Trump Tweet Word Clouds
Mining Twitter Data
Is rather easy. You have to arrange a developer account with Twitter and set up an app. After that, Twitter gives you access to a consumer key and secret and an access token and access secret. My tool of choice for this is rtweet because it automagically processes tweet elements and makes them easy to slice and dice. I also played with twitteR but it was harder to work with for what I wanted. The first section involves setting up a token for `rtweet.
# Change the next four lines based on your own consumer_key, consume_secret, access_token, and access_secret.
token <- create_token(
app = "MyAppName",
consumer_key <- "CK",
consumer_secret <- "CS",
access_token <- "AT",
access_secret <- "AS")Now I want to collect some tweets from a particular user’s timeline and look into them. For this example, I will use @realDonaldTrump.
Who does Trump tweet about?
A cool post on sentiment analysis can be found here. The first step is to grab his timeline. rtweet makes this quite easy. I will grab it and then save it in the code below so that I do not spam the API. I will get at the time series characteristics of his tweets and the sentiment stuff in a further analysis. For now, let me just show some wordclouds.
tml.djt <- get_timeline("realDonaldTrump", n = 3200)
save(tml.djt, file="../data/TMLS.RData")I start by loading the tmls object that I created above. What does it look like?
library(wordcloud2)
library(tidyverse)
library(tidytext)
library(rtweet)
load(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/TMLS.RData"))
names(tml.djt)## [1] "user_id" "status_id"
## [3] "created_at" "screen_name"
## [5] "text" "source"
## [7] "display_text_width" "reply_to_status_id"
## [9] "reply_to_user_id" "reply_to_screen_name"
## [11] "is_quote" "is_retweet"
## [13] "favorite_count" "retweet_count"
## [15] "hashtags" "symbols"
## [17] "urls_url" "urls_t.co"
## [19] "urls_expanded_url" "media_url"
## [21] "media_t.co" "media_expanded_url"
## [23] "media_type" "ext_media_url"
## [25] "ext_media_t.co" "ext_media_expanded_url"
## [27] "ext_media_type" "mentions_user_id"
## [29] "mentions_screen_name" "lang"
## [31] "quoted_status_id" "quoted_text"
## [33] "quoted_created_at" "quoted_source"
## [35] "quoted_favorite_count" "quoted_retweet_count"
## [37] "quoted_user_id" "quoted_screen_name"
## [39] "quoted_name" "quoted_followers_count"
## [41] "quoted_friends_count" "quoted_statuses_count"
## [43] "quoted_location" "quoted_description"
## [45] "quoted_verified" "retweet_status_id"
## [47] "retweet_text" "retweet_created_at"
## [49] "retweet_source" "retweet_favorite_count"
## [51] "retweet_retweet_count" "retweet_user_id"
## [53] "retweet_screen_name" "retweet_name"
## [55] "retweet_followers_count" "retweet_friends_count"
## [57] "retweet_statuses_count" "retweet_location"
## [59] "retweet_description" "retweet_verified"
## [61] "place_url" "place_name"
## [63] "place_full_name" "place_type"
## [65] "country" "country_code"
## [67] "geo_coords" "coords_coords"
## [69] "bbox_coords" "status_url"
## [71] "name" "location"
## [73] "description" "url"
## [75] "protected" "followers_count"
## [77] "friends_count" "listed_count"
## [79] "statuses_count" "favourites_count"
## [81] "account_created_at" "verified"
## [83] "profile_url" "profile_expanded_url"
## [85] "account_lang" "profile_banner_url"
## [87] "profile_background_url" "profile_image_url"I want to first get rid of retweets to render President Trump in his own voice.
DJTDF <- tml.djt %>% filter(is_retweet==FALSE)With just his tweets, a few things can be easily accomplished. Who does he mention?
library(wordcloud)## Loading required package: RColorBrewerMNTDJT <- DJTDF %>% filter(!is.na(mentions_screen_name)) %>% select(mentions_screen_name)
Ments <- as.character(unlist(MNTDJT))
TMents <- data.frame(table(Ments))
pal <- brewer.pal(8,"Spectral")
wordcloud(TMents$Ments,TMents$Freq, colors=pal)
That’s interesting. But that is twitter accounts. That is far less interesting that his actual text. I want to look at words and bigrams for this segment.
