2r. Its most important functions require learning only a small handful of commands, which are explained below. tei2r makes it easy to measure word frequencies, build concordances, and train topic models. Each of these operations is explained in this introduction (which can be followed step-by-step in less than 20 minutes). For this reason, tei2r is ideal for DH-curious scholars with a strong interest in early modern literature and history.

Scholars already comfortable doing text analysis with R or Python may still find tei2r useful, because its method for organizing objects in the R environment makes it easy to work with small- to medium-sized collections. Forthcoming complementary packages – htn for historical text network analysis and empson for vector-space modeling – build on and extend the core tei2r functionalities with more complex analytical techniques.

Anyone using tei2r should feel free to email Michael Gavin at mgavin@mailbox.sc.edu with questions, complaints, critiques, requests, bug reports, suggestions, or offers to collaborate.

tei2r was written and developed by Michael Gavin and Travis Mullen (who was intimately involved in all aspects of the design), and was supported by an ASPIRE I Research Grant from the University of South Carolina.

## Installation

The initial development version of tei2r is released on Github. If you’re using RStudio, you’ll already have the devtools package installed and ready-to-use. (If you’re using R by itself, you may need to install devtools separately by running the command, install.packages("devtools"), followed by library(devtools).)

To install tei2r and activate its library:

devtools::install_github("michaelgavin/tei2r", build_vignettes = T)
library(tei2r)

To browse the help files, look over the main page:

?tei2r

## Getting started with the EEBO-TCP

(Readers most interested in learning about tei2r’s data structure may want to skip to the next section.)

tei2r has two functions that are designed solely for working with the EEBO-TCP corpus. They are called tcpSearch and tcpDownload, and, as their names imply, they allow you to search the TCP archive and download files from the TCP.

If you’re just exploring and browsing EEBO, the already existing web interfaces will work better than R Studio, but if you have a good sense of the files you want to grab, tcpSearch will get you started. If you want all the books published in a given year, or everything attributed to an individual author, or everything with a certain term in the title or subject headings, run this command:

tcpSearch(term = "Dryden", field = "Author")

This function does two things: it creates a special kind of table called a data frame in R, and it displays that data frame in your viewing window. The search results pop up in a format that looks very much like a spreadsheet and that contains all the EEBO metadata for every book that includes John Dryden as a contributing author.

Of course you can search by any field. If searching by date, don’t use the term argument, instead use range.

tcpSearch(range = 1580:1620, field = "Date")

(Notice that you should not use quotation marks around the date range. This tells R to treat the dates like numbers, rather than like words.)

To download the files, first you need to save the results of your search as a .csv index file. Create a folder to store the results! You don’t want to fill your desktop with hundreds of XML documents. After creating the folder, set it as your working directory. (The working directory is the folder that R looks to first for reading and writing files.) In R Studio, you can navigate to the folder in the “Files” pane on the lower right, then click “More –> Set as working directory”. Otherwise, you can use the setwd() command, as in the example here:

dir.create(path = "testFolder")
setwd("testFolder")

Now store the results:

results = tcpSearch(term = "Dryden", field = "Author", write = T)

In addition to performing the search, this command saves the results in two places. First, the data frame is stored as an object called results in your R environment. (You should see it in the upper right panel of R Studio.) Second, it saves a .csv file called "index.csv" in your working directory. (You should see it appear in the lower right.)

Note that tcpSearch does not include advanced search functionality, nor does it include any special function for further filtering the results. If you want to drill down with some complex search and you’re comfortable with R, just filter the results data frame however you like, then re-save it using write.csv(). If you’re not yet comfortable with R’s syntax, you can open the index in Microsoft Excel, delete out the rows you don’t want, re-save it (remember to keep it in .csv format), and then upload it back into R like this:

results = read.csv("index.csv")

This saves over the results object by reading the new contents of the index back into it. (Reading and writing files using R is really easy to do, once you’ve done it a few times.)

tcpDownload(results)

Depending on how big your index is and how strong your internet connection is, this may take awhile. If you’re just doing a few dozen or few hundred documents, it just takes a minute or so, usually.

At the end, you’ll have everything you need to get started: a folder filled with EEBO-TCP XML files and a .csv index that holds the metadata for each.

