Quick Start Guide
Installing the package
Since quanteda is available on CRAN, you can install by using your GUI’s R package installer, or execute:
install.packages("quanteda") See an instructions at https://github.com/quanteda/quanteda to install the GitHub version,
Additional recommended packages:
The following packages work well with or extend quanteda and we recommend that you also install them:
- readtext: An easy way to read text data into R, from almost any input format.
- spacyr: NLP using the spaCy library, including part-of-speech tagging, entity recognition, and dependency parsing.
quanteda.corpora: Additional textual data for use with quanteda.
devtools::install_github("quanteda/quanteda.corpora")LIWCalike: An R implementation of the Linguistic Inquiry and Word Count approach to text analysis.
devtools::install_github("kbenoit/LIWCalike")
Creating a Corpus
You load the package to access to functions and data in the package.
library(quanteda)Currently available corpus sources
quanteda has a simple and powerful companion package for loading texts: readtext. The main function in this package, readtext(), takes a file or fileset from disk or a URL, and returns a type of data.frame that can be used directly with the corpus() constructor function, to create a quanteda corpus object.
readtext() works on:
- text (
.txt) files; - comma-separated-value (
.csv) files; - XML formatted data;
- data from the Facebook API, in JSON format;
- data from the Twitter API, in JSON format; and
- generic JSON data.
The corpus constructor command corpus() works directly on:
- a vector of character objects, for instance that you have already loaded into the workspace using other tools;
- a
VCorpuscorpus object from the tm package. - a data.frame containing a text column and any other document-level metadata.
Building a corpus from a character vector
The simplest case is to create a corpus from a vector of texts already in memory in R. This gives the advanced R user complete flexbility with his or her choice of text inputs, as there are almost endless ways to get a vector of texts into R.
If we already have the texts in this form, we can call the corpus constructor function directly. We can demonstrate this on the built-in character object of the texts about immigration policy extracted from the 2010 election manifestos of the UK political parties (called data_char_ukimmig2010).
my_corpus <- corpus(data_char_ukimmig2010) # build a new corpus from the texts
summary(my_corpus)## Corpus consisting of 9 documents, showing 9 documents:
##
## Text Types Tokens Sentences
## BNP 1125 3280 88
## Coalition 142 260 4
## Conservative 251 499 15
## Greens 322 679 21
## Labour 298 683 29
## LibDem 251 483 14
## PC 77 114 5
## SNP 88 134 4
## UKIP 346 723 27If we wanted, we could add some document-level variables – what quanteda calls docvars – to this corpus.
We can do this using the R’s names() function to get the names of the character vector data_char_ukimmig2010, and assign this to a document variable (docvar).
docvars(my_corpus, "Party") <- names(data_char_ukimmig2010)
docvars(my_corpus, "Year") <- 2010
summary(my_corpus)## Corpus consisting of 9 documents, showing 9 documents:
##
## Text Types Tokens Sentences Party Year
## BNP 1125 3280 88 BNP 2010
## Coalition 142 260 4 Coalition 2010
## Conservative 251 499 15 Conservative 2010
## Greens 322 679 21 Greens 2010
## Labour 298 683 29 Labour 2010
## LibDem 251 483 14 LibDem 2010
## PC 77 114 5 PC 2010
## SNP 88 134 4 SNP 2010
## UKIP 346 723 27 UKIP 2010If we wanted to tag each document with additional meta-data not considered a document variable of interest for analysis, but rather something that we need to know as an attribute of the document, we could also add those to our corpus.
