Package AhoCorasickTrie
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1 Type Package Package AhoCorasickTrie July 29, 2016 Title Fast Searching for Multiple Keywords in Multiple Texts Version Description Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported. License Apache License 2.0 URL BugReports Encoding UTF-8 LazyData true Imports Rcpp (>= ) LinkingTo Rcpp Suggests Biostrings, microbenchmark SystemRequirements C++11 RoxygenNote NeedsCompilation yes Author Matt Chambers [aut, cre], Tomas Petricek [aut, cph], Vanderbilt University [cph] 1
2 2 AhoCorasickSearch Maintainer Matt Chambers Repository CRAN Date/Publication :40:52 R topics documented: AhoCorasickSearch AhoCorasickSearchList AhoCorasickTrie Index 6 AhoCorasickSearch Fast searching for one or more keywords in one or more texts Description Usage Builds an Aho-Corasick trie from one or more keywords and uses it to search one or more texts. For a large number of keywords, Aho-Corasick is much faster than a naive approach (such as lapply(keywords, gregexpr, text)). Use AhoCorasickSearchList instead of AhoCorasickSearch when you want to keep the matches of each input text separate. If the input texts have names, the resulting list of matches will include those names and non-matched texts will be excluded from the results. If the input texts do not have names, then the resulting list of matches will be in the same order as the input texts, and nonmatched texts will be kept to preserve that order. Thus, it is more efficient to use named input texts (so non-matched texts can be dropped). The default alphabet allows all 128 ASCII characters in the keywords and the texts. Characters outside this range will cause an error. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. Set the alphabet parameter if a reduced alphabet is appropriate. UTF-8 (Unicode) matching is not currently supported. AhoCorasickSearch(keywords, text, alphabet = "ascii", groupbykeyword = FALSE) Arguments keywords Character vector of one or more keywords text Character vector of one or more texts to search alphabet Alphabet to use; one of ascii, aminoacid, or nucleicacid groupbykeyword If true, matches are grouped by keyword (instead of by text)
3 AhoCorasickSearch 3 Value See Also List of matches, grouped by either text or by keyword Examples Aho-Corasick string matching in C# for the article this package is based on matchpdict and match_ac for more memory efficient, but DNA-only, implementations of the algorithm. listequals = function(a, b) { is.null(unlist(a)) && is.null(unlist(b))!is.null(a) &&!is.null(b) && all(unlist(a) == unlist(b)) } # 1. Search for multiple keywords in a single text keywords = c("abra", "cadabra", "is", "the", "Magic", "Word") onesearch = AhoCorasickSearch(keywords, "Is Abracadabra the Magic Word?") stopifnot(listequals(onesearch[[1]][[1]], list(keyword="abra", offset=4))) stopifnot(listequals(onesearch[[1]][[2]], list(keyword="cadabra", offset=8))) stopifnot(listequals(onesearch[[1]][[3]], list(keyword="the", offset=16))) stopifnot(listequals(onesearch[[1]][[4]], list(keyword="magic", offset=20))) stopifnot(listequals(onesearch[[1]][[5]], list(keyword="word", offset=26))) # 2. Search multiple named texts in a named list with keyword grouping and aminoacid alphabet # * all matches to a keyword are accessed by name # * non-matched keywords are dropped proteins = c(protein1="peptidepeptidedadadararararakekekekepeptide", protein2="derpaderpapewpewpeepeerawrawwarragtagpeptidekesequence") peptides = c("peptide", "DERPA", "SEQUENCE", "KEKE", "PEPPIE") peptidesearch = AhoCorasickSearch(peptides, proteins, alphabet="aminoacid", groupbykeyword=true) stopifnot(listequals(peptidesearch$peptide, list(list(keyword="protein1", offset=1), list(keyword="protein1", offset=8), list(keyword="protein1", offset=37), list(keyword="protein2", offset=38)))) stopifnot(listequals(peptidesearch$derpa, list(list(keyword="protein2", offset=1), list(keyword="protein2", offset=6)))) stopifnot(listequals(peptidesearch$sequence, list(list(keyword="protein2", offset=47)))) stopifnot(listequals(peptidesearch$keke, list(list(keyword="protein1", offset=29), list(keyword="protein1", offset=31), list(keyword="protein1", offset=33)))) stopifnot(listequals(peptidesearch$peppie, NULL)) # 3. Grouping by keyword without text names: offsets are given without reference to the text names(proteins) = NULL peptidesearch = AhoCorasickSearch(peptides, proteins, groupbykeyword=true) stopifnot(listequals(peptidesearch$peptide, list(1, 8, 37, 38))) stopifnot(listequals(peptidesearch$derpa, list(1, 6))) stopifnot(listequals(peptidesearch$sequence, list(47))) stopifnot(listequals(peptidesearch$keke, list(29, 31, 33)))
