textmining.R
dell
Sun Jan 10 19:11:51 2016
#CITATION Rdatamining.com
getwd()## [1] "C:/Users/dell/Desktop/Teaching"rm(list=ls())
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 293848 7.9 592000 15.9 350000 9.4
## Vcells 324251 2.5 786432 6.0 677529 5.2sessionInfo()## R version 3.2.2 (2015-08-14)
## Platform: i386-w64-mingw32/i386 (32-bit)
## Running under: Windows 7 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] magrittr_1.5 tools_3.2.2 htmltools_0.3 stringi_1.0-1 rmarkdown_0.7
## [6] knitr_1.10.5 stringr_1.0.0 digest_0.6.8 evaluate_0.7memory.limit()## [1] 1535library(tm)## Loading required package: NLPlibrary(wordcloud)## Loading required package: RColorBrewergetSources()## [1] "DataframeSource" "DirSource" "URISource" "VectorSource"
## [5] "XMLSource" "ZipSource"getReaders()## [1] "readDOC" "readPDF"
## [3] "readPlain" "readRCV1"
## [5] "readRCV1asPlain" "readReut21578XML"
## [7] "readReut21578XMLasPlain" "readTabular"
## [9] "readTagged" "readXML"txt2="C:/Users/dell/Desktop/test"
#?Corpus
# Corpora are collections of documents
# containing (natural language) text.
# In packages which employ the infrastructure
# provided by package tm,
# such corpora are represented via the
# virtual S3 class Corpus:
b=Corpus(DirSource(txt2),
readerControl = list(language = "eng"))
b## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 1inspect(b)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 1
##
## [[1]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 24
## Content: chars: 17
## Content: chars: 0
## Content: chars: 19
## Content: chars: 0
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## Content: chars: 0
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## Content: chars: 202
## Content: chars: 0
## Content: chars: 508
## Content: chars: 0
## Content: chars: 68b <- tm_map(b, tolower)
# tm_map
# Interface to apply transformation functions
# (also denoted as mappings) to corpora.
# remove punctuation
b <- tm_map(b, removePunctuation)
# remove numbers
b <- tm_map(b, removeNumbers)
# remove stopwords
myStopwords <- c(stopwords('english'), "can")
b <- tm_map(b, removeWords, myStopwords)
b <- tm_map(b, stemDocument)
#run and running and ran
b <- tm_map(b, PlainTextDocument)
myDtm <- TermDocumentMatrix(b, control = list(minWordLength = 1))
inspect(myDtm)## <<TermDocumentMatrix (terms: 810, documents: 1)>>
## Non-/sparse entries: 810/0
## Sparsity : 0%
## Maximal term length: 16
## Weighting : term frequency (tf)
##
## Docs
## Terms character(0)
## Â’ll 1
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## young 1findFreqTerms(myDtm, lowfreq=10)## [1] "nation" "new" "will"findAssocs(myDtm, 'nation', 0.1)## $nation
## numeric(0)m <- as.matrix(myDtm)
v <- sort(rowSums(m), decreasing=TRUE)
v## will nation new america
## 18 12 11 8
## every must less let
## 8 8 7 7
## common now people time
## 6 6 6 6
## today work day generation
## 6 6 5 5
## know spirit world come
## 5 5 5 4
## crisis end greater long
## 4 4 4 4
## meet men power seek
## 4 4 4 4
## things whether women words
## 4 4 4 4
## across american carried earth
## 3 3 3 3
## economy father force forward
## 3 3 3 3
## freedom future god hard
## 3 3 3 3
## history hope ideals jobs
## 3 3 3 3
## journey just last man
## 3 3 3 3
## many may moment nations
## 3 3 3 3
## oath old peace prosperity
## 3 3 3 3
## question rather say shall
## 3 3 3 3
## small upon afford age
## 3 3 2 2
## also ambitions americans americas
## 2 2 2 2
## answer back begin better
## 2 2 2 2
## big birth bless blood
## 2 2 2 2
## brave build calls care
## 2 2 2 2
## challenges charter child children
## 2 2 2 2
## confidence conflict cooperation country
## 2 2 2 2
## courage defense done duties
## 2 2 2 2
## enduring era even extend
## 2 2 2 2
## face faced fail faith
## 2 2 2 2
## false far fear feed
## 2 2 2 2
## find forth founding free
## 2 2 2 2
## friend generations gift good
## 2 2 2 2
## government greatness grows health
## 2 2 2 2
## hours icy knowledge liberty
## 2 2 2 2
## life light like lines
## 2 2 2 2
## longer lower make market
## 2 2 2 2
## meaning met might minds
## 2 2 2 2
## mutual never nothing often
## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
