quanteda package - RDocumentation

Learn R Programming

⚠️There's a newer version (4.1.0) of this package.Take me there.

About

An R package for managing and analyzing text, created by Kenneth Benoit. Supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS.

For more details, see https://quanteda.io.

How to Install

The normal way from CRAN, using your R GUI or

install.packages("quanteda")

Or for the latest development version:

# devtools package required to install quanteda from Github 
devtools::install_github("quanteda/quanteda")

Because this compiles some C++ and Fortran source code, you will need to have installed the appropriate compilers.

If you are using a Windows platform, this means you will need also to install the Rtools software available from CRAN.

If you are using macOS, you should install the macOS tools, namely the Clang 6.x compiler and the GNU Fortran compiler (as quanteda requires gfortran to build). If you are still getting errors related to gfortran, follow the fixes here.

How to Use

See the quick start guide to learn how to use quanteda.

How to cite

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. (2018) “quanteda: An R package for the quantitative analysis of textual data”. Journal of Open Source Software. 3(30), 774. https://doi.org/10.21105/joss.00774.

For a BibTeX entry, use the output from citation(package = "quanteda").

Leaving Feedback

If you like quanteda, please consider leaving feedback or a testimonial here.

Contributing

Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

Fork the source code, modify, and issue a pull request through the project GitHub page. See our Contributor Code of Conduct and the all-important quanteda Style Guide.
Issues, bug reports, and wish lists: File a GitHub issue.
Usage questions: Submit a question on the quanteda channel on StackOverflow.
Contact the maintainer by email.

Copy Link

Version

Install

install.packages('quanteda')

Monthly Downloads

23,966

Version

2.0.1

License

GPL-3

Maintainer

Last Published

March 18th, 2020

Functions in quanteda (2.0.1)

Extract a subset of a corpus

Coercion and checking functions for fcm objects

Internal data sets

Function to assign multiple slots to a S4 object

Convert the case of character objects

Combine dfm objects by Rows or Columns

Coercion, checking, and combining functions for tokens objects

Bootstrap a dfm

Recast the document units of a corpus

Compute the Mean Segmental Type-Token Ratio (MSTTR)

Function extending base::attributes()

Segment texts on a pattern match

Convert quanteda objects to non-quanteda formats

Compute the Moving-Average Type-Token Ratio (MATTR)

Construct a corpus object

Remove sentences based on their token lengths or a pattern match

Base method extensions for corpus objects

Formerly included data objects

compute_lexdiv_stats

Compute lexical diversity from a dfm or tokens

Replace features in dfm

Randomly sample documents from a corpus

Check if font is available on the system

Convert quanteda dictionary objects to the YAML format

data_dfm_lbgexample

dfm from data in Table 1 of Laver, Benoit, and Garry (2003)

Create a document-feature matrix

data_char_sampletext

A paragraph of text for testing various text-based functions

convert same-value pairs to NA in a textstat_proxy object

Convert a dfm to an lsa "textmatrix"

dictionary2-class

dictionary class objects and functions

data_char_ukimmig2010

Immigration-related sections of 2010 UK party manifestos

convert-wrappers

Convenience wrappers for dfm convert

Return the first or last part of a corpus

Randomly sample documents or features from a dfm

Convert the case of the features of a dfm and combine

Weight a dfm by tf-idf

Get or set document names

Get or set document-level meta-data

Virtual class "dfm" for a document-feature matrix

Return the first or last part of a dfm

corpus_trimsentences

Remove sentences based on their token lengths or a pattern match

Apply a dictionary to a dfm

Simpler and faster version of expand.grid() in base package

Internal function for select_types() to escape regular expressions

Combine documents in a dfm by a grouping variable

Extract a subset of a dfm

Get or set document-level variables

flatten_dictionary

Flatten a hierarchical dictionary into a list of character vectors

data_corpus_inaugural

US presidential inaugural address texts

Match the feature set of a dfm to given feature names

Convert regex and glob patterns to type IDs or fixed patterns

Internal functions for dfm objects

dfm_split_hyphenated_features

Split a dfm's hyphenated features into constituent parts

Sort a dfm by frequency of one or more margins

Create a feature co-occurrence matrix

Create a dictionary

Select features from a dfm or fcm

Sort an fcm in alphabetical order of the features

Internal function to extract docvars

Virtual class "fcm" for a feature co-occurrence matrix

Compute the (weighted) document frequency of a feature

data_dictionary_LSD2015

Lexicoder Sentiment Dictionary (2015)

format_sparsity

format a sparsity value for printing

Segment tokens object by patterns

head.textstat_proxy

Return the first or last part of a textstat_proxy object

friendly_class_undefined_message

Print friendly object class not defined message

Compute the frequencies of features

Count the Scrabble letter values of text

Count the number of sentences

generate_groups

Generate a grouping vector from docvars

Grouping variable(s) for various functions

Internal function for select_types() to check if a string is a regular expression

Internal function for select_types to search the index using fastmatch.

