title: “Lab 5–Text Analysis”" author: "" date: “September 21, 2020” output: html_document — This is a continuation of the last lab. Please copy your code from question 2 of lab 4 here: (so in particular, you should be reading in all of Jane Austen’s works, and ending with an R-object called austen.words, which is a vector of all words in Jane Austen’s works. )
COPY HERE.
setwd("C:/Users/ethan/Desktop")
AustenLines<-readLines("Austen.txt")
linLens <- nchar(AustenLines)
length(which(linLens == 0))## [1] 12601NewAustenLines <- AustenLines[which(linLens != 0)]
length(AustenLines)## [1] 80478length(NewAustenLines)+length(which(linLens == 0))## [1] 80478Austen.all <- paste(NewAustenLines, collapse = " ")
Austen.words <- strsplit(Austen.all, " ")
#Austen.words[[1]]- In the last lab, you created a vector of words “austen.words”. It consisted of all words written in Jane Austen’s complete works–each entry of the vector is a single word in one of Jane Austen’s texts. The problem is that the data set is not very “clean”.
In this lab we wll do a bit of “data cleaning”, followed by a little bit of word analysis.
To get us going, we need to see just how flawed the data is currently. Look at the first 500 words in austen.words:
Austen.words[[1]][1:500]## [1] "Project" "Gutenberg's" "The"
## [4] "Complete" "Works" "of"
## [7] "Jane" "Austen," "by"
## [10] "Jane" "Austen" "This"
## [13] "eBook" "is" "for"
## [16] "the" "use" "of"
## [19] "anyone" "anywhere" "at"
## [22] "no" "cost" "and"
## [25] "with" "almost" "no"
## [28] "restrictions" "whatsoever." ""
## [31] "You" "may" "copy"
## [34] "it," "give" "it"
## [37] "away" "or" "re-use"
## [40] "it" "under" "the"
## [43] "terms" "of" "the"
## [46] "Project" "Gutenberg" "License"
## [49] "included" "with" "this"
## [52] "eBook" "or" "online"
## [55] "at" "www.gutenberg.org" "Title:"
## [58] "The" "Complete" "Project"
## [61] "Gutenberg" "Works" "of"
## [64] "Jane" "Austen" "Author:"
## [67] "Jane" "Austen" "Editor:"
## [70] "David" "Widger" "Release"
## [73] "Date:" "January" "25,"
## [76] "2010" "[EBook" "#31100]"
## [79] "Language:" "English" "Character"
## [82] "set" "encoding:" "ASCII"
## [85] "***" "START" "OF"
## [88] "THIS" "PROJECT" "GUTENBERG"
## [91] "EBOOK" "THE" "WORKS"
## [94] "OF" "JANE" "AUSTEN"
## [97] "***" "Produced" "by"
## [100] "many" "Project" "Gutenberg"
## [103] "volunteers." "THE" "WORKS"
## [106] "OF" "JANE" "AUSTEN"
## [109] "Edited" "by" "David"
## [112] "Widger" "Project" "Gutenberg"
## [115] "Editions" "" ""
## [118] "" "" ""
## [121] "" "" ""
## [124] "" "" ""
## [127] "" "" "DEDICATION"
## [130] "" "" ""
## [133] "" "" "This"
## [136] "Jane" "Austen" "collection"
## [139] "" "" ""
## [142] "" "" ""
## [145] "" "" ""
## [148] "is" "dedicated" "to"
## [151] "" "" ""
## [154] "" "" "Alice"
## [157] "Goodson" "[Hart]" "Woodby"
## [160] "[Note:" "The" "accompanying"
## [163] "HTML" "file" "has"
## [166] "active" "links" "to"
## [169] "all" "the" "volumes"
## [172] "and" "chapters" "in"
## [175] "this" "set.]" "CONTENTS:"
## [178] " " "" "PERSUASION"
## [181] "" "" ""
## [184] "NORTHANGER" "ABBEY" " "
## [187] "" "MANSFIELD" "PARK"
## [190] " " "" "EMMA"
## [193] " " "" "LADY"
## [196] "SUSAN" " " ""
## [199] "LOVE" "AND" "FREINDSHIP"
## [202] "AND" "OTHER" "EARLY"
## [205] "WORKS" "" ""
## [208] "" "PRIDE" "AND"
## [211] "PREJUDICE" "" ""
## [214] "" "SENSE" "AND"
## [217] "SENSIBILITY" "PERSUASION" "by"
## [220] "Jane" "Austen" "(1818)"
## [223] "Chapter" "1" "Sir"
## [226] "Walter" "Elliot," "of"