4 4 AhoCorasickSearchList AhoCorasickSearchList Fast searching for one or more keywords in a list of texts Description Usage Builds an Aho-Corasick trie from one or more keywords and uses it to search a list of one or more texts. For a large number of keywords, Aho-Corasick is much faster than a naive approach (such as lapply(keywords, gregexpr, text)). Use AhoCorasickSearchList instead of AhoCorasickSearch when you want to keep the matches of each input sublist separate. If the sublists of the input list have names, the resulting list of lists will use those names, but sublists with no matches will still be in the resulting list. If the texts of the sublists have names, the resulting sublists of matches will use those names, and the texts with no matches will be dropped. If the input texts do not have names, then the resulting sublists of matches will be in the same order as the input texts, and non-matched texts will be kept to preserve that order. Thus, it is more efficient to use named input texts (so non-matched texts can be dropped). The default alphabet allows all 128 ASCII characters in the keywords and the texts. Characters outside this range will cause an error. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. Set the alphabet parameter if a reduced alphabet is appropriate. UTF-8 (Unicode) matching is not currently supported. AhoCorasickSearchList(keywords, textlist, alphabet = "ascii", groupbykeyword = FALSE) Arguments Value keywords textlist alphabet Character vector of one or more keywords List of lists, each sublist with one or more texts to search Alphabet to use; one of ascii, aminoacid, or nucleicacid groupbykeyword If true, matches are grouped by keyword (instead of by text) List of lists of matches, grouped by either text or by keyword (each list of texts gets its own list of matches) See Also Aho-Corasick string matching in C# for the article this package is based on matchpdict and match_ac for more memory efficient, but DNA-only, implementations of the algorithm.
5 AhoCorasickTrie 5 Examples listequals = function(a, b) { is.null(unlist(a)) && is.null(unlist(b))!is.null(a) &&!is.null(b) && all(unlist(a) == unlist(b)) } keywords = c("abra", "cadabra", "is", "the", "Magic", "Word") # 1. Search a list of lists without names # * sublists are accessed by index # * texts are accessed by index # * non-matched texts are kept (input index order is preserved) listsearch = AhoCorasickSearchList(keywords, list(c("what in", "the world"), c("is"), "secret about", "the Magic Word?")) stopifnot(listequals(listsearch[[1]][[1]], list())) stopifnot(listequals(listsearch[[1]][[2]][[1]], list(keyword="the", offset=1))) stopifnot(listequals(listsearch[[2]][[1]][[1]], list(keyword="is", offset=1))) stopifnot(listequals(listsearch[[3]], list())) stopifnot(listequals(listsearch[[4]][[1]][[1]], list(keyword="the", offset=1))) stopifnot(listequals(listsearch[[4]][[1]][[2]], list(keyword="magic", offset=5))) stopifnot(listequals(listsearch[[4]][[1]][[3]], list(keyword="word", offset=11))) # 2. Search a named list of named lists # * sublists are accessed by name # * matched texts are accessed by name # * non-matched texts are dropped namedsearch = AhoCorasickSearchList(keywords, list(subject=c(phrase1="what in", phrase2="the world"), verb=c(phrase1="is"), predicate1=c(phrase1="secret about"), predicate2=c(phrase1="the Magic Word?"))) stopifnot(listequals(namedsearch$subject$phrase2[[1]], list(keyword="the", offset=1))) stopifnot(listequals(namedsearch$verb$phrase1[[1]], list(keyword="is", offset=1))) stopifnot(listequals(namedsearch$predicate1, list())) stopifnot(listequals(namedsearch$predicate2$phrase1[[1]], list(keyword="the", offset=1))) stopifnot(listequals(namedsearch$predicate2$phrase1[[2]], list(keyword="magic", offset=5))) stopifnot(listequals(namedsearch$predicate2$phrase1[[3]], list(keyword="word", offset=11))) AhoCorasickTrie AhoCorasickTrie: fast searching for multiple keywords in multiple texts Description AhoCorasickTrie: fast searching for multiple keywords in multiple texts