What does Trump tweet about?
Some more stuff from stack overflow. There is quite a bit of code in here. I simply wrote a function that takes an input character string and cleans it up. Uncomment the various components and pipe them. The sequencing is important and I found this to get everything that I wanted.
library(RColorBrewer)
TDF <- DJTDF %>% select(text)
# TDF contains the text of tweets.
library(stringr)
tweet_cleaner <- function(text) {
temp1 <- str_replace_all(text, "&", "") %>%
str_replace_all(., "https://t+", "") %>%
str_replace_all(.,"@[a-z,A-Z]*","")
# str_replace_all(., "[[:punct:]]", "")
# str_replace_all(., "[[:digit:]]", "") %>%
# str_replace_all(., "[ \t]{2,}", "") %>%
# str_replace_all(., "^\\s+|\\s+$", "") %>%
# str_replace_all(., " "," ") %>%
# str_replace_all(., "http://t.co/[a-z,A-Z,0-9]*{8}","")
# str_replace_all(.,"RT @[a-z,A-Z]*: ","") %>%
# str_replace_all(.,"#[a-z,A-Z]*","")
return(temp1)
}
clean_tweets <- data.frame(text=sapply(1:dim(TDF)[[1]], function(x) {tweet_cleaner(TDF[x,"text"])}))
clean_tweets$text <- as.character(clean_tweets$text)
Trumps.Words <- clean_tweets %>% unnest_tokens(., word, text) %>% anti_join(stop_words, "word")
TTW <- table(Trumps.Words)
TTW <- TTW[order(TTW, decreasing = T)]
TTW <- data.frame(TTW)
names(TTW) <- c("word","freq")
wordcloud(TTW$word, TTW$freq)
Well, that is kinda cool. Now, I want to do a bit more with it using more complicated word combinations.
The Wonders of tidytext
The tidytext section on n-grams is great. I will start with a tweet identifier – something I should have deployed long ago – before parsing these; I will not need this now but it will be encessary when the sentiment stuff comes around.
library(tidyr)
CT <- clean_tweets %>% mutate(tweetno= row_number())
DJT2G <- clean_tweets %>% unnest_tokens(bigram, text, token = "ngrams", n=2)
bigrams_separated <- DJT2G %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
# new bigram counts:
bigram_counts <- bigrams_filtered %>%
count(word1, word2, sort = TRUE)
bigram_counts## word1 word2 n
## 1 fake news 160
## 2 witch hunt 128
## 3 north korea 84
## 4 white house 71
## 5 news media 56
## 6 total endorsement 49
## 7 law enforcement 47
## 8 crooked hillary 43
## 9 supreme court 39
## 10 border security 38
## 11 president trump 36
## 12 nancy pelosi 33
## 13 donald trump 32
## 14 hillary clinton 32
## 15 tax cuts 32
## 16 james comey 31
## 17 prime minister 31
## 18 trump campaign 30
## 19 angry democrats 28
## 20 trade deals 28
## 21 rigged witch 27
## 22 2nd amendment 25
## 23 military vets 25
## 24 southern border 25
## 25 immigration laws 24
## 26 york times 24
## 27 american people 23
## 28 kim jong 23
## 29 illegal immigration 22
## 30 jobs jobs 22
## 31 president obama 22
## 32 european union 21
## 33 god bless 21
## 34 13 angry 20
## 35 obama administration 20
## 36 south carolina 20
## 37 jeff sessions 19
## 38 president xi 18
## 39 west virginia 18
## 40 brett kavanaugh 17
## 41 ms 13 17
## 42 peter strzok 17
## 43 republican party 17
## 44 highly respected 16
## 45 lisa page 16
## 46 russian collusion 16
## 47 stock market 16
## 48 washington post 16
## 49 chuck schumer 15
## 50 justice department 15
## 51 trade barriers 15
## 52 00 p.m 14
## 53 bob mueller 14
## 54 mainstream media 14
## 55 president donald 14
## 56 strong endorsement 14
## 57 billion dollars 13
## 58 crime borders 13
## 59 national security 13
## 60 russian witch 13
## 61 border patrol 12
## 62 borders loves 12
## 63 bruce ohr 12
## 64 chairman