The vignette walks you through importing a variety of different text files into R using the readtext package. Currently, 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 and Microsoft Word formatted files (.pdf, .doc, .docx).
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). Usually, you do not have to determine the format of the files explicitly - readtext takes this information from the file ending.
The readtext package comes with a data directory called extdata that contains examples of all files listed above. In the vignette, we use this data directory.
The extdata directory contains several subfolders that include different text files. In the following examples, we load one or more files stored in each of these folders. The paste0 command is used to concatenate the extdata folder from the readtext package with the subfolders. When reading in custom text files, you will need to determine your own data directory (see ?setwd()).
The folder “txt” contains a subfolder named UDHR with .txt files of the Universal Declaration of Human Rights in 13 languages.
# 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 rowsWe can specify document-level metadata (docvars) based on the file names or on a separate data.frame. Below we take the docvars from the filenames (docvarsfrom = "filenames") and set the names for each variable (docvarnames = c("unit", "context", "year", "language", "party")). The command dvsep = "_" determines the separator (a regular expression character string) included in the filenames to delimit the docvar elements.
# Manifestos with docvars from filenames
readtext(paste0(DATA_DIR, "/txt/EU_manifestos/*.txt"),
docvarsfrom = "filenames",
docvarnames = c("unit", "context", "year", "language", "party"),
dvsep = "_",
encoding = "ISO-8859-1")
## readtext object consisting of 17 documents and 5 docvars.
## # Description: df[,7] [17 × 7]
## doc_id text unit context year language party
## <chr> <chr> <chr> <chr> <int> <chr> <chr>
## 1 EU_euro_2004_de_PSE.txt "\"PES · PSE \"..." EU euro 2004 de PSE
## 2 EU_euro_2004_de_V.txt "\"Gemeinsame\"..." EU euro 2004 de V
## 3 EU_euro_2004_en_PSE.txt "\"PES · PSE \"..." EU euro 2004 en PSE
## 4 EU_euro_2004_en_V.txt "\"Manifesto\n\"..… EU euro 2004 en V
## 5 EU_euro_2004_es_PSE.txt "\"PES · PSE \"..." EU euro 2004 es PSE
## 6 EU_euro_2004_es_V.txt "\"Manifesto\n\"..… EU euro 2004 es V
## # … with 11 more rowsreadtext can also curse through subdirectories. In our example, the folder txt/movie_reviews contains two subfolders (called neg and pos). We can load all texts included in both folders.
# Recurse through subdirectories
readtext(paste0(DATA_DIR, "/txt/movie_reviews/*"))
## readtext object consisting of 10 documents and 0 docvars.
## # Description: df[,2] [10 × 2]
## doc_id text
## <chr> <chr>
## 1 neg_cv000_29416.txt "\"plot : two\"..."
## 2 neg_cv001_19502.txt "\"the happy \"..."
## 3 neg_cv002_17424.txt "\"it is movi\"..."
## 4 neg_cv003_12683.txt "\" \" quest f\"..."
## 5 neg_cv004_12641.txt "\"synopsis :\"..."
## 6 pos_cv000_29590.txt "\"films adap\"..."
## # … with 4 more rowsRead in comma separated values (.csv files) that contain textual data. We determine the texts variable in our .csv file as the text_field. This is the column that contains the actual text. The other columns of the original csv file (Year, President, FirstName) are by default treated as document-level variables.
# Read in comma-separated values
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 ThomasThe same procedure applies to tab-separated values.
# Read in tab-separated values
readtext(paste0(DATA_DIR, "/tsv/dailsample.tsv"), text_field = "speech")
## readtext object consisting of 33 documents and 9 docvars.
## # Description: df[,11] [33 × 11]
## doc_id text speechID memberID partyID constID title date member_name
## <chr> <chr> <int> <int> <int> <int> <chr> <chr> <chr>
## 1 dails… "\"M… 1 977 22 158 1. C… 1919… Count Geor…
## 2 dails… "\"I… 2 1603 22 103 1. C… 1919… Mr. Pádrai…
## 3 dails… "\"'… 3 116 22 178 1. C… 1919… Mr. Cathal…
## 4 dails… "\"T… 4 116 22 178 2. C… 1919… Mr. Cathal…
## 5 dails… "\"L… 5 116 22 178 3. A… 1919… Mr. Cathal…
## 6 dails… "\"-… 6 116 22 178 3. A… 1919… Mr. Cathal…
## # … with 27 more rows, and 2 more variables: party_name <chr>, const_name <chr>You can also read .json data. Again you need to specify the text_field.
