JMSC 6116 Lecture 5: Text Mining: Tweet Mining and Web Scraping
King-wa Fu
February 12, 2018
if (!require("rtweet")) install.packages("rtweet", repos="https://cran.cnr.berkeley.edu/", dependencies = TRUE)
if (!require("plotly")) install.packages("plotly", repos="https://cran.cnr.berkeley.edu/", dependencies = TRUE)
if (!require("stringr")) install.packages("stringr", repos="https://cran.cnr.berkeley.edu/", dependencies = TRUE)
library(plotly) # load the required libraries
library(rtweet)
library(stringr)1. Tweet Mining
First, We conduct a hashtag search by “bitcoin” from Twitter which returns 500 tweets (no retweet) in English. Pattern matching functions is performed one by one for examples.
bitcoin <- search_tweets("#bitcoin", n=500, include_rts = FALSE, lang="en") # Twitter search
bitcoin.text <- bitcoin$text # Get the text of the tweets
### Which tweet containing URL?
grep("https",bitcoin.text)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [18] 18 19 20 21 22 23 24 25 26 27 28 29 30 32 34 35 36
## [35] 37 38 39 40 41 42 43 44 45 46 47 49 51 52 53 54 55
## [52] 56 57 58 59 60 61 62 63 64 65 67 68 69 70 71 72 73
## [69] 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [86] 91 92 93 95 96 97 98 99 100 101 102 103 104 105 107 109 110
## [103] 113 114 115 117 119 120 121 122 123 124 125 126 127 128 129 130 131
## [120] 132 133 135 136 137 138 140 141 142 143 145 146 147 149 150 152 154
## [137] 155 157 158 160 161 162 163 165 166 167 168 169 170 171 172 174 175
## [154] 177 179 180 181 182 183 184 185 186 187 188 190 191 192 193 194 195
## [171] 196 197 198 199 200 201 202 203 204 205 206 209 210 211 213 214 215
## [188] 216 217 219 220 221 222 223 226 227 228 230 231 232 233 235 237 238
## [205] 239 240 241 243 244 245 246 247 248 249 250 251 252 255 256 257 258
## [222] 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 277
## [239] 278 279 280 281 282 283 284 287 288 289 290 291 292 293 294 295 297
## [256] 298 299 300 303 304 305 306 307 308 310 311 312 313 316 317 318 319
## [273] 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 336 337
## [290] 339 340 341 342 343 344 345 346 347 348 350### Which tweet containing URL (TRUE/FALSE)?
grepl("https",bitcoin.text)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [12] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [23] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [34] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [45] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [56] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [67] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [78] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [89] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [100] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
## [111] FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
## [122] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [133] TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [144] FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
## [155] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
## [177] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [188] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [199] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
## [210] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [221] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
## [232] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [243] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [254] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [276] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [287] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [298] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [309] FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [320] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [331] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE
## [342] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE### Display all those matached
head(grep("https",bitcoin.text,value=TRUE))## [1] "Just signed up for WCX, the global digital currency exchange. Sign up & earn 50 WCX tokens. @wcxofficial #bitcoin https://t.co/1tlAN1RAhs"
## [2] "Great Buy Trading Opportunity of #ETCBTC , Don't miss it!\n#Ethereum_Classic #Bitcoin \nhttps://t.co/XBtB1t4Ast"
## [3] "Arizona May Soon Allow Residents To Pay Taxes Using Bitcoin! via /r/#Bitcoin https://t.co/OKagqLkQj8 https://t.co/1HqlNeXgjx"
## [4] "CCs are unlikely to disappear completely via /r/#Bitcoin https://t.co/ibiokzKXKq https://t.co/1HqlNeXgjx"
## [5] "Bitcoin mining incentives via /r/#Bitcoin https://t.co/bu1MWCzOCP https://t.co/1HqlNeXgjx"
## [6] "Just found out I have BCH. Wanna get rid of it. But I'm afraid. via /r/#Bitcoin https://t.co/xZeJDK30Fe https://t.co/1HqlNeXgjx"### Which tweet NOT containing URL?