kim 12
## 65 fantastic job 12
## 66 rick scott 12
## 67 trade deficit 12
## 68 trump administration 12
## 69 2016 election 11
## 70 classified information 11
## 71 fair trade 11
## 72 judge brett 11
## 73 mike pompeo 11
## 74 special counsel 11
## 75 17 angry 10
## 76 american workers 10
## 77 clinton campaign 10
## 78 criminal aliens 10
## 79 election day 10
## 80 fake dossier 10
## 81 illegal immigrants 10
## 82 president putin 10
## 83 press conference 10
## 84 puerto rico 10
## 85 ron desantis 10
## 86 russian meddling 10
## 87 south korea 10
## 88 vote republican 10
## 89 american history 9
## 90 anti trump 9
## 91 brian kemp 9
## 92 democrat party 9
## 93 fbi doj 9
## 94 hurricane florence 9
## 95 intelligence committee 9
## 96 maxine waters 9
## 97 national anthem 9
## 98 robert mueller 9
## 99 saudi arabia 9
## 100 street journal 9
## 101 tax cut 9
## 102 u.s senate 9
## 103 unfair trade 9
## 104 united nations 9
## 105 wall street 9
## 106 00 a.m 8
## 107 800 billion 8
## 108 anonymous sources 8
## 109 billy graham 8
## 110 border wall 8
## 111 country illegally 8
## 112 don’t care 8
## 113 federal government 8
## 114 frame donald 8
## 115 henry mcmaster 8
## 116 ig report 8
## 117 john brennan 8
## 118 john james 8
## 119 judicial watch 8
## 120 middle east 8
## 121 news conference 8
## 122 north carolina 8
## 123 russia hoax 8
## 124 strong borders 8
## 125 trump tower 8
## 126 african american 7
## 127 agent peter 7
## 128 andrew brunson 7
## 129 border military 7
## 130 comey mccabe 7
## 131 complete endorsement 7
## 132 consumer confidence 7
## 133 cryin chuck 7
## 134 enforcement officers 7
## 135 fbi agent 7
## 136 gregg jarrett 7
## 137 happy birthday 7
## 138 house intelligence 7
## 139 iran deal 7
## 140 john kelly 7
## 141 local officials 7
## 142 lou barletta 7
## 143 merit based 7
## 144 michael cohen 7
## 145 minister abe 7
## 146 news cnn 7
## 147 oil prices 7
## 148 pastor andrew 7
## 149 paul manafort 7
## 150 red wave 7
## 151 sanctuary cities 7
## 152 trade deal 7
## 153 troy balderson 7
## 154 unemployment rate 7
## 155 washington d.c 7
## 156 world cup 7
## 157 9 00 6
## 158 alan dershowitz 6
## 159 amendment loves 6
## 160 angry dems 6
## 161 approval ratings 6
## 162 bill nelson 6
## 163 border crime 6
## 164 carter page 6
## 165 christopher steele 6
## 166 clinton foundation 6
## 167 cutting taxes 6
## 168 dan bongino 6
## 169 davos switzerland 6
## 170 doesn’t matter 6
## 171 double standard 6
## 172 gina haspel 6
## 173 governor henry 6
## 174 harley davidson 6
## 175 highly conflicted 6
## 176 jerry brown 6
## 177 john cox 6
## 178 lady melania 6
## 179 lowest level 6
## 180 massive tariffs 6
## 181 matt rosendale 6
## 182 national guard 6
## 183 people including 6
## 184 phony witch 6
## 185 republican primary 6
## 186 ronald reagan 6
## 187 secret service 6
## 188 total witch 6
## 189 trade deficits 6
## 190 vice president 6
## 191 vote vote 6
## 192 151 billion 5
## 193 7 00 5
## 194 7 00pme 5
## 195 9th circuit 5
## 196 america safe 5
## 197 american worker 5
## 198 andrew mccabe 5
## 199 approval rating 5
## 200 beautiful evening 5
## 201 billion dollar 5
## 202 broward county 5
## 203 bush family 5
## 204 campaign finance 5
## 205 catch release 5
## 206 chain migration 5
## 207 cia director 5
## 208 conflicted democrats 5
## 209 congressman keith 5
## 210 court justice 5
## 211 dishonest people 5
## 212 dnc server 5
## 213 doj fbi 5
## 214 don mcgahn 5
## 215 don’t worry 5
## 216 dossier paid 5
## 217 el