## Read in JSON data
readtext(paste0(DATA_DIR, "/json/inaugural_sample.json"), text_field = "texts")
## readtext object consisting of 3 documents and 3 docvars.
## # Description: df[,5] [3 × 5]
## doc_id text Year President FirstName
## <chr> <chr> <int> <chr> <chr>
## 1 inaugural_sample.json.1 "\"Fellow-Cit\"..." 1789 Washington George
## 2 inaugural_sample.json.2 "\"Fellow cit\"..." 1793 Washington George
## 3 inaugural_sample.json.3 "\"When it wa\"..." 1797 Adams Johnreadtext can also read in and convert .pdf files.
In the example below we load all .pdf files stored in the UDHR folder, and determine that the docvars shall be taken from the filenames. We call the document-level variables document and language, and specify the delimiter (dvsep).
## Read in Universal Declaration of Human Rights pdf files
(rt_pdf <- readtext(paste0(DATA_DIR, "/pdf/UDHR/*.pdf"),
docvarsfrom = "filenames",
docvarnames = c("document", "language"),
sep = "_"))
## readtext object consisting of 11 documents and 2 docvars.
## # Description: df[,4] [11 × 4]
## doc_id text document language
## <chr> <chr> <chr> <chr>
## 1 UDHR_chinese.pdf "\"世界人权宣言\n联合国\"..." UDHR chinese
## 2 UDHR_czech.pdf "\"VŠEOBECNÁ \"..." UDHR czech
## 3 UDHR_danish.pdf "\"Den 10. de\"..." UDHR danish
## 4 UDHR_english.pdf "\"Universal \"..." UDHR english
## 5 UDHR_french.pdf "\"Déclaratio\"..." UDHR french
## 6 UDHR_greek.pdf "\"ΟΙΚΟΥΜΕΝΙΚ\"..." UDHR greek
## # … with 5 more rowsMicrosoft Word formatted files are converted through the package antiword for older .doc files, and using XML for newer .docx files.
You can also read in text directly from a URL.
Finally, it is possible to include text from archives.
readtext was originally developed in early versions of the quanteda package for the quantitative analysis of textual data. It was spawned from the textfile() function from that package, and now lives exclusively in readtext. 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.
You can easily construct a corpus from a readtext object.
# 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 ThomasWhen a document contains page numbers, they are imported as well. If you want to remove them, you can use a regular expression. We strongly recommend using the stringi package. For the most common regular expressions you can look at this cheatsheet.
You first need to check in the original file in which format the page numbers occur (e.g., “1”, “-1-”, “page 1” etc.). We can make use of the fact that page numbers are almost always preceded and followed by a linebreak (\n). After loading the text with readtext, you can replace the page numbers.
In the first example, the page numbers have the format “page X”.
# Make some text with page numbers
sample_text_a <- "The quick brown fox named Seamus jumps over the lazy dog also named Seamus,
page 1
with the newspaper from a boy named quick Seamus, in his mouth.
page 2
The quicker brown fox jumped over 2 lazy dogs."
sample_text_a
## [1] "The quick brown fox named Seamus jumps over the lazy dog also named Seamus, \npage 1 \nwith the newspaper from a boy named quick Seamus, in his mouth.\npage 2\nThe quicker brown fox jumped over 2 lazy dogs."
# Remove "page" and respective digit
sample_text_a2 <- unlist(stri_split_fixed(sample_text_a, '\n'), use.names = FALSE)
sample_text_a2 <- stri_replace_all_regex(sample_text_a2, "page \\d*", "")
sample_text_a2 <- stri_trim_both(sample_text_a2)
sample_text_a2 <- sample_text_a2[sample_text_a2 != '']
stri_paste(sample_text_a2, collapse = '\n')
## [1] "The quick brown fox named Seamus jumps over the lazy dog also named Seamus,\nwith the newspaper from a boy named quick Seamus, in his mouth.\nThe quicker brown fox jumped over 2 lazy dogs."In the second example we remove page numbers which have the format “- X -”.
sample_text_b <- "The quick brown fox named Seamus
- 1 -
jumps over the lazy dog also named Seamus, with
- 2 -
the newspaper from a boy named quick Seamus, in his mouth.