## [229] "Kellynch" "Hall," "in"
## [232] "Somersetshire," "was" "a"
## [235] "man" "who," "for"
## [238] "his" "own" "amusement,"
## [241] "never" "took" "up"
## [244] "any" "book" "but"
## [247] "the" "Baronetage;" "there"
## [250] "he" "found" "occupation"
## [253] "for" "an" "idle"
## [256] "hour," "and" "consolation"
## [259] "in" "a" "distressed"
## [262] "one;" "there" "his"
## [265] "faculties" "were" "roused"
## [268] "into" "admiration" "and"
## [271] "respect," "by" "contemplating"
## [274] "the" "limited" "remnant"
## [277] "of" "the" "earliest"
## [280] "patents;" "there" "any"
## [283] "unwelcome" "sensations," "arising"
## [286] "from" "domestic" "affairs"
## [289] "changed" "naturally" "into"
## [292] "pity" "and" "contempt"
## [295] "as" "he" "turned"
## [298] "over" "the" "almost"
## [301] "endless" "creations" "of"
## [304] "the" "last" "century;"
## [307] "and" "there," "if"
## [310] "every" "other" "leaf"
## [313] "were" "powerless," "he"
## [316] "could" "read" "his"
## [319] "own" "history" "with"
## [322] "an" "interest" "which"
## [325] "never" "failed." ""
## [328] "This" "was" "the"
## [331] "page" "at" "which"
## [334] "the" "favourite" "volume"
## [337] "always" "opened:" ""
## [340] "" "" ""
## [343] "" "" ""
## [346] "" "" ""
## [349] "" "\"ELLIOT" "OF"
## [352] "KELLYNCH" "HALL." "\"Walter"
## [355] "Elliot," "born" "March"
## [358] "1," "1760," "married,"
## [361] "July" "15," "1784,"
## [364] "Elizabeth," "daughter" "of"
## [367] "James" "Stevenson," "Esq."
## [370] "of" "South" "Park,"
## [373] "in" "the" "county"
## [376] "of" "Gloucester," "by"
## [379] "which" "lady" "(who"
## [382] "died" "1800)" "he"
## [385] "has" "issue" "Elizabeth,"
## [388] "born" "June" "1,"
## [391] "1785;" "Anne," "born"
## [394] "August" "9," "1787;"
## [397] "a" "still-born" "son,"
## [400] "November" "5," "1789;"
## [403] "Mary," "born" "November"
## [406] "20," "1791.\"" "Precisely"
## [409] "such" "had" "the"
## [412] "paragraph" "originally" "stood"
## [415] "from" "the" "printer's"
## [418] "hands;" "but" "Sir"
## [421] "Walter" "had" "improved"
## [424] "it" "by" "adding,"
## [427] "for" "the" "information"
## [430] "of" "himself" "and"
## [433] "his" "family," "these"
## [436] "words," "after" "the"
## [439] "date" "of" "Mary's"
## [442] "birth--" "\"Married," "December"
## [445] "16," "1810," "Charles,"
## [448] "son" "and" "heir"
## [451] "of" "Charles" "Musgrove,"
## [454] "Esq." "of" "Uppercross,"
## [457] "in" "the" "county"
## [460] "of" "Somerset,\"" "and"
## [463] "by" "inserting" "most"
## [466] "accurately" "the" "day"
## [469] "of" "the" "month"
## [472] "on" "which" "he"
## [475] "had" "lost" "his"
## [478] "wife." "Then" "followed"
## [481] "the" "history" "and"
## [484] "rise" "of" "the"
## [487] "ancient" "and" "respectable"
## [490] "family," "in" "the"
## [493] "usual" "terms;" "how"
## [496] "it" "had" "been"
## [499] "first" "settled"Notice there are digits, empty characters, and punctuation that are causing problems. The easiest way to deal with this is to use strsplit (as we did on the last lab) to split on punctuation as well as blanks. Execute the following line to do so:
Austen.words<-strsplit(Austen.all, split="[[:space:]]|[[:punct:]]")[[1]]
head(Austen.words)## [1] "Project" "Gutenberg" "s" "The" "Complete" "Works"In class we used the gsub (global substitution)on vectors. The following example gives a way to remove digits from a vector and replace them with blanks characters. Execute it to see what it does.