salvador 5
## 218 elizabeth warren 5
## 219 entire nation 5
## 220 farm bill 5
## 221 farmers workers 5
## 222 fisa abuse 5
## 223 friend president 5
## 224 fusion gps 5
## 225 george h.w 5
## 226 governor jerry 5
## 227 h.w bush 5
## 228 highly trained 5
## 229 house chief 5
## 230 hunt continues 5
## 231 hunt headed 5
## 232 hunt hoax 5
## 233 illegal aliens 5
## 234 illicit scheme 5
## 235 immigration bill 5
## 236 incredible job 5
## 237 intellectual property 5
## 238 it’s time 5
## 239 jon tester 5
## 240 leaked classified 5
## 241 level playing 5
## 242 manufacturing jobs 5
## 243 march 5th 5
## 244 massive amounts 5
## 245 massive tax 5
## 246 massive trade 5
## 247 mexico canada 5
## 248 military protection 5
## 249 mueller witch 5
## 250 nbc news 5
## 251 nuclear testing 5
## 252 nuclear tests 5
## 253 oval office 5
## 254 phony russia 5
## 255 playing field 5
## 256 police officers 5
## 257 post office 5
## 258 primary win 5
## 259 raise taxes 5
## 260 real deal 5
## 261 regulation cuts 5
## 262 rick saccone 5
## 263 russia witch 5
## 264 saturday night 5
## 265 school safety 5
## 266 special council 5
## 267 strong border 5
## 268 strzok page 5
## 269 successful businessman 5
## 270 talented people 5
## 271 ted cruz 5
## 272 test site 5
## 273 text messages 5
## 274 tom fitton 5
## 275 total disaster 5
## 276 total disgrace 5
## 277 trade talks 5
## 278 trillion dollars 5
## 279 vets 2nd 5
## 280 visa lottery 5
## 281 woodward book 5
## 282 world war 5
## 283 13 gang 4
## 284 abolish ice 4
## 285 adam schiff 4
## 286 african americans 4
## 287 air force 4
## 288 american citizens 4
## 289 american patriots 4
## 290 american unemployment 4
## 291 annapolis maryland 4
## 292 anonymous source 4
## 293 august 7th 4
## 294 background checks 4
## 295 bad trade 4
## 296 barack obama 4
## 297 based immigration 4
## 298 billion trade 4
## 299 bless america 4
## 300 bob dole 4
## 301 border laws 4
## 302 borders crime 4
## 303 bump stocks 4
## 304 clinton emails 4
## 305 conflicted bob 4
## 306 congressman andy 4
## 307 correspondents dinner 4
## 308 crime border 4
## 309 crime loves 4
## 310 criminal justice 4
## 311 cut bill 4
## 312 cuts military 4
## 313 debbie stabenow 4
## 314 dirty dossier 4
## 315 dishonest reporting 4
## 316 don’t exist 4
## 317 duluth minnesota 4
## 318 easily win 4
## 319 election victory 4
## 320 existing conditions 4
## 321 fantastic governor 4
## 322 fbi comey 4
## 323 fbi director 4
## 324 fbi lovers 4
## 325 fisa court 4
## 326 fought hard 4
## 327 fraudulent dossier 4
## 328 gun free 4
## 329 harvard law 4
## 330 hillary clinton’s 4
## 331 house correspondents 4
## 332 immigration system 4
## 333 intelligence community 4
## 334 jobless claims 4
## 335 jobs maga 4
## 336 joint press 4
## 337 joseph mccarthy 4
## 338 josh hawley 4
## 339 judge kavanaugh 4
## 340 june 12th 4
## 341 kanye west 4
## 342 keith rothfus 4
## 343 las vegas 4
## 344 lover peter 4
## 345 maga rally 4
## 346 mark levin 4
## 347 martha mcsally 4
## 348 mccabe strzok 4
## 349 memorial day 4
## 350 mike pence 4
## 351 million people 4
## 352 moon township 4
## 353 mueller team 4
## 354 north dakota 4
## 355 outstanding job 4
## 356 palm beach 4
## 357 patrol agents 4
## 358 pay tribute 4
## 359 phony crime 4
## 360 phony story 4
## 361 pre existing 4
## 362 president george 4
## 363 presidential proclamation 4
## 364 prison reform 4
## 365 pro life 4
## 366 public safety 4
## 367 rally tickets 4
## 368 russian hoax 4
## 369 safety security 