- 33 -
The quicker brown fox jumped over 2 lazy dogs."
sample_text_b
## [1] "The quick brown fox named Seamus \n- 1 - \njumps over the lazy dog also named Seamus, with \n- 2 - \nthe newspaper from a boy named quick Seamus, in his mouth. \n- 33 - \nThe quicker brown fox jumped over 2 lazy dogs."
sample_text_b2 <- unlist(stri_split_fixed(sample_text_b, '\n'), use.names = FALSE)
sample_text_b2 <- stri_replace_all_regex(sample_text_b2, "[-] \\d* [-]", "")
sample_text_b2 <- stri_trim_both(sample_text_b2)
sample_text_b2 <- sample_text_b2[sample_text_b2 != '']
stri_paste(sample_text_b2, collapse = '\n')
## [1] "The quick brown fox named Seamus\njumps over the lazy dog also named Seamus, with\nthe newspaper from a boy named quick Seamus, in his mouth.\nThe quicker brown fox jumped over 2 lazy dogs."Such stringi functions can also be applied to readtext objects.
Sometimes files of the same type have different encodings. If the encoding of a file is included in the file name, we can extract this information and import the texts correctly.
# create a temporary directory to extract the .zip file
FILEDIR <- tempdir()
# unzip file
unzip(system.file("extdata", "data_files_encodedtexts.zip", package = "readtext"), exdir = FILEDIR)Here, we will get the encoding from the filenames themselves.
# get encoding from filename
filenames <- list.files(FILEDIR, "^(Indian|UDHR_).*\\.txt$")
head(filenames)
## [1] "IndianTreaty_English_UTF-16LE.txt" "IndianTreaty_English_UTF-8-BOM.txt"
## [3] "UDHR_Arabic_ISO-8859-6.txt" "UDHR_Arabic_UTF-8.txt"
## [5] "UDHR_Arabic_WINDOWS-1256.txt" "UDHR_Chinese_GB2312.txt"
# Strip the extension
filenames <- gsub(".txt$", "", filenames)
parts <- strsplit(filenames, "_")
fileencodings <- sapply(parts, "[", 3)
head(fileencodings)
## [1] "UTF-16LE" "UTF-8-BOM" "ISO-8859-6" "UTF-8" "WINDOWS-1256"
## [6] "GB2312"
# Check whether certain file encodings are not supported
notAvailableIndex <- which(!(fileencodings %in% iconvlist()))
fileencodings[notAvailableIndex]
## [1] "UTF-8-BOM"If we read the text files without specifying the encoding, we get erroneously formatted text. To avoid this, we determine the encoding using the character object fileencoding created above.
We can also add docvars based on the filenames.
txts <- readtext(paste0(DATA_DIR, "/data_files_encodedtexts.zip"),
encoding = fileencodings,
docvarsfrom = "filenames",
docvarnames = c("document", "language", "input_encoding"))
print(txts, n = 50)