v<-c("7", "help78", "6help77")
gsub("[[:digit:]]", "", v)## [1] "" "help" "help"- Do something similar to remove all digits in austen.words. Make sure you overwrite the current austen.words with this command, so that all the digits are removed for good.
Austen.words <- gsub("[[:digit:]]", "", Austen.words)- If you look at the results, you should see that there are no more numbers in the text, but you’ll see there are still empty words. Find a way to remove them, and check that it worked.
Austen.words <- Austen.words[which(Austen.words != "")]- As a last bit of data cleaning, we should not distinguish between, for instance, “Because” and “because”. Look up how to convert strings in R to all lowercase, and do this to the current austen.words.
Austen.words <- tolower(Austen.words)Your final austen.words should have no punctuation, uppercase words, digits, or blank spaces. It may not be perfect for word analysis, but it is better!
- Using the “unique” function, find all of the unique words in austen.words, and store them in austen.words.unique. How many unique words are there? Compare this to how many words there are in austen.words.
Austen.words.unique <- unique(Austen.words)
length(Austen.words.unique)## [1] 14804length(Austen.words)## [1] 792471- Get a histogram of the number of characters in austen.words.unique. What do you notice? (It is easiest to use the basic hist command rather than ggplot in this case.)
hist(nchar(Austen.words.unique))
Remember, the sort() function sorts a vector into increasing order. A close relative is the order() function, which returns the indices that put the vector into increasing order. See the code for an example.
v<-c(10, 1, 1000, 100, 1)
order(v)## [1] 2 5 1 4 3x<-order(v)
v[x]## [1] 1 1 10 100 1000sort(x)## [1] 1 2 3 4 5- As you can see, using the order function can do the same thing as the sort function. Use this idea to put austen.words.unique into increasing order in terms of the size of the words. Look at the first 50 and last 50 elements of the sorted vector. What are your observations?