4
## 370 sanctuary policies 4
## 371 school shooting 4
## 372 september 11th 4
## 373 setting records 4
## 374 slippery james 4
## 375 social media 4
## 376 something’s happening 4
## 377 spending bill 4
## 378 stay tuned 4
## 379 steel industry 4
## 380 tax regulation 4
## 381 taxes substantially 4
## 382 terror attack 4
## 383 tomorrow night 4
## 384 total hoax 4
## 385 totally conflicted 4
## 386 township pennsylvania 4
## 387 trade agreement 4
## 388 trade surplus 4
## 389 trade war 4
## 390 tremendous success 4
## 391 trump agenda 4
## 392 trump team 4
## 393 u.s history 4
## 394 u.s military 4
## 395 u.s steel 4
## 396 vast amounts 4
## 397 washington michigan 4
## 398 wonderful family 4
## 399 worst trade 4
## 400 xi jinping 4
## 401 york city 4
## 402 10 00 3
## 403 100 billion 3
## 404 13 thugs 3
## 405 2 00 3
## 406 2018 election 3
## 407 2018 world 3
## 408 24 7 3
## 409 243rd birthday 3
## 410 500 billion 3
## 411 6 00 3
## 412 6 months 3
## 413 60 minutes 3
## 414 7 00pm 3
## 415 7 trillion 3
## 416 716 billion 3
## 417 abiding americans 3
## 418 acting attorney 3
## 419 aluminum industries 3
## 420 american dream 3
## 421 american president 3
## 422 american public 3
## 423 andrew cuomo 3
## 424 andrew gillum 3
## 425 andrew mccarthy 3
## 426 andy barr 3
## 427 armed forces 3
## 428 arms race 3
## 429 bad people 3
## 430 bad stories 3
## 431 barbara bush 3
## 432 biggest political 3
## 433 bilateral meeting 3
## 434 birthright citizenship 3
## 435 black americans 3
## 436 blue wave 3
## 437 bob casey 3
## 438 business optimism 3
## 439 california wildfires 3
## 440 campaign colluded 3
## 441 can’t win 3
## 442 carolina governor 3
## 443 cars coming 3
## 444 catherine herridge 3
## 445 chris farrell 3
## 446 city illinois 3
## 447 claire mccaskill 3
## 448 coast guard 3
## 449 companies moving 3
## 450 complete total 3
## 451 confidence hits 3
## 452 congressman ron 3
## 453 corrupt cities 3
## 454 corrupt mainstream 3
## 455 country’s history 3
## 456 court decision 3
## 457 court justices 3
## 458 crime collusion 3
## 459 crime strong 3
## 460 crooked hillary’s 3
## 461 deepest sympathies 3
## 462 deleted emails 3
## 463 delivery boy 3
## 464 dem votes 3
## 465 democrat inspired 3
## 466 democrat votes 3
## 467 democrats fault 3
## 468 derangement syndrome 3
## 469 discredited dossier 3
## 470 discredited mueller 3
## 471 doesn’t exist 3
## 472 drug prices 3
## 473 economic success 3
## 474 elected president 3
## 475 enforcement officer 3
## 476 enforcement professionals 3
## 477 erie pennsylvania 3
## 478 exact opposite 3
## 479 expensive witch 3
## 480 facebook ads 3
## 481 fair deal 3
## 482 fair share 3
## 483 fallen heroes 3
## 484 false statements 3
## 485 fantastic evening 3
## 486 fantastic senator 3
## 487 farmers ranchers 3
## 488 fbi investigation 3
## 489 federal judge 3
## 490 fight hard 3
## 491 fine person 3
## 492 fired fbi 3
## 493 florida ron 3
## 494 forever grateful 3
## 495 free trade 3
## 496 free zones 3
## 497 government shutdown 3
## 498 governor rick 3
## 499 granite city 3
## 500 happy 243rd 3
## 501 heavily conflicted 3
## 502 helsinki finland 3
## 503 hispanic unemployment 3
## 504 historic tax 3
## 505 hit 50 3
## 506 homeland security 3
## 507 house councel 3
## 508 house counsel 3
## 509 house senate 3
## 510 houston texas 3
## 511 hyde smith 3
## 512 illegally leaked 3
## 513 immigration crisis 3
## 514 independence day 3
## 515 independent counsel 3
## 516 it’s called 3
## 517 james comey’s 3
## 