## readtext object consisting of 36 documents and 3 docvars.
## # Description: df[,5] [36 × 5]
## doc_id text document language input_encoding
## <chr> <chr> <chr> <chr> <chr>
## 1 IndianTreaty_English_… "\"WHEREAS, t\"..." IndianTre… English UTF-16LE
## 2 IndianTreaty_English_… "\"ARTICLE 1.\"..." IndianTre… English UTF-8-BOM
## 3 UDHR_Arabic_ISO-8859-… "\"الديباجة\nل\"..… UDHR Arabic ISO-8859-6
## 4 UDHR_Arabic_UTF-8.txt "\"الديباجة\nل\"..… UDHR Arabic UTF-8
## 5 UDHR_Arabic_WINDOWS-1… "\"الديباجة\nل\"..… UDHR Arabic WINDOWS-1256
## 6 UDHR_Chinese_GB2312.t… "\"世界人权宣言\n联合国\"..… UDHR Chinese GB2312
## 7 UDHR_Chinese_GBK.txt "\"世界人权宣言\n联合国\"..… UDHR Chinese GBK
## 8 UDHR_Chinese_UTF-8.txt "\"世界人权宣言\n联合国\"..… UDHR Chinese UTF-8
## 9 UDHR_English_UTF-16BE… "\"Universal \"..." UDHR English UTF-16BE
## 10 UDHR_English_UTF-16LE… "\"Universal \"..." UDHR English UTF-16LE
## 11 UDHR_English_UTF-8.txt "\"Universal \"..." UDHR English UTF-8
## 12 UDHR_English_WINDOWS-… "\"Universal \"..." UDHR English WINDOWS-1252
## 13 UDHR_French_ISO-8859-… "\"Déclaratio\"..." UDHR French ISO-8859-1
## 14 UDHR_French_UTF-8.txt "\"Déclaratio\"..." UDHR French UTF-8
## 15 UDHR_French_WINDOWS-1… "\"Déclaratio\"..." UDHR French WINDOWS-1252
## 16 UDHR_German_ISO-8859-… "\"Die Allgem\"..." UDHR German ISO-8859-1
## 17 UDHR_German_UTF-8.txt "\"Die Allgem\"..." UDHR German UTF-8
## 18 UDHR_German_WINDOWS-1… "\"Die Allgem\"..." UDHR German WINDOWS-1252
## 19 UDHR_Greek_CP1253.txt "\"ΟΙΚΟΥΜΕΝΙΚ\"..." UDHR Greek CP1253
## 20 UDHR_Greek_ISO-8859-7… "\"ΟΙΚΟΥΜΕΝΙΚ\"..." UDHR Greek ISO-8859-7
## 21 UDHR_Greek_UTF-8.txt "\"ΟΙΚΟΥΜΕΝΙΚ\"..." UDHR Greek UTF-8
## 22 UDHR_Hindi_UTF-8.txt "\"मानव अधिका\"..." UDHR Hindi UTF-8
## 23 UDHR_Icelandic_ISO-88… "\"Mannréttin\"..." UDHR Iceland… ISO-8859-1
## 24 UDHR_Icelandic_UTF-8.… "\"Mannréttin\"..." UDHR Iceland… UTF-8
## 25 UDHR_Icelandic_WINDOW… "\"Mannréttin\"..." UDHR Iceland… WINDOWS-1252
## 26 UDHR_Japanese_CP932.t… "\"『世界人権宣言』\n \"..… UDHR Japanese CP932
## 27 UDHR_Japanese_ISO-202… "\"『世界人権宣言』\n \"..… UDHR Japanese ISO-2022-JP
## 28 UDHR_Japanese_UTF-8.t… "\"『世界人権宣言』\n \"..… UDHR Japanese UTF-8
## 29 UDHR_Japanese_WINDOWS… "\"『世界人権宣言』\n \"..… UDHR Japanese WINDOWS-936
## 30 UDHR_Korean_ISO-2022-… "\"세 계 인 권 선 \"...… UDHR Korean ISO-2022-KR
## 31 UDHR_Korean_UTF-8.txt "\"세 계 인 권 선 \"...… UDHR Korean UTF-8
## 32 UDHR_Russian_ISO-8859… "\"Всеобщая д\"..." UDHR Russian ISO-8859-5
## 33 UDHR_Russian_KOI8-R.t… "\"Всеобщая д\"..." UDHR Russian KOI8-R
## 34 UDHR_Russian_UTF-8.txt "\"Всеобщая д\"..." UDHR Russian UTF-8
## 35 UDHR_Russian_WINDOWS-… "\"Всеобщая д\"..." UDHR Russian WINDOWS-1251
## 36 UDHR_Thai_UTF-8.txt "\"ปฏิญญาสากล\"..." UDHR Thai UTF-8From this file we can easily create a quanteda corpus object.
corpus_txts <- corpus(txts)
summary(corpus_txts, 5)
## Corpus consisting of 36 documents, showing 5 documents:
##
## Text Types Tokens Sentences document
## IndianTreaty_English_UTF-16LE.txt 617 2577 155 IndianTreaty
## IndianTreaty_English_UTF-8-BOM.txt 645 3092 154 IndianTreaty
## UDHR_Arabic_ISO-8859-6.txt 753 1555 86 UDHR
## UDHR_Arabic_UTF-8.txt 753 1555 86 UDHR
## UDHR_Arabic_WINDOWS-1256.txt 753 1555 86 UDHR
## language input_encoding
## English UTF-16LE
## English UTF-8-BOM
## Arabic ISO-8859-6
## Arabic UTF-8
## Arabic WINDOWS-1256