Check if a glob pattern is indexed by index_types

Converts a Matrix to a dfm

Recombine a dfm or fcm by combining identical dimension elements

Get or set object metadata

Replace tokens in a tokens object

Trim a dfm using frequency threshold-based feature selection

lowercase_dictionary_values

Internal function to lowercase dictionary values

Count the number of documents or features

Converts a Matrix to a fcm

get_object_version

Get the package version that created an object

Get the feature labels from a dfm

Internal function to get, set or initialize system metadata

Check if patterns contains glob wildcard

Compute keyness (internal functions)

merge_dictionary_values

Internal function to merge values of duplicated keys

Weight the feature frequencies in a dfm

object-builders

Object compilers

textplot_network

Plot a network of feature co-occurrences

Internal functions to create a list for the meta attribute

Pattern for feature, token and keyword matching

Shortcut functions to access or assign metadata

read_dict_functions

Internal functions to import dictionary files

Count syllables in a text

Print methods for quanteda core objects

Extensions for and from spacy_parse objects

Objects exported from other packages

nest_dictionary

Utility function to generate a nested list

Select types without performing slow regex search

Declare a compound character to be a sequence of separate pattern matches

Special handling for names of quanteda objects

Compute the sparsity of a document-feature matrix

Sample a vector by a group

Return an error message

Print a phrase object

set_fcm_slots<-

Set values to a fcm's S4 slots

Count the number of tokens or types

Get or assign corpus texts

Plot the dispersion of key word(s)

Convert various input as pattern to a vector used in tokens_select, tokens_compound and kwic.

summarize_texts

Summary statistics on a character vector

textstat_select

Select rows of textstat objects by glob, regex or fixed patterns

Similarity and distance computation between documents or features

Models for scaling and classification of textual data

set_dfm_dimnames<-

Internal functions to set dimnames

replace_dictionary_values

Internal function to replace dictionary values

Extract a subset of a tokens

Get word types from a tokens object

remove_empty_keys

Utility function to remove empty keys

textplot_keyness

Plot word keyness

textstat_lexdiv

Calculate lexical diversity

summary_metadata

Functions to add or retrieve corpus summary metadata

serialize_tokens

Function to serialize list-of-character tokens

textstat_proxy-class

textstat_simil/dist classes

textplot_wordcloud

Plot features as a wordcloud

set_dfm_slots<-

Set values to a dfm's S4 slots

Split tokens by a separator pattern

Recombine documents tokens by groups

Summarize a corpus

textstat_keyness

Calculate keyness statistics

Internal function for special handling of multi-word dictionary values

Locate keywords-in-context

Create ngrams and skipgrams from tokens

textstat_frequency

Tabulate feature frequencies

unlist_character

Unlist a list of character vectors safely

wordcloud_comparison

Internal function for textplot_wordcloud

tokenize_internal

quanteda tokenizers

textstat_entropy

Compute entropies of documents or features

Apply a dictionary to a tokens object

Construct a tokens object

quanteda-package

An R package for the quantitative analysis of textual data

tokens_compound

Convert token sequences into compound tokens

Segment tokens object by chunks of a given size

quanteda_options

Get or set package options for quanteda

tokens_recompile

recompile a serialized tokens object

Convert the case of tokens

Select or remove tokens from a tokens object

list2dictionary

Internal function to convert a list to a dictionary

Pattern matching using valuetype

tokens_wordstem

Stem the terms in an object

Internal function for textplot_wordcloud

[Experimental] Change direction of words in tokens

Identify the most frequent features in a dfm

textstat_collocations

Identify and score multi-word expressions

summary.character

summary.character method to override the network::summary.character()

[Experimental] Compute document/feature proximity

textstat_readability

Calculate readability

Randomly sample documents from a tokens object

Unlist a list of integer vectors safely

Raise warning of unused dots

Coercion and checking functions for dictionary objects

View methods for quanteda

Coercion and checking functions for dfm objects

Coerce a dfm to a matrix or data.frame

Convert an fcm to an igraph object

redefinition of network::as.network()

as.data.frame.dfm

Convert a dfm to a data.frame

coerce a compressed corpus to a standard corpus

as.matrix,textstat_simil_sparse-method

as.matrix method for textstat_simil_sparse