word.order <- order(nchar(Austen.words.unique))
Austen.words.unique <- Austen.words.unique[word.order]
Austen.words.unique[1:50]## [1] "s" "a" "z" "i" "o" "t" "c" "m" "d" "w" "n" "e" "f" "v" "b"
## [16] "x" "h" "g" "r" "l" "k" "p" "u" "of" "by" "is" "at" "no" "it" "or"
## [31] "re" "to" "in" "up" "he" "an" "as" "if" "on" "ii" "be" "do" "so" "mr" "we"
## [46] "go" "am" "me" "my" "us"tail(Austen.words.unique, 50)## [1] "acknowledgments" "uninterruptedly" "procrastinating"
## [4] "unexceptionably" "inconsideration" "inadmissibility"
## [7] "capitalisations" "brighthelmstone" "notwithstanding"
## [10] "conscientiously" "superintendence" "uncompanionable"
## [13] "condescendingly" "characteristics" "inquisitiveness"
## [16] "proportionately" "connoisseurship" "enfranchisement"
## [19] "unobtrusiveness" "merchantibility" "unreasonableness"
## [22] "incomprehensible" "misconstructions" "acknowledgements"
## [25] "susceptibilities" "superciliousness" "disproportionate"
## [28] "incomprehensibly" "indiscriminating" "unseasonableness"
## [31] "misunderstanding" "undistinguishing" "prognostications"
## [34] "northamptonshire" "indiscriminately" "predetermination"
## [37] "unsuspiciousness" "disingenuousness" "disagreeableness"
## [40] "discontentedness" "unenforceability" "disinterestedness"
## [43] "misrepresentation" "unaccountableness" "companionableness"
## [46] "unceremoniousness" "misunderstandings" "communicativeness"
## [49] "disqualifications" "incomprehensibility"print("there are lots of letters that are probably holdovers from cleaning and not actual words in the head.")## [1] "there are lots of letters that are probably holdovers from cleaning and not actual words in the head."- Let’s create a word cloud of word frequencies. To keep things manageable, first select only words with 5 characters or more from austen.words and store it in a vector called austen.words.long.
Austen.words.long <- Austen.words[which(nchar(Austen.words) >=5)]- Now create a frequency table from austen.words.long using the table command (save it as austen.words.table):
Austen.words.table <- table(Austen.words.long)Make austen.words.table into a dataframe with the following command (you need it in this format to make the wordcloud):
Austen.df<-as.data.frame(Austen.words.table)- View austen.df and see that it contains what you think it should. What is the most common word that has at least 5 characters? What are some of the least common words?
Austen.df[which(Austen.df[,2] == max(Austen.df[,2])),]## Austen.words.long Freq
## 2692 could 3770print("could")## [1] "could"Austen.df[which(Austen.df[,2] == min(Austen.df[,2])),]## Austen.words.long Freq
## 5 abating 1
## 7 abbeyland 1
## 9 abbots 1
## 10 abbott 1
## 11 abbreviation 1
## 12 abdication 1
## 19 abiding 1
## 22 abjectly 1
## 23 abjuring 1
## 24 ablest 1
## 25 ablution 1
## 27 abolish 1
## 28 abolishing 1
## 29 abolition 1
## 33 abominates 1
## 34 abound 1
## 35 abounded 1
## 36 abounding 1
## 39 abridge 1
## 40 abridgement 1
## 41 abridger 1
## 46 abscond 1
## 48 absences 1
## 54 absorbed 1
## 55 absorbing 1
## 57 abstained 1
## 58 abstinence 1
## 59 abstract 1
## 60 abstracted 1
## 62 abstruse 1
## 74 abydos 1
## 75 abyss 1
## 76 acacia 1
## 77 academy 1
## 80 acceding 1
## 81 accelerate 1
## 86 acceptably 1
## 90 accepts 1
## 92 accessed 1
## 95 accessions 1
## 100 accidents 1
## 102 accommodated 1
## 106 accomodate 1
## 109 accompaniment 1
## 110 accompaniments 1
## 123 accords 1
## 131 accrued 1
## 133 accumulations 1
## 134 accuracy 1
## 141 accusing 1
## 142 accustom 1
## 147 achieve 