See http://docs.quanteda.io for additional tutorials, examples, and general documentation.
The “grammar” of the package is split between three basic types of functions and data objects:
object: a constructor function named
object() that returns an object of class object. Example:
corpus() constructs a
corpus class object.
_verb: a function that inputs an object of class object, and returns a a modified object class object. There are no exceptions to this naming rule, so that even functions that operate on character objects following this convention, such as
char_tolower(). (Ok, so there is a slight exception: we abbreviated
_descriptor: data objects are named this way to clearly distinguish them and to make them easy to identify in the index. The first part identifies them as data, the second names their object class, and the third component is a descriptor. Example:
data_corpus_inaugural is the quanteda
corpus() class object consisting of the US presidents’ inaugural addresses.
_specific: functions that input a quanteda object and return the result of an analysis, as a new type of object. Only the underscored functions that begin with
text break the previous rule about the first part of the name identifying the object class that is input and output. Examples:
textstat_readability() takes a character or corpus as input, and returns a data.frame;
textplot_xray() takes a
kwic object as input, and generates a dispersion plot (named “x-ray” because of its similarity to the plot produced by Kindle).
Extensions of R functions: These are commonly used R functions, such as
head(), that are also defined for quanteda objects. Examples:
head.dfm(), coercion functions such as
as.list.tokens, and Boolean class type checking functions such as
is.dfm(). Many post-estimation methods defined for
lm objects, for instance
predict(), are also defined for most
R-like functions. These are functions for quanteda objects that follow naming conventions and functionality that should be very familiar to users of R. Example:
ndoc() returns the number of documents in a corpus, tokens, or dfm object, similar to
base::nrow(). Note that like
ndoc() is not plural. Other examples include
featnames() – similar to
Grammatical exceptions: Every language has these, usually due to path dependency from historical development, and quanteda is no exception. The list, however, is short:
The quanteda package consists of a few core data types, created by calling constructors with identical names. These are all “nouns” in the sense of declaring what they construct. This follows very similar R behaviour in many of the core R objects, such as
Core object types and their constructor functions:
Note that a core object class in quanteda is also the
character atomic type, for which there is no constructor function, and is abbreviated as
char in the function nomenclature.
All functions that begin with the name of a core object class will both input and output an object of this class, without exception.
This replaces the approach in versions up to 0.9.8.5 where a general method such as
selectFeatures() was defined for each applicable class of core object. This approach made the specific function behaviour unpredictable from the description of the general behaviour. It also made it difficult to get an overview of the functionality available for each object class. By renaming these functions following the convention of object class, followed by an underscore, followed by a verb (or verb-like statement), we could both separate the behaviours into specific functions, as well as clearly describe through the function name what action is taken on what type of object.
In our view, the advantages of this clarity outweigh whatever advantages might be found from overloading a generic function. The functions
dfm_sample(), for instance, are clearer to understand and read from a package’s function index, than the previously overloaded version of
sample() that could be dispatched on a corpus, tokenized text, or dfm object. Additionally, in the case of
sample(), we avoid the namespace “conflict” caused by redefining the function as a generic, so that it could be overloaded. Our new, more specific naming conventions therefore reduce the likelihood of namespace conflicts with other packages.
Many simple base R functions – simpler at least than the example of
sample() cited above – are still extended to quanteda objects through overloading. The logic of allowing is that these functions, e.g.
cbind() for a dfm, are very simple and very common, and therefore are well-known to users. Furthermore, they can operate in only one fashion on the object for which they are defined, such as
cbind() combining two dfm objects by joining columns. Similar functions extended in this way include
t(). Most of these functions are so natural that their documentation is not included in the package index.
Additional functions have been defined for quanteda objects that are very similar to simple base R functions, but are not named using the
class_action format because they do not return a modified object of the same class. These follow as closely as possible the naming conventions found in the base R functions that are similar. For instance,
featnames() return the document names of various quanteda objects, in the same way that
rownames() does for matrix-like objects (a matrix, data.frame, data.table, etc.). The abbreviation of
featnames() is intentionally modeled on
ndoc() returns the number of documents, using the singular form similar to
quanteda is designed both to facilitate and to enforce a “best-practice” workflow. This includes the following basic principles.