metadoc(my_corpus, "language") <- "english"## Warning: metadoc is deprecatedmetadoc(my_corpus, "docsource") <- paste("data_char_ukimmig2010", 1:ndoc(my_corpus), sep = "_")## Warning: metadoc is deprecatedsummary(my_corpus, showmeta = TRUE)## Corpus consisting of 9 documents, showing 9 documents:
##
## Text Types Tokens Sentences Party Year _language
## BNP 1125 3280 88 BNP 2010 english
## Coalition 142 260 4 Coalition 2010 english
## Conservative 251 499 15 Conservative 2010 english
## Greens 322 679 21 Greens 2010 english
## Labour 298 683 29 Labour 2010 english
## LibDem 251 483 14 LibDem 2010 english
## PC 77 114 5 PC 2010 english
## SNP 88 134 4 SNP 2010 english
## UKIP 346 723 27 UKIP 2010 english
## _docsource
## data_char_ukimmig2010_1
## data_char_ukimmig2010_2
## data_char_ukimmig2010_3
## data_char_ukimmig2010_4
## data_char_ukimmig2010_5
## data_char_ukimmig2010_6
## data_char_ukimmig2010_7
## data_char_ukimmig2010_8
## data_char_ukimmig2010_9The last command, metadoc, allows you to define your own document meta-data fields. Note that in assiging just the single value of "english", R has recycled the value until it matches the number of documents in the corpus. In creating a simple tag for our custom metadoc field docsource, we used the quanteda function ndoc() to retrieve the number of documents in our corpus. This function is deliberately designed to work in a way similar to functions you may already use in R, such as nrow() and ncol().
Loading in files using the readtext package
require(readtext)
# Twitter json
mytf1 <- readtext("~/Dropbox/QUANTESS/social media/zombies/tweets.json")
my_corpusTwitter <- corpus(mytf1)
summary(my_corpusTwitter, 5)
# generic json - needs a textfield specifier
mytf2 <- readtext("~/Dropbox/QUANTESS/Manuscripts/collocations/Corpora/sotu/sotu.json",
textfield = "text")
summary(corpus(mytf2), 5)
# text file
mytf3 <- readtext("~/Dropbox/QUANTESS/corpora/project_gutenberg/pg2701.txt", cache = FALSE)
summary(corpus(mytf3), 5)
# multiple text files
mytf4 <- readtext("~/Dropbox/QUANTESS/corpora/inaugural/*.txt", cache = FALSE)
summary(corpus(mytf4), 5)
# multiple text files with docvars from filenames
mytf5 <- readtext("~/Dropbox/QUANTESS/corpora/inaugural/*.txt",
docvarsfrom = "filenames", sep = "-", docvarnames = c("Year", "President"))
summary(corpus(mytf5), 5)
# XML data
mytf6 <- readtext("~/Dropbox/QUANTESS/quanteda_working_files/xmlData/plant_catalog.xml",
textfield = "COMMON")
summary(corpus(mytf6), 5)
# csv file
write.csv(data.frame(inaugSpeech = texts(data_corpus_inaugural),
docvars(data_corpus_inaugural)),
file = "/tmp/inaug_texts.csv", row.names = FALSE)
mytf7 <- readtext("/tmp/inaug_texts.csv", textfield = "inaugSpeech")
summary(corpus(mytf7), 5)How a quanteda corpus works
Corpus principles
A corpus is designed to be a “library” of original documents that have been converted to plain, UTF-8 encoded text, and stored along with meta-data at the corpus level and at the document-level. We have a special name for document-level meta-data: docvars. These are variables or features that describe attributes of each document.
A corpus is designed to be a more or less static container of texts with respect to processing and analysis. This means that the texts in corpus are not designed to be changed internally through (for example) cleaning or pre-processing steps, such as stemming or removing punctuation. Rather, texts can be extracted from the corpus as part of processing, and assigned to new objects, but the idea is that the corpus will remain as an original reference copy so that other analyses – for instance those in which stems and punctuation were required, such as analyzing a reading ease index – can be performed on the same corpus.
To extract texts from a corpus, we use an extractor, called texts().
texts(data_corpus_inaugural)[2]## 1793-Washington
## "Fellow citizens, I am again called upon by the voice of my country to execute the functions of its Chief Magistrate. When the occasion proper for it shall arrive, I shall endeavor to express the high sense I entertain of this distinguished honor, and of the confidence which has been reposed in me by the people of united America.\n\nPrevious to the execution of any official act of the President the Constitution requires an oath of office. This oath I am now about to take, and in your presence: That if it shall be found during my administration of the Government I have in any instance violated willingly or knowingly the injunctions thereof, I may (besides incurring constitutional punishment) be subject to the upbraidings of all who are now witnesses of the present solemn ceremony.\n\n "To summarize the texts from a corpus, we can call a summary() method defined for a corpus.