When working with students, you won’t want them downloading the entire corpus, but for your own research you’ll quickly find that it’s easier to do the download once, then just change the index file based on your interests at any given moment. To do this, run:

data(tcp)
tcp = tcp[which(tcp$Status == "Free"),] tcpDownload(tcp) This will download more than 32,000 documents directly to your hard drive. It’s several GB worth of data. Be sure you save it in a convenient place. ### Importing TEI documents into R When I’ve taught coding to humanities professors and graduate students, by far and away the biggest challenge has just been keeping track of all the steps. When you start defining variables and creating lists and tables of data, your workspace (your ‘environment’) quickly gets cluttered with objects. Really, the entire purpose of tei2r is to mitigate this problem by creating a structure in your R environment that mimics the results of a bibliographic search. Your index file, created above, provides your list of documents, and it is the blueprint for everything that happens in tei2r. You can use your index to create a document list, or docList, that holds all the metadata about your texts, as well as all the information R will need to analyze those documents in more complex operations. In this example, let’s return to the works of Dryden. If you haven’t done so, re-write your folder like this: dir.create("dryden") setwd("dryden") results = tcpSearch(term = "Dryden", field = "Author", write = T) tcpDownload(results) After the download completes (it’ll take about a minute), you’ll have a folder called “dryden” that has the index.csv file and a collection of 131 XML documents. Let’s get them into R. dl = buildDocList(directory = getwd(), indexFile = "index.csv") Now, look and see what’s in the document list: print(dl) Each one of the @ symbols corresponds to a different slot in the document list, and each slot holds a specific kind of data. To view the index, for example, you could run View(dl@index). (Be sure to capitalize View()) As you can see, the docList object stores several bits of data: 1. The path to the source directory 2. The filenames and the full paths to each file 3. The path to the index .csv file 4. The index itself, stored as a data frame in the R environment 5. The path to your stopwords file, if you have one 6. The stopwords themselves (tei2r has a default list), stored as a character vector Notice that you still haven’t imported the texts themselves. The document list is simply a structure that holds all the metadata for your collection in R, including the location of the folder (in case you change around your working directory), links and bibliographic data for each file, as well as the location and content of your stopwords list. Everything in tei2r and its complementary packages, htn and empson, is built on the basic document list structure. This way, you only have to enter the filepath, the index, and the stopwords once, and R will remember what your settings are. To import the texts, you’ll need to create one more object: dt = importTexts(dl) This operation will take about a minute or so. The importTexts function automates a number of very common operations in R – importing XML data, stripping the tags and converting to plain text, switching to lower-case, removing stopwords, and regularizing the long-S in EEBO data.1 If you wanted the text but didn’t want to remove stopwords, adjust the parameter: dt = importTexts(dl = dl, removeStopwords = FALSE) Note, too, that importTexts works with plain-text documents just fine. However, if you’re using XML, make sure it’s in proper TEI format. tei2r won’t work for XML encoded using other namespaces. When imported into R, your documents’ texts have a structure very similar to that of the list itself: it holds the name of the source directory and the index file, then a big list that holds each text as a character vector. To get a sense of the contours of the docTexts object, look at dt: print(dt) You’ll see a couple pieces of metadata and the first several words for each document, usually from the title page. Notice that, by default, the texts are stored with their TCP number as the key. If you ever need to look a number up, remember that the index is stored in your environment. Just call View(dl@index). To print out the character vector of any individual text, you can select it by number (that is, its place in the order of the collection) or by TCP number. For example, here are two different ways to access the word vectors that make up Dryden’s Tyrannick Love, which has the TCP number ‘A36708’ and is the 72nd item in the collection: dt@text$A36708

dt@text[[72]]

tei2r does not have many built-in analytical functions, but it does have a few. To see the word frequencies for each document in the collection, call

df = frequencies(dt)

To build a correspondence around any single term, try

dc = buildConcordance(dt = dt, keyword = "wit", context = 5)

Each of these objects will have a structure very similar to the others: some metadata at the top, followed by a list of results for each document in your collection. Words frequency counts and concordances are very useful for text analysis of various kinds. The empson package uses them to perform various kinds of more advanced semantic analysis. But even on their own, they can be very useful for getting a picture of how the documents in your collection relate to each other.

### Topic modeling with mallet and tei2r

Topic modeling with the mallet package is already pretty easy, but I’ve found that a number of steps need to be performed every time I run a model. There are some constraints: using mallet in R Studio means that you’re constrained to R’s memory limitations. This is fine for small- to medium-sized collections. But if you want to model more than a few hundred texts, you’re likely to run into memory errors.

First make sure you have the mallet package installed, then run this:

library(mallet)
tmod = buildModel(dl = dl, dt = dt, tnum = 20)

The buildModel function runs the model and stores the results in several different slots for easy viewing:

1. tmod@index: the index containing all your documents’ metadata
2. tmod@topics: a table showing the top words in each topic
3. tmod@frequencies: a table showing how frequent each topic is in each document
4. tmod@weights: a table showing how frequent each word is in each topic

The data in any of these slots can be viewed, written to csv, or plotted in any number of ways. The mallet package has a number of options, most of which can be controlled as parameters in buildModel(). Users interested in delving deeper into mallet are encouraged to consult its documentation and work with its functions directly.

1. Right now, importTexts always cleans up the long-S and always converts XML into plain-text character vectors. The only adjustable parameters are removeCaps and removeStopwords. If you use tei2r and would like more options built into this or any function, please contact the developers.