An R package for reading text files in all their various formats, by Ken Benoit, Adam Obeng, Paul Nulty, Aki Matsuo, Kohei Watanabe, and Stefan Müller.
readtext is a one-function package that does exactly what it says on the tin: It reads files containing text, along with any associated document-level metadata, which we call “docvars”, for document variables. Plain text files do not have docvars, but other forms such as .csv, .tab, .xml, and .json files usually do.
readtext accepts filemasks, so that you can specify a pattern to load multiple texts, and these texts can even be of multiple types. readtext is smart enough to process them correctly, returning a data.frame with a primary field “text” containing a character vector of the texts, and additional columns of the data.frame as found in the document variables from the source files.
As encoding can also be a challenging issue for those reading in texts, we include functions for diagnosing encodings on a file-by-file basis, and allow you to specify vectorized input encodings to read in file types with individually set (and different) encodings. (All encoding functions are handled by the stringi package.)
From CRAN
From GitHub, if you want the latest development version.
Linux note: There are a couple of dependencies that may not be available on linux systems. On Debian/Ubuntu try installing these packages by running these commands at the command line:
readtext supports plain text files (.txt), data in some form of JavaScript Object Notation (.json), comma-or tab-separated values (.csv, .tab, .tsv), XML documents (.xml), as well as PDF, Microsoft Word formatted files and other document formats (.pdf, .doc, .docx, .odt, .rtf). readtext also handles multiple files and file types using for instance a “glob” expression, files from a URL or an archive file (.zip, .tar, .tar.gz, .tar.bz).
The file formats are determined automatically by the filename extensions. If a file has no extension or is unknown, readtext will assume that it is plain text. The following command, for instance, will load in all of the files from the subdirectory txt/UDHR/:
require(readtext)
## Loading required package: readtext
# get the data directory from readtext
DATA_DIR <- system.file("extdata/", package = "readtext")
# read in all files from a folder
readtext(paste0(DATA_DIR, "/txt/UDHR/*"))
## readtext object consisting of 13 documents and 0 docvars.
## # Description: df[,2] [13 × 2]
## doc_id text
## <chr> <chr>
## 1 UDHR_chinese.txt "\"世界人权宣言\n联合国\"..."
## 2 UDHR_czech.txt "\"VŠEOBECNÁ \"..."
## 3 UDHR_danish.txt "\"Den 10. de\"..."
## 4 UDHR_english.txt "\"Universal \"..."
## 5 UDHR_french.txt "\"Déclaratio\"..."
## 6 UDHR_georgian.txt "\"FLFVBFYBC \"..."
## # … with 7 more rowsFor files that contain multiple documents, such as comma-separated-value documents, you will need to specify the column name containing the texts, using the text_field argument:
# read in comma-separated values and specify text field
readtext(paste0(DATA_DIR, "/csv/inaugCorpus.csv"), text_field = "texts")
## readtext object consisting of 5 documents and 3 docvars.
## # Description: df[,5] [5 × 5]
## doc_id text Year President FirstName
## <chr> <chr> <int> <chr> <chr>
## 1 inaugCorpus.csv.1 "\"Fellow-Cit\"..." 1789 Washington George
## 2 inaugCorpus.csv.2 "\"Fellow cit\"..." 1793 Washington George
## 3 inaugCorpus.csv.3 "\"When it wa\"..." 1797 Adams John
## 4 inaugCorpus.csv.4 "\"Friends an\"..." 1801 Jefferson Thomas
## 5 inaugCorpus.csv.5 "\"Proceeding\"..." 1805 Jefferson ThomasFor a more complete demonstration, see the package vignette.
readtext was originally developed in early versions of the quanteda package for the quantitative analysis of textual data. Because quanteda’s corpus constructor recognizes the data.frame format returned by readtext(), it can construct a corpus directly from a readtext object, preserving all docvars and other meta-data.
require(quanteda)
## Loading required package: quanteda
## Package version: 2.1.2
## Parallel computing: 2 of 12 threads used.
## See https://quanteda.io for tutorials and examples.
##
## Attaching package: 'quanteda'
## The following object is masked from 'package:utils':
##
## View
# read in comma-separated values with readtext
rt_csv <- readtext(paste0(DATA_DIR, "/csv/inaugCorpus.csv"), text_field = "texts")
# create quanteda corpus
corpus_csv <- corpus(rt_csv)
summary(corpus_csv, 5)
## Corpus consisting of 5 documents, showing 5 documents:
##
## Text Types Tokens Sentences Year President FirstName
## inaugCorpus.csv.1 625 1539 23 1789 Washington George
## inaugCorpus.csv.2 96 147 4 1793 Washington George
## inaugCorpus.csv.3 826 2577 37 1797 Adams John
## inaugCorpus.csv.4 717 1923 41 1801 Jefferson Thomas
## inaugCorpus.csv.5 804 2380 45 1805 Jefferson Thomasreadtext returns a data.frame that is formatted as per the corpus structure of the Text Interchange Format, it can easily be used by other packages that can accept a corpus in data.frame format.
If you only want a named character object, readtext also defines an as.character() method that inputs its data.frame and returns just the named character vector of texts, conforming to the TIF definition of the character version of a corpus.