518 jeff flake 3
## 519 jerome corsi 3
## 520 jim justice 3
## 521 job killing 3
## 522 jobs defend 3
## 523 joe digenova 3
## 524 john kerry 3
## 525 jonathan turley 3
## 526 katie arrington 3
## 527 killing regulations 3
## 528 korea china 3
## 529 larry kudlow 3
## 530 law abiding 3
## 531 law professor 3
## 532 law school 3
## 533 left wing 3
## 534 lou dobbs 3
## 535 lovely lisa 3
## 536 low taxes 3
## 537 lower taxes 3
## 538 lowest unemployment 3
## 539 lyin james 3
## 540 main street 3
## 541 marine corps 3
## 542 mark sanford 3
## 543 means jobs 3
## 544 media coverage 3
## 545 media hates 3
## 546 medical center 3
## 547 mesa arizona 3
## 548 mid term 3
## 549 mid terms 3
## 550 million dollar 3
## 551 mitch mcconnell 3
## 552 months ago 3
## 553 mueller investigation 3
## 554 mueller report 3
## 555 national border 3
## 556 national defense 3
## 557 nato countries 3
## 558 neverforget september11th 3
## 559 news doesn’t 3
## 560 news washington 3
## 561 northern border 3
## 562 nuclear option 3
## 563 nuclear test 3
## 564 omnibus spending 3
## 565 paris agreement 3
## 566 pastor brunson 3
## 567 patrick morrisey 3
## 568 patrol council 3
## 569 paul ryan 3
## 570 pay tariffs 3
## 571 people coming 3
## 572 people start 3
## 573 phone company 3
## 574 phony russian 3
## 575 phony sources 3
## 576 pittsburgh pennsylvania 3
## 577 political agenda 3
## 578 political purposes 3
## 579 political reasons 3
## 580 popular republican 3
## 581 prescription drugs 3
## 582 president macron 3
## 583 protect criminal 3
## 584 protect europe 3
## 585 putting america 3
## 586 radical left 3
## 587 reagan bob 3
## 588 record pace 3
## 589 record setting 3
## 590 reed medical 3
## 591 republican leadership 3
## 592 republican votes 3
## 593 returned home 3
## 594 richard blumenthal 3
## 595 rigged investigation 3
## 596 rigged russia 3
## 597 rod rosenstein 3
## 598 rush limbaugh 3
## 599 russia investigation 3
## 600 russia probe 3
## 601 san diego 3
## 602 scott pruitt 3
## 603 scott walker 3
## 604 security clearance 3
## 605 senator john 3
## 606 senator jon 3
## 607 sheriff’s office 3
## 608 short time 3
## 609 single day 3
## 610 slow walking 3
## 611 special councel 3
## 612 special election 3
## 613 special people 3
## 614 stop people 3
## 615 strzok firing 3
## 616 stupid trade 3
## 617 switzerland wef18 3
## 618 tariffs barriers 3
## 619 term elections 3
## 620 terrible job 3
## 621 time record 3
## 622 times wrote 3
## 623 top student 3
## 624 total joke 3
## 625 total mess 3
## 626 total support 3
## 627 tower meeting 3
## 628 trade relationship 3
## 629 treated fairly 3
## 630 tremendous potential 3
## 631 tremendous pressure 3
## 632 trump derangement 3
## 633 trump proclaims 3
## 634 trump russia 3
## 635 u.s attorney 3
## 636 u.s china 3
## 637 u.s companies 3
## 638 u.s pays 3
## 639 u.s supreme 3
## 640 u.s.a maga 3
## 641 unredacted documents 3
## 642 vegas nevada 3
## 643 vladimir putin 3
## 644 vote maga 3
## 645 wacky omarosa 3
## 646 walter reed 3
## 647 war games 3
## 648 war hero 3
## 649 washington examiner 3
## 650 wasteful spending 3
## 651 weak laws 3
## 652 wife nelly 3
## 653 world leaders 3
## 654 1 million 2
## 655 1.3 billion 2
## 656 1.6 billion 2
## 657 10 democrat 2
## 658 100th anniversary 2
## 659 11 30 2
## 660 12 trillion 2
## 661 15,000 jobs 2
## 662 17 million 2
## 663 2 commitment 2
## 664 2026 world 2
## 665 3 days 2
## 666 3 minutes 2
## 667 3 p.m 2
## 668 30 a.m 2
## 669 30 p.m 2
## 670 3000 people 2
## 