1
## 149 achievement 1
## 150 achievements 1
## 151 achieving 1
## 156 acknowledgements 1
## 171 acquiring 1
## 174 acquits 1
## 179 acrimony 1
## 181 acrostic 1
## 190 actress 1
## 198 acutest 1
## 199 adair 1
## 202 adapting 1
## 215 adept 1
## 217 adequately 1
## 219 adherence 1
## 223 adjoined 1
## 225 adjourned 1
## 235 admiralty 1
## 246 admits 1
## 250 admonishing 1
## 254 adopting 1
## 255 adoption 1
## 259 adores 1
## 261 adorn 1
## 274 adventuring 1
## 275 adventurous 1
## 276 adversary 1
## 278 advertise 1
## 282 advertising 1
## 288 advisers 1
## 289 advises 1
## 292 advocates 1
## 310 affirmed 1
## 311 affix 1
## 331 afresh 1
## 339 againsts 1
## 341 agathas 1
## 343 aggrandise 1
## 344 aggrandizement 1
## 345 aggravated 1
## 347 aggregate 1
## 348 aggression 1
## 350 agincourt 1
## 368 agricola 1
## 369 agricultural 1
## 370 agriculture 1
## 371 ahead 1
## 372 ailed 1
## 375 aimable 1
## 380 aisles 1
## 382 aladdin 1
## 388 albion 1
## 389 alcove 1
## 390 alcoves 1
## 391 alderneys 1
## 392 alehouses 1
## 395 alfred 1
## 396 algebra 1
## 399 alienable 1
## 400 alienate 1
## 402 alienations 1
## 407 allay 1
## 409 alledged 1
## 411 allegretto 1
## 415 alleviate 1
## 416 alleviated 1
## 419 alley 1
## 424 alliterative 1
## 425 allot 1
## 426 allotted 1
## 429 allowably 1
## 434 allows 1
## 436 alloyed 1
## 439 alludes 1
## 444 almane 1
## 446 almshouses 1
## 452 alphabetically 1
## 459 altercation 1
## 466 alternatives 1
## 471 amateur 1
## 475 amazes 1
## 478 amber 1
## 479 ambiguous 1
## 483 amenable 1
## 484 amended 1
## 491 amicable 1
## 502 amplifying 1
## 512 ancestry 1
## 513 anchor 1
## 514 anchorage 1
## 517 andersons 1
## 524 angles 1
## 530 animals 1
## 535 animosities 1
## 536 anjou 1
## 543 anniversary 1
## 546 announces 1
## 549 annoyed 1
## 553 annulling 1
## 555 anonymous 1
## 561 answerit 1
## 564 antechamber 1
## 565 anthology 1
## 566 anthony 1
## 567 antichambers 1
## 573 antidote 1
## 575 antique 1
## 582 anxiousness 1
## 585 anymore 1
## 589 anywise 1
## 594 apeice 1
## 595 apiece 1
## 601 apologized 1
## 602 apologizing 1
## 604 apoplectic 1
## 605 apoplexy 1
## 606 apostrophe 1
## 608 appalled 1
## 610 apparatus 1
## 614 apparents 1
## 617 appealing 1
## 618 appeals 1
## 628 appendages 1
## 629 appertain 1
## 630 appertaining 1
## 632 appetites 1
## 634 applauding 1
## 635 applause 1
## 639 applicant 1
## 640 applicants 1
## 644 applies 1
## 649 appointments 1
## 651 appreciated 1
## 652 appreciating 1
## 653 appreciation 1
## 661 apprised 1
## 663 approachable 1
## 670 appropriating 1
## 671 appropriation 1
## 675 approver 1
## 680 apron 1
## 684 arbiters 1
## 689 arched 1
## 696 archway 1
## 697 arcturus 1
## 701 ardours 1
## 706 argumentative 1
## 712 arises 1
## 714 aristocratic 1
## 715 armadas 1
## 716 armed 1
## 717 arming 1
## 721 aroused 1
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## 736 arrives 1
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## 740 arrow 1
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## 750 artifices 1
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## 850 atrocities 1
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## 887 attorneys 1
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## 900 audacity 1