## stand still storms stronger
## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 2
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## 2 2 2 1
## abandoned ability accept account
## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
## bold borders born borne
## 1 1 1 1
## break bridges brings broken
## 1 1 1 1
## bush business businesses came
## 1 1 1 1
## campfires capacity capital capitals
## 1 1 1 1
## cares carry cars cause
## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
## choose chosen christians citizen
## 1 1 1 1
## citizenship city civil clean
## 1 1 1 1
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## 1 1 1 1
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## 1 1 1 1
## consequence consider consume consumed
## 1 1 1 1
## continue control convictions corruption
## 1 1 1 1
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## 1 1 1 1
## creed culture curiosity currents
## 1 1 1 1
## cut cynics danger dangers
## 1 1 1 1
## dark darkest data deceit
## 1 1 1 1
## decent decides decisions decline
## 1 1 1 1
## defeat define defining delivered
## 1 1 1 1
## demand demanded demands depended
## 1 1 1 1
## depends depth deserts deserve
## 1 1 1 1
## destini destroy determination died
## 1 1 1 1
## differences difficult digital dignified
## 1 1 1 1
## dignity discord dissent dissolve
## 1 1 1 1
## distant docu doers dogmas
## 1 1 1 1
## dollars domestic doubt drafted
## 1 1 1 1
## drawn dust dying earlier
## 1 1 1 1
## earned easily effect effort
## 1 1 1 1
## electric emanates embody emerged
## 1 1 1 1
## endure endured enemy energy
## 1 1 1 1
## enjoy entitle equal everywhere
## 1 1 1 1
## evidence example expand expanded
## 1 1 1 1
## expediences eye eyes fact
## 1 1 1 1
## factories failure fainthearted fair
## 1 1 1 1
## faithful fallen falter fame
## 1 1 1 1
## families farms faroff farreaching
## 1 1 1 1
## fascism fathers favors fellow
## 1 1 1 1
## filled finally firefighters firm
## 1 1 1 1
## fist fixed flourish flow
## 1 1 1 1
## foes forbearers forge forgotten
## 1 1 1 1
## former fortyfour fought foundation
## 1 1 1 1
## friends fuel full gather
## 1 1 1 1
## gathering gener generate generosity
## 1 1 1 1
## gettysburg give given giving
## 1 1 1 1
## gladly globe godgiven gods
## 1 1 1 1
## goods govern governments grace
## 1 1 1 1
## grandest grateful gratitude great
## 1 1 1 1
## greed grids grievances gross
## 1 1 1 1
## ground growth grudgingly guardians
## 1 1 1 1
## guided habits hand hands
## 1 1 1 1
## happi hardearned hardship harness
## 1 1 1 1
## hatred hatreds heart held
## 1 1 1 1
## help helps heritage heroes
## 1 1 1 1
## high hindus homes honesty
## 1 1 1 1
## honor horizon hour huddled
## 1 1 1 1
## humanity humble humbled humility
## 1 1 1 1
## hungry idea ill ills
## 1 1 1 1
## imagination imagine inaugural indicators
## 1 1 1 1
## indifference individual inducing inevitable
## 1 1 1 1
## inhabit innocents instead instruments
## 1 1 1 1
## intend interest interests inventive
## 1 1 1 1
## iraq irresponsibility january jews
## 1 1 1 1
## job join joined judge
## 1 1 1 1
## justness keepers khe kindness
## 1 1 1 