Corpus texts should remain unchanged during subsequent analysis and processing. In other words, after loading and encoding, we should discourage users from modifying a corpus of texts as a form of processing, so that the corpus can act as a library and record of the original texts, prior to any downstream processing. This not only aids in replication, but also means that a corpus presents the unmodified texts to which any processing, feature selection, transformations, or sampling may be applied or reapplied, without hard-coding any changes made as part of the process of analyzing the texts. The only exception is to reshape the units of text in a corpus, but we will record the details of this reshaping to make it relatively easy to reverse unit changes. Since the definition of a “document” is part of the process of loading texts into a corpus, however, rather than processing, we will take a less stringent line on this aspect of changing a corpus.
A corpus should be capable of holding additional objects that will be associated with the corpus, such as dictionaries, stopword, and phrase lists. These will be named objects, that can be invoked when using (for instance)
dfm(). This allows a corpus to contain all of the additional objects that would normally be associated with it, rather than requiring a set of separate, extra-corpus objects.
Objects should record histories of the operations applied to them. This is for purposes of analytic transparency. A tokens object and a dfm object, for instance, should have settings that record the processing options applied to the texts or corpus from which they were created. These provide a record of what was done to the text, and where it came from. Examples are
dfm_wordstem(), and settings such as
remove_twitter. They also include any objects used in feature selection, such as dictionaries or stopword lists.
A dfm should always be a documents (or document groups) in rows by features in columns. A
dfm object may be transposed but then it is no longer a
dfm class object.
Encoding of texts should always be UTF-8.
Creating the corpus
Reading files, probably using
readtext() from the readtext package, then creating a corpus using
corpus(), making sure the texts have a common encoding, and adding document variables (
docvars()) and metadata (
Defining and delimiting documents
Defining and delimiting textual featuresThis step involves defining and extracting the relevant features from each document, using
tokens(), the main function for this step, involves identifying instances of defined features (“tokens”) and extracting them as vectors. Usually these will consist of words, but may also consist of:
ngrams: adjacent sequences of words, not separated by punctuation marks or sentence boundaries; including
tokens_compound(), for selected word ngrams as identified in selected lists rather than simply using all adjacent word pairs or n-sequences.
By defining the broad class of tokens we wish to extract, in this step we also apply rules that will keep or ignore elements such as punctuation or digits, or special aggregations of word and other characters that make up URLs, Twitter tags, or currency-prefixed digits. This will involve adding the following options to
tokens() returns a new object class of tokenized texts, a hashed list of index types, with each element in the list corresponding to a document, and each hash vector representing the tokens in that document.
tokens() extracts word tokens, and only
TRUE, meaning that
tokens() will return a list including punctuation as tokens. This follows a philosophy of minimal intervention, and one requiring that additional decisions be made explicit by the user when invoking
For converting to lowercase, it is actually faster to perform this step before tokenization, but logically it falls under the next workflow step. However for efficiency,
*_tolower() functions are defined for
Since the tokenizer we will use may not distinguish the punctuation characters used in constructs such as URLs, email addresses, Twitter handles, or digits prefixed by currency symbols, we will mostly need to use a substitution strategy to replace these with alternative characters prior to tokenization, and then replace the substitutions with the original characters. This will slow down processing but will only be active by explicit user request for this type of handling to take place.
Note that that defining and delimiting features may also include their parts of speech, meaning we will need to add functionality for POS tagging and extraction in this step.
Further feature selectionOnce features have been identified and separated from the texts in the tokenization step, features may be removed from token lists, or handled as part of
dfmconstruction. Features may be:
dfm(x, remove = )or
dfm(x, select = )or
dfm(x, dictionary = )or as a supplement to uncollapsed features through
dfm(x, thesaurus = )
dfm_tolower()to consider as equivalent the same word features despite having different cases, by converting all features to lower case
It will be sometimes possible to perform these steps separately from the
dfm creation stage, but in most cases these steps will be performed as options to the
Analysis of the documents and features
In most cases, users will use the default settings to create a dfm straight from a corpus.
dfm() will combine steps 3–4, even though basic functions will be available to perform these separately. All options shown in steps 3–4 will be available in
dfm objects can always be built up using constituent steps, through tokenizing and then selecting on the tokens. quanteda integrates the magrittr
%>% pipe operator, to make it easy to chain functions without creating intermediate objects:
mydfm <- texts(mycorpus, group = "party") %>% char_tolower() %>% tokens() %>% tokens_wordstem() %>% tokens_remove(stopwords("english")) %>% dfm()
We recognize however that not all sequences will make sense, for instance
tokens_wordstem() will only work after tokenization, and will try to catch these errors and make the proper sequence clear to users.