## 904 auditor 1
## 905 auditors 1
## 906 aught 1
## 908 augmentation 1
## 910 augmenting 1
## 912 augured 1
## 918 auspicious 1
## 919 auspiciously 1
## 922 austin 1
## 923 authentic 1
## 924 authenticity 1
## 926 authoress 1
## 935 autumnal 1
## 936 auxiliary 1
## 945 averse 1
## 951 avocations 1
## 961 awaiting 1
## 965 awakening 1
## 966 awaking 1
## 969 awfulness 1
## 970 awhile 1
## 972 awkwardest 1
## 978 azure 1
## 979 babel 1
## 988 badge 1
## 989 badly 1
## 991 bahama 1
## 993 baited 1
## 994 baits 1
## 998 bakewell 1
## 999 baking 1
## 1001 balanced 1
## 1005 balmy 1
## 1006 banbury 1
## 1008 bandied 1
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## 1010 banging 1
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## 1038 baronetcies 1
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## 1092 beautified 1
## 1093 beautifier 1
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## 1128 beggar 1
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## 1179 benefactress 1
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## 1199 bermuda 1
## 1201 bernards 1
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## 1218 betimes 1
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## 1235 bewitches 1
## 1240 bickerings 1
## 1243 biddy 1
## 1244 bigger 1
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## 1250 binary 1
## 1251 binding 1
## 1255 biography 1
## 1259 birthday 1
## 1260 birthright 1
## 1263 bitch 1
## 1264 biting 1
## 1270 blacken 1
## 1273 blacksmith 1
## 1274 blains 1
## 1283 blamelessness 1
## 1285 blanche 1
## 1287 blankets 1
## 1288 blanks 1
## 1292 bleak 1
## 1293 blemish 1
## 1295 blenheim 1
## 1298 blessedness 1
## 1302 blighted 1
## 1303 blights 1
## 1306 blinder 1
## 1308 blindly 1
## 1312 blister 1
## 1313 blocked 1
## 1315 blockheads 1
## 1320 blossom 1
## 1322 blots 1
## 1323 blotted 1
## 1324 blotting 1
## 1328 blowsy 1
## 1329 blues 1
## 1334 blunted 1
## 1335 bluntly 1
## 1336 bluntness 1
## 1341 bluster 1
## 1345 boards 1
## 1348 boastful 1
## 1350 boasts 1
## 1351 boatmen 1
## 1353 boatswain 1
## 1354 boded 1
## 1358 boiling 1
## 1359 boils 1
## 1360 boisterous 1
## 1361 boisterously 1
## 1365 bolts 1
## 1367 bonmots 1
## 1369 bonnets 1
## 1370 bonomi 1
## 1371 bookbinder 1
## 1373 bookish 1
## 1376 booksellers 1
## 1377 booted 1
## 1382 bores 1
## 1388 bosoms 1
## 1389 bosworth 1
## 1390 bother 1
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## 1394 bough 1
## 1396 boulanger 1
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## 1412 brabourne 1
## 1414 bracelets 1
## 1417 bragging 1
## 1418 braids 1
## 1420 brained 1
## 1422 braithwaites 1
## 1424 branched 1
## 1426 brand 1
## 1428 branxholm 1
## 1429 bravado 1
## 1432 bravery 1
## 1433 braving 1
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## 1436 brawn 1
## 1439 breadth 1
## 1443 breakfasting 1
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## 1451 breathings 1
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## 1459 brethren 1
## 1460 brevity 1
## 1461 brewing 1
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## 1465 bribing 1
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## 1468 bricklayer 1
## 1471 bridesmaids 1
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## 1498 briskness 1
## 1502 brittle 1
## 1506 broadly 1
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## 13446 zigzags 1print("abdicate, abating, abbots, etc")## [1] "abdicate, abating, abbots, etc"Create a wordcloud with the following command:
#install.packages("ggplot2")
#install.packages("wordcloud")
library("ggplot2")## Warning: package 'ggplot2' was built under R version 4.0.3library("wordcloud")## Warning: package 'wordcloud' was built under R version 4.0.3## Loading required package: RColorBrewerwordcloud(words=Austen.df$Austen.words.long, freq=Austen.df$Freq, max.words = 100, colors=rainbow(10) )