summary(data_corpus_irishbudget2010)## Corpus consisting of 14 documents, showing 14 documents:
##
## Text Types Tokens Sentences year debate number foren
## Lenihan, Brian (FF) 1953 8641 374 2010 BUDGET 01 Brian
## Bruton, Richard (FG) 1040 4446 217 2010 BUDGET 02 Richard
## Burton, Joan (LAB) 1624 6393 307 2010 BUDGET 03 Joan
## Morgan, Arthur (SF) 1595 7107 343 2010 BUDGET 04 Arthur
## Cowen, Brian (FF) 1629 6599 250 2010 BUDGET 05 Brian
## Kenny, Enda (FG) 1148 4232 153 2010 BUDGET 06 Enda
## ODonnell, Kieran (FG) 678 2297 133 2010 BUDGET 07 Kieran
## Gilmore, Eamon (LAB) 1181 4177 201 2010 BUDGET 08 Eamon
## Higgins, Michael (LAB) 488 1286 44 2010 BUDGET 09 Michael
## Quinn, Ruairi (LAB) 439 1284 59 2010 BUDGET 10 Ruairi
## Gormley, John (Green) 401 1030 49 2010 BUDGET 11 John
## Ryan, Eamon (Green) 510 1643 90 2010 BUDGET 12 Eamon
## Cuffe, Ciaran (Green) 442 1240 45 2010 BUDGET 13 Ciaran
## OCaolain, Caoimhghin (SF) 1188 4044 176 2010 BUDGET 14 Caoimhghin
## name party
## Lenihan FF
## Bruton FG
## Burton LAB
## Morgan SF
## Cowen FF
## Kenny FG
## ODonnell FG
## Gilmore LAB
## Higgins LAB
## Quinn LAB
## Gormley Green
## Ryan Green
## Cuffe Green
## OCaolain SFWe can save the output from the summary command as a data frame, and plot some basic descriptive statistics with this information:
tokenInfo <- summary(data_corpus_inaugural)
if (require(ggplot2))
ggplot(data=tokenInfo, aes(x = Year, y = Tokens, group = 1)) + geom_line() + geom_point() +
scale_x_continuous(labels = c(seq(1789, 2017, 12)), breaks = seq(1789, 2017, 12)) +
theme_bw()## Loading required package: ggplot2
# Longest inaugural address: William Henry Harrison
tokenInfo[which.max(tokenInfo$Tokens), ] ## Text Types Tokens Sentences Year President FirstName
## 14 1841-Harrison 1896 9144 210 1841 Harrison William HenryTools for handling corpus objects
Adding two corpus objects together
The + operator provides a simple method for concatenating two corpus objects. If they contain different sets of document-level variables, these will be stitched together in a fashion that guarantees that no information is lost. Corpus-level medata data is also concatenated.
library(quanteda)
my_corpus1 <- corpus(data_corpus_inaugural[1:5])
my_corpus2 <- corpus(data_corpus_inaugural[53:58])
my_corpus3 <- my_corpus1 + my_corpus2
summary(my_corpus3)## Corpus consisting of 11 documents, showing 11 documents:
##
## Text Types Tokens Sentences Year President FirstName
## text1 625 1538 23 1789 Washington George
## text2 96 147 4 1793 Washington George
## text3 826 2578 37 1797 Adams John
## text4 717 1927 41 1801 Jefferson Thomas
## text5 804 2381 45 1805 Jefferson Thomas
## text6 773 2449 111 1997 Clinton Bill
## text7 621 1808 97 2001 Bush George W.
## text8 773 2319 100 2005 Bush George W.
## text9 938 2711 110 2009 Obama Barack
## text10 814 2317 88 2013 Obama Barack
## text11 582 1660 88 2017 Trump Donald J.Subsetting corpus objects
There is a method of the corpus_subset() function defined for corpus objects, where a new corpus can be extracted based on logical conditions applied to docvars:
summary(corpus_subset(data_corpus_inaugural, Year > 1990))## Corpus consisting of 7 documents, showing 7 documents:
##
## Text Types Tokens Sentences Year President FirstName
## 1993-Clinton 642 1833 81 1993 Clinton Bill
## 1997-Clinton 773 2449 111 1997 Clinton Bill
## 2001-Bush 621 1808 97 2001 Bush George W.
## 2005-Bush 773 2319 100 2005 Bush George W.
## 2009-Obama 938 2711 110 2009 Obama Barack
## 2013-Obama 814 2317 88 2013 Obama Barack
## 2017-Trump 582 1660 88 2017 Trump Donald J.summary(corpus_subset(data_corpus_inaugural, President == "Adams"))## Corpus consisting of 2 documents, showing 2 documents:
##
## Text Types Tokens Sentences Year President FirstName
## 1797-Adams 826 2578 37 1797 Adams John
## 1825-Adams 1003 3152 74 1825 Adams John QuincyExploring corpus texts
The kwic function (keywords-in-context) performs a search for a word and allows us to view the contexts in which it occurs:
kwic(data_corpus_inaugural, "terror")##
## [1797-Adams, 1325] fraud or violence, by | terror |
## [1933-Roosevelt, 112] nameless, unreasoning, unjustified | terror |
## [1941-Roosevelt, 287] seemed frozen by a fatalistic | terror |
## [1961-Kennedy, 866] alter that uncertain balance of | terror |
## [1981-Reagan, 813] freeing all Americans from the | terror |
## [1997-Clinton, 1055] They fuel the fanaticism of | terror |
## [1997-Clinton, 1655] maintain a strong defense against | terror |
## [2009-Obama, 1632] advance their aims by inducing | terror |
##
## , intrigue, or venality
## which paralyzes needed efforts to
## , we proved that this
## that stays the hand of
## of runaway living costs.
## . And they torment the
## and destruction. Our children
## and slaughtering innocents, wekwic(data_corpus_inaugural, "terror", valuetype = "regex")##
## [1797-Adams, 1325] fraud or violence, by | terror |
## [1933-Roosevelt, 112] nameless, unreasoning, unjustified | terror |
## [1941-Roosevelt, 287] seemed frozen by a fatalistic | terror |
## [1961-Kennedy, 866] alter that uncertain balance of | terror |
## [1961-Kennedy, 990] of science instead of its | terrors |
## [1981-Reagan, 813] freeing all Americans from the | terror |
## [1981-Reagan, 2196] understood by those who practice | terrorism |
## [1997-Clinton, 1055] They fuel the fanaticism of | terror |
## [1997-Clinton, 1655] maintain a strong defense against | terror |
## [2009-Obama, 1632] advance their aims by inducing | terror |
## [2017-Trump, 1117] civilized world against radical Islamic | terrorism |
##
## , intrigue, or venality
## which paralyzes needed efforts to
## , we proved that this
## that stays the hand of
## . Together let us explore
## of runaway living costs.
## and prey upon their neighbors
## . And they torment the
## and destruction. Our children
## and slaughtering innocents, we
## , which we will eradicatekwic(data_corpus_inaugural, "communist*")##
## [1949-Truman, 834] the actions resulting from the | Communist |
## [1961-Kennedy, 519] -- not because the | Communists |
##
## philosophy are a threat to
## may be doing it,In the above summary, Year and President are variables associated with each document. We can access such variables with the docvars() function.
# inspect the document-level variables
head(docvars(data_corpus_inaugural))## Year President FirstName
## 1 1789 Washington George
## 2 1793 Washington George
## 3 1797 Adams John
## 4 1801 Jefferson Thomas
## 5 1805 Jefferson Thomas
## 6 1809 Madison James# inspect the corpus-level metadata
metacorpus(data_corpus_inaugural)## $source
## [1] "Gerhard Peters and John T. Woolley. The American Presidency Project."
##
## $notes
## [1] "http://www.presidency.ucsb.edu/inaugurals.php"More corpora are available from the quanteda.corpora package.
Extracting Features from a Corpus
In order to perform statistical analysis such as document scaling, we must extract a matrix associating values for certain features with each document. In quanteda, we use the dfm function to produce such a matrix. “dfm” is short for document-feature matrix, and always refers to documents in rows and “features” as columns. We fix this dimensional orientation because it is standard in data analysis to have a unit of analysis as a row, and features or variables pertaining to each unit as columns. We call them “features” rather than terms, because features are more general than terms: they can be defined as raw terms, stemmed terms, the parts of speech of terms, terms after stopwords have been removed, or a dictionary class to which a term belongs. Features can be entirely general, such as ngrams or syntactic dependencies, and we leave this open-ended.