6 Index AhoCorasickSearch, 2, 2, 4 AhoCorasickSearchList, 2, 4, 4 AhoCorasickTrie, 5 AhoCorasickTrie-package (AhoCorasickTrie), 5 match_ac, 3, 4 matchpdict, 3, 4 6
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Type Package Version 0.2.2 Package githubinstall February 18, 2018 Title A Helpful Way to Install R Packages Hosted on GitHub Provides an helpful way to install packages hosted on GitHub. URL https://github.com/hoxo-m/githubinstall
Package pinyin. October 17, 2018
Version 1.1.4 Date 2018-10-14 Package pinyin October 17, 2018 Title Convert Chinese Characters into Pinyin, Sijiao, Wubi or Other Codes Maintainer Peng Zhao Depends R (>= 3.1.0) Imports
Package docxtools. July 6, 2018
Title Tools for R Markdown to Docx Documents Version 0.2.0 Language en-us Package docxtools July 6, 2018 A set of helper functions for using R Markdown to create documents in docx format, especially documents
Package rcba. May 27, 2018
Package rcba May 27, 2018 Title CBA Classifier for R Version 0.4.2 Author Jaroslav Kuchar [aut, cre], Tomas Kliegr [ctb] Maintainer Jaroslav Kuchar URL https://github.com/jaroslav-kuchar/rcba
Package RPresto. July 13, 2017
Title DBI Connector to Presto Version 1.3.0 Copyright Facebook, Inc. 2015-present. Package RPresto July 13, 2017 Implements a 'DBI' compliant interface to Presto. Presto is an open source distributed SQL
Package SimilaR. June 21, 2018
Version 1.0.2 Date 2018-06-21 Title R Source Code Similarity Evaluation Package SimilaR June 21, 2018 Description An Implementation of a novel method to determine similarity of R functions based on program
Package tidyimpute. March 5, 2018
Title Imputation the Tidyverse Way Version 0.1.0 Date 2018-02-01 Package tidyimpute March 5, 2018 URL https://github.com/decisionpatterns/tidyimpute Functions and methods for imputing missing values (NA)
Package tm.plugin.factiva
Package tm.plugin.factiva February 15, 2013 Type Package Title Import articles from Factiva using the tm text mining framework Version 1.2 Date 2013-01-28 Enhances tm (>= 0.5) Imports tm (>= 0.5), XML
Package haven. April 9, 2015
Version 0.2.0 Title Import SPSS, Stata and SAS Files Package haven April 9, 2015 Import foreign statistical formats into R via the embedded ReadStat C library (https://github.com/wizardmac/readstat). Package
Package reticulate. November 24, 2017
Type Package Title R Interface to Python Version 1.3.1 Package reticulate November 24, 2017 R interface to Python modules, classes, and functions. When calling into Python R data types are automatically
Package gggenes. R topics documented: November 7, Title Draw Gene Arrow Maps in 'ggplot2' Version 0.3.2
Title Draw Gene Arrow Maps in 'ggplot2' Version 0.3.2 Package gggenes November 7, 2018 Provides a 'ggplot2' geom and helper functions for drawing gene arrow maps. Depends R (>= 3.3.0) Imports grid (>=
Package wikitaxa. December 21, 2017
Title Taxonomic Information from 'Wikipedia' Package wikitaxa December 21, 2017 'Taxonomic' information from 'Wikipedia', 'Wikicommons', 'Wikispecies', and 'Wikidata'. Functions included for getting taxonomic
Package geoops. March 19, 2018
Type Package Package geoops March 19, 2018 Title 'GeoJSON' Topology Calculations and Operations Tools for doing calculations and manipulations on 'GeoJSON', a 'geospatial' data interchange format ().
Package wdman. January 23, 2017
Type Package Title 'Webdriver'/'Selenium' Binary Manager Version 0.2.2 Package wdman January 23, 2017 There are a number of binary files associated with the 'Webdriver'/'Selenium' project (see ,
Package catchr. R topics documented: December 30, Type Package. Title Catch, Collect, and Raise Conditions. Version 0.1.0
Package catchr December 30, 2018 Type Package Title Catch, Collect, and Raise Conditions Version 0.1.0 Maintainer Zachary Burchill Simple code to catch, collect, and raise