1
## knew labor land language
## 1 1 1 1
## lash law lay lead
## 1 1 1 1
## leaders leave legacy leisure
## 1 1 1 1
## lessen levees lie lif
## 1 1 1 1
## live local look lose
## 1 1 1 1
## lost loyalty magnificent makers
## 1 1 1 1
## mall manage mark measurable
## 1 1 1 1
## measure memories midst mindful
## 1 1 1 1
## missiles moments month months
## 1 1 1 1
## mountains move much muslim
## 1 1 1 1
## muslims nagging narrow necessity
## 1 1 1 1
## needed network next noble
## 1 1 1 1
## nonbelievers normandy nourish nuclear
## 1 1 1 1
## nurture obama obscure oceans
## 1 1 1 1
## office opportunity ordered outcome
## 1 1 1 1
## outlast outside packed parents
## 1 1 1 1
## pass pat patchwork patriotism
## 1 1 1 1
## patriots patrol peac peopl
## 1 1 1 1
## peoples perils petty pick
## 1 1 1 1
## place places plans please
## 1 1 1 1
## pleasures pledge plenty plowed
## 1 1 1 1
## polit political poor possessions
## 1 1 1 1
## powerful precious precisely prefer
## 1 1 1 1
## prepare president presidential price
## 1 1 1 1
## principles proclaim product productive
## 1 1 1 1
## profound programs progress promises
## 1 1 1 1
## prosper protect protecting prudent
## 1 1 1 1
## publics pursue putting qualities
## 1 1 1 1
## quality quiet race raging
## 1 1 1 1
## raise raw reach read
## 1 1 1 1
## ready reaffirm reaffirming real
## 1 1 1 1
## recall recognition recriminations reform
## 1 1 1 1
## refused regard reject relative
## 1 1 1 1
## relies remained remains remaking
## 1 1 1 1
## remembrance reminded required resources
## 1 1 1 1
## respect responsibility responsibly restaurant
## 1 1 1 1
## restraint retirement return reveal
## 1 1 1 1
## revolution riches rightful rights
## 1 1 1 1
## rising risktakers river road
## 1 1 1 1
## roads role roll route
## 1 1 1 1
## rugged rule run sacred
## 1 1 1 1
## sacrificed sacrifices safely safety
## 1 1 1 1
## sahn said sake sapping
## 1 1 1 1
## satisfying saw scale scarcely
## 1 1 1 1
## science scripture search security
## 1 1 1 1
## see seeks sees segregation
## 1 1 1 1
## seize selflessness serious served
## 1 1 1 1
## services set settled settling
## 1 1 1 1
## shape shaped shed shifted
## 1 1 1 1
## shores shortcuts shown shuttered
## 1 1 1 1
## side sight silencing simply
## 1 1 1 1
## sixty size skill slaughtering
## 1 1 1 1
## smaller smoke snow societys
## 1 1 1 1
## soil someday soon source
## 1 1 1 1
## sow span specter spend
## 1 1 1 1
## spin spoken stained stairway
## 1 1 1 1
## stale standing starting starved
## 1 1 1 1
## state states statistics storm
## 1 1 1 1
## stranger strangled strength strengthen
## 1 1 1 1
## struggled sturdy subject suffering
## 1 1 1 1
## suggest sum sun surely
## 1 1 1 1
## surest survivethat sweatshops swift
## 1 1 1 1
## swill system tanks tasted
## 1 1 1 1
## technologys tell tempering terror
## 1 1 1 1
## tested threat threaten threats
## 1 1 1 1
## tides till timeless tirelessly
## 1 1 1 1
## together toiled told tolerance
## 1 1 1 1
## tolerate towards transform transit
## 1 1 1 1
## tribe truths turn ultimately
## 1 1 1 1
## uncertain unclench understanding undiminished
## 1 1 1 1
## unfolds unity universities unmatched
## 1 1 1 1
## unpleasant ushering values village
## 1 1 1 1
## violence vision vital wage
## 1 1 1 1
## warming watchful watching waver
## 1 1 1 1
## ways weakened weakness week
## 1 1 1 1
## whip whisper whose wield
## 1 1 1 1
## winds wisely woman wonders
## 1 1 1 1
## worked works worldly worlds
## 1 1 1 1
## worldthat worn wrong years
## 1 1 1 1
## yes young
## 1 1myNames <- names(v)
d <- data.frame(word=myNames, freq=v)
wordcloud(d$word, d$freq, min.freq=3,col=brewer.pal(5,"Spectral"))