Tokenizing texts
To simply tokenize a text, quanteda provides a powerful command called tokens(). This produces an intermediate object, consisting of a list of tokens in the form of character vectors, where each element of the list corresponds to an input document.
tokens() is deliberately conservative, meaning that it does not remove anything from the text unless told to do so.
txt <- c(text1 = "This is $10 in 999 different ways,\n up and down; left and right!",
text2 = "@kenbenoit working: on #quanteda 2day\t4ever, http://textasdata.com?page=123.")
tokens(txt)## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "10" "in" "999"
## [7] "different" "ways" "," "up" "and" "down"
## [13] ";" "left" "and" "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "123" "."tokens(txt, remove_numbers = TRUE, remove_punct = TRUE)## tokens from 2 documents.
## text1 :
## [1] "This" "is" "in" "different" "ways" "up"
## [7] "and" "down" "left" "and" "right"
##
## text2 :
## [1] "@kenbenoit" "working" "on" "#quanteda"
## [5] "2day" "4ever" "http" "textasdata.com"
## [9] "page"tokens(txt, remove_numbers = FALSE, remove_punct = TRUE)## tokens from 2 documents.
## text1 :
## [1] "This" "is" "10" "in" "999" "different"
## [7] "ways" "up" "and" "down" "left" "and"
## [13] "right"
##
## text2 :
## [1] "@kenbenoit" "working" "on" "#quanteda"
## [5] "2day" "4ever" "http" "textasdata.com"
## [9] "page" "123"tokens(txt, remove_numbers = TRUE, remove_punct = FALSE)## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "in" "different" "ways"
## [7] "," "up" "and" "down" ";" "left"
## [13] "and" "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "."tokens(txt, remove_numbers = FALSE, remove_punct = FALSE)## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "10" "in" "999"
## [7] "different" "ways" "," "up" "and" "down"
## [13] ";" "left" "and" "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "123" "."tokens(txt, remove_numbers = FALSE, remove_punct = FALSE, remove_separators = FALSE)## tokens from 2 documents.
## text1 :
## [1] "This" " " "is" " " "$" "10"
## [7] " " "in" " " "999" " " "different"
## [13] " " "ways" "," "\n" " " "up"
## [19] " " "and" " " "down" ";" " "
## [25] "left" " " "and" " " "right" "!"
##
## text2 :
## [1] "@kenbenoit" " " "working" ":"
## [5] " " "on" " " "#quanteda"
## [9] " " "2day" "\t" "4ever"
## [13] "," " " "http" ":"
## [17] "/" "/" "textasdata.com" "?"
## [21] "page" "=" "123" "."We also have the option to tokenize characters:
tokens("Great website: http://textasdata.com?page=123.", what = "character")## tokens from 1 document.
## text1 :
## [1] "G" "r" "e" "a" "t" "w" "e" "b" "s" "i" "t" "e" ":" "h" "t" "t" "p" ":" "/"
## [20] "/" "t" "e" "x" "t" "a" "s" "d" "a" "t" "a" "." "c" "o" "m" "?" "p" "a" "g"
## [39] "e" "=" "1" "2" "3" "."tokens("Great website: http://textasdata.com?page=123.", what = "character",
remove_separators = FALSE)## tokens from 1 document.
## text1 :
## [1] "G" "r" "e" "a" "t" " " "w" "e" "b" "s" "i" "t" "e" ":" " " "h" "t" "t" "p"
## [20] ":" "/" "/" "t" "e" "x" "t" "a" "s" "d" "a" "t" "a" "." "c" "o" "m" "?" "p"
## [39] "a" "g" "e" "=" "1" "2" "3" "."and sentences:
# sentence level
tokens(c("Kurt Vongeut said; only assholes use semi-colons.",
"Today is Thursday in Canberra: It is yesterday in London.",
"En el caso de que no puedas ir con ellos, ¿quieres ir con nosotros?"),
what = "sentence")## tokens from 3 documents.