671 4 9 2
## 672 5 billion 2
## 673 500 days 2
## 674 51 votes 2
## 675 55,000 factories 2
## 676 6 million 2
## 677 6,000,000 manufacturing 2
## 678 60 vote 2
## 679 7 365 2
## 680 70th anniversary 2
## 681 73rd session 2
## 682 7th troy 2
## 683 8 30 2
## 684 9 00pme 2
## 685 9 months 2
## 686 90 days 2
## 687 a.g jeff 2
## 688 a.m eastern 2
## 689 a.m enjoy 2
## 690 abc cbs 2
## 691 abc news 2
## 692 absolute power 2
## 693 accumulated trade 2
## 694 acting swiftly 2
## 695 adam laxalt 2
## 696 additional dollars 2
## 697 adds trade 2
## 698 adept teachers 2
## 699 adjusting imports 2
## 700 admiral jackson 2
## 701 agricultural product 2
## 702 agricultural products 2
## 703 allen county 2
## 704 aluminum tariffs 2
## 705 amazon washington 2
## 706 ambassador nikki 2
## 707 amendment vote 2
## 708 america’s future 2
## 709 america’s workers 2
## 710 american cemetery 2
## 711 american democracy 2
## 712 american flag 2
## 713 american hero 2
## 714 american patients 2
## 715 american spirit 2
## 716 american taxpayer 2
## 717 american taxpayers 2
## 718 americans receiving 2
## 719 andres manuel 2
## 720 andrew napolitano 2
## 721 andrews air 2
## 722 angela merkel 2
## 723 animal assad 2
## 724 annual convention 2
## 725 approval rate 2
## 726 attempted sabotage 2
## 727 attorney client 2
## 728 august 2nd 2
## 729 auto companies 2
## 730 bad ideas 2
## 731 bad policies 2
## 732 ballot box 2
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## 9658 zte flourish 1bigrams_united <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")
my.df <- data.frame(table(bigrams_united))
my.df <- my.df[order(my.df$Freq, decreasing=TRUE),]
my.df <- my.df[c(1:500),]
head(my.df)## bigrams_united Freq
## 3287 fake news 160
## 9465 witch hunt 128
## 6050 north korea 84
## 9426 white house 71
## 5994 news media 56
## 8652 total endorsement 49With that, we have the data for the bigram cloud.
library(wordcloud2)
wordcloud2(my.df, color="random-light", backgroundColor = "black")After seeing a few competing renditions, I prefer wordcloud2. One thing to be careful about is scaling. In this case, the most frequent bigram is missing because the ratio makes it too large to fit. With size smaller, it can be made to show. It appears that embedding multiple of these in one post does not render. I will stick with the one correct one.
library(wordcloud2)
hhww <- wordcloud2(my.df, color="random-light", backgroundColor = "black", size = 0.5)
library(widgetframe)## Loading required package: htmlwidgetsframeWidget(hhww, width=600)Getting this to work with frame widgets is tricky. I started something below but cannot seem to make it work so I am constrained to one wordcloud2 per document because they rely on underlying html rendering.
library(htmlwidgets)
library(webshot)
library(widgetframe)
hw1 <- wordcloud2(my.df, color="random-light", backgroundColor = "black", size = 0.5)
frameWidget(hw1, width=600)I think that works quite nicely. The use of jpg for shapes has not worked for me. Nor has letterCloud. I found some code on github that will supposedly solve this but it does not seem to work either. It is supposed to render as an htmlwidget but something about that seems not to work properly.
library(htmlwidgets)
library(webshot)
library(widgetframe)
Ments.Tab <- data.frame(table(Ments))
Ments.Tab <- Ments.Tab[order(Ments.Tab$Freq, decreasing=TRUE),]
my.df.short <- my.df[c(1:40),]
hw1 <- letterCloud(Ments.Tab, "@", size=4, color='random-light')
frameWidget(hw1, width=600)