## text1 :
## [1] "Kurt Vongeut said; only assholes use semi-colons."
##
## text2 :
## [1] "Today is Thursday in Canberra: It is yesterday in London."
##
## text3 :
## [1] "En el caso de que no puedas ir con ellos, ¿quieres ir con nosotros?"Constructing a document-feature matrix
Tokenizing texts is an intermediate option, and most users will want to skip straight to constructing a document-feature matrix. For this, we have a Swiss-army knife function, called dfm(), which performs tokenization and tabulates the extracted features into a matrix of documents by features. Unlike the conservative approach taken by tokens(), the dfm() function applies certain options by default, such as tolower() – a separate function for lower-casing texts – and removes punctuation. All of the options to tokens() can be passed to dfm(), however.
my_corpus <- corpus_subset(data_corpus_inaugural, Year > 1990)
# make a dfm
my_dfm <- dfm(my_corpus)
my_dfm[, 1:5]## Document-feature matrix of: 7 documents, 5 features (0.0% sparse).
## 7 x 5 sparse Matrix of class "dfm"
## features
## docs my fellow citizens , today
## 1993-Clinton 7 5 2 139 10
## 1997-Clinton 6 7 7 131 5
## 2001-Bush 3 1 9 110 2
## 2005-Bush 2 3 6 120 3
## 2009-Obama 2 1 1 130 6
## 2013-Obama 3 3 6 99 4
## 2017-Trump 1 1 4 96 4Other options for a dfm() include removing stopwords, and stemming the tokens.
# make a dfm, removing stopwords and applying stemming
myStemMat <- dfm(my_corpus, remove = stopwords("english"), stem = TRUE, remove_punct = TRUE)
myStemMat[, 1:5]## Document-feature matrix of: 7 documents, 5 features (17.1% sparse).
## 7 x 5 sparse Matrix of class "dfm"
## features
## docs fellow citizen today celebr mysteri
## 1993-Clinton 5 2 10 4 1
## 1997-Clinton 7 8 6 1 0
## 2001-Bush 1 10 2 0 0
## 2005-Bush 3 7 3 2 0
## 2009-Obama 1 1 6 2 0
## 2013-Obama 3 8 6 1 0
## 2017-Trump 1 4 5 3 1The option remove provides a list of tokens to be ignored. Most users will supply a list of pre-defined “stop words”, defined for numerous languages, accessed through the stopwords() function:
head(stopwords("english"), 20)## [1] "i" "me" "my" "myself" "we"
## [6] "our" "ours" "ourselves" "you" "your"
## [11] "yours" "yourself" "yourselves" "he" "him"
## [16] "his" "himself" "she" "her" "hers"head(stopwords("russian"), 10)## [1] "и" "в" "во" "не" "что" "он" "на" "я" "с" "со"head(stopwords("arabic"), 10)## Warning: 'stopwords(language = "ar")' is deprecated.
## Use 'stopwords(language = "ar", source = "misc")' instead.
## See help("Deprecated")## [1] "فى" "في" "كل" "لم" "لن" "له" "من" "هو" "هي" "قوة"Viewing the document-feature matrix
The dfm can be inspected in the Enviroment pane in RStudio, or by calling R’s View function. Calling plot on a dfm will display a wordcloud.
my_dfm <- dfm(data_char_ukimmig2010, remove = stopwords("english"), remove_punct = TRUE)
my_dfm## Document-feature matrix of: 9 documents, 1,547 features (83.8% sparse).To access a list of the most frequently occurring features, we can use topfeatures():
topfeatures(my_dfm, 20) # 20 top words## immigration british people asylum britain uk
## 66 37 35 29 28 27
## system population country new immigrants ensure
## 27 21 20 19 17 17
## shall citizenship social national bnp illegal
## 17 16 14 14 13 13
## work percent
## 13 12Plotting a word cloud is done using textplot_wordcloud(), for a dfm class object. This function passes arguments through to wordcloud() from the wordcloud package, and can prettify the plot using the same arguments:
set.seed(100)
textplot_wordcloud(my_dfm, min_count = 6, random_order = FALSE,
rotation = .25,
colors = RColorBrewer::brewer.pal(8,"Dark2"))