grep("https",bitcoin.text,invert=TRUE)## [1] 31 33 48 50 66 94 106 108 111 112 116 118 134 139 144 148 151
## [18] 153 156 159 164 173 176 178 189 207 208 212 218 224 225 229 234 236
## [35] 242 253 254 275 276 285 286 296 301 302 309 314 315 335 338 349 351
## [52] 352### Logical or/and
grep("blockchain",bitcoin.text)## [1] 32 36 38 39 46 47 51 54 55 56 58 59 60 61 62 63 81
## [18] 82 101 106 109 114 122 123 124 125 126 127 128 129 130 131 132 133
## [35] 139 148 149 155 165 191 196 227 239 248 249 251 252 260 261 266 270
## [52] 281 282 284 295 307 310 312 316 317 319 322 341 342 346 349grep("cryptocurrency",bitcoin.text)## [1] 29 30 32 36 39 47 50 51 57 65 68 71 103 106 108 109 114
## [18] 144 148 151 155 161 165 170 172 175 177 178 181 184 191 196 210 223
## [35] 225 227 228 239 250 252 255 259 260 264 266 273 274 275 281 282 284
## [52] 286 299 302 303 306 310 313 316 321 322 325 327 329 330 336 341 344
## [69] 345### Mentioning blockchain or bank: default case sensitive
grepl("blockchain|cryptocurrency",bitcoin.text, ignore.case = TRUE)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [34] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [45] FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [56] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [67] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
## [78] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
## [111] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [122] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [133] TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [144] TRUE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
## [155] TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
## [166] FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
## [177] TRUE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE
## [188] TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE
## [199] TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
## [210] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [221] FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE
## [232] FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## [243] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [254] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [276] FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
## [287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [298] TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE
## [309] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE TRUE
## [320] TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
## [331] FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
## [342] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSEgrepl("blockchain",bitcoin.text,ignore.case = TRUE) & grepl("cryptocurrency",bitcoin.text,ignore.case = TRUE)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [34] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [111] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [122] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [144] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [155] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [166] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [188] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [199] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [210] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [221] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [232] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## [243] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [254] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [265] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [276] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
## [287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [298] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [309] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [320] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [331] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [342] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE### Use grep/grepl as an index vector
bitcoin$screen_name[grepl("blockchain",bitcoin.text,ignore.case = TRUE) & grepl("cryptocurrency",bitcoin.text,ignore.case = TRUE)]## [1] "creamcoin" "Remi_Vladuceanu" "cryptocyinvest"
## [4] "Medici_Bank" "IDXHCI" "coinspectator"
## [7] "1FoxyCrypto" "harkorede911" "abrahammia01"
## [10] "Remi_Vladuceanu" "elena201232" "myavikram"
## [13] "elena201232" "Dash_Fans_" "CryptoCurrent"
## [16] "hossein761" "povr_" "Remi_Vladuceanu"
## [19] "dangsaoha2" "taisnot" "1FoxyCrypto"
## [22] "torsten_gartner" "Euro_Hyips" "ricardokozak"
## [25] "BitcoinNewsAus" "Remi_Vladuceanu" "OfficialVenkat"
## [28] "BitcoinNewsAus" "Crypto_Kate" "ridleyg"
## [31] "Crypto_Kate" "cryptonews_____" "xitcliff_msk"
## [34] "crypt0_airdrop" "trilodi" "Remi_Vladuceanu"
## [37] "henyamaryn"###
# Regular Expression -
# . - anything
# + - wildcard (at least 1 match)
# * - wildcard (zero or more matches)
# ? - non-greedy
# $ - end of line
# [] - either one within the bracket
# | - or
#
### Extract the urls
tweet_url <- str_extract_all(bitcoin.text,'https://[0-9a-zA-Z./]+|https://[0-9a-zA-Z./]+$')
tweet_url <- unlist(tweet_url)
head(tweet_url)## [1] "https://t.co/1tlAN1RAhs" "https://t.co/XBtB1t4Ast"
## [3] "https://t.co/OKagqLkQj8" "https://t.co/1HqlNeXgjx"
## [5] "https://t.co/ibiokzKXKq" "https://t.co/1HqlNeXgjx"### Top 5 Most Mentioned @
tweet_mention <- str_extract_all(bitcoin.text,'@[0-9a-zA-Z_]+|@[0-9a-zA-Z_]+$')
tweet_mention <- unlist(tweet_mention)
top10mention <- sort(table(tweet_mention),decreasing=TRUE)[1:5]
top10mention## tweet_mention
## @mijanya @shineonmag @St0rmar @0rjiinal
## 2 2 2 1
## @aelfblockchain
## 1## Plot
p <- plot_ly(x = names(top10mention), y = top10mention, name = "Top 10 Mention", type = 'bar')
layout(p, title = "Top 10 Mention (bitcoin)", xaxis = list(title = "@Name"), yaxis = list (title = "Frequency"))### Top 5 Most Popular Hashtag #
tweet_hashtag <- str_extract_all(bitcoin.text,'#[0-9a-zA-Z_]+|#[0-9a-zA-Z_]+$')
tweet_hashtag <- unlist(tweet_hashtag)
top10hashtag <- sort(table(tweet_hashtag),decreasing=TRUE)[1:5]
top10hashtag## tweet_hashtag
## #Bitcoin #bitcoin #cryptocurrency #blockchain
## 180 172 64 62
## #crypto
## 60## Plot
p <- plot_ly(x = names(top10hashtag), y = top10hashtag, name = "Top 10 Hashtag", type = 'bar')
layout(p, title = "Top 10 Hashtag (bitcoin)", xaxis = list(title = "#Hashtag"), yaxis = list (title = "Frequency"))### Remove all URLs, mention and hashtag and 'RT' and puncutnation
bitcoin.text <- gsub('https://[0-9a-zA-Z./]+|https://[0-9a-zA-Z./]+$','',bitcoin.text)
bitcoin.text <- gsub('#[0-9a-zA-Z_]+|#[0-9a-zA-Z_]+$','',bitcoin.text)
bitcoin.text <- gsub('@[0-9a-zA-Z_]+|@[0-9a-zA-Z_]+$','',bitcoin.text)
bitcoin.text <- gsub('RT','',bitcoin.text)
bitcoin.text <- gsub('[[:punct:]]','',bitcoin.text)
### Remove leading and ending whitespace
bitcoin.text <- gsub('^[ ]*','',bitcoin.text)
bitcoin.text <- gsub('[ ]*$','',bitcoin.text)
head(bitcoin.text)## [1] "Just signed up for WCX the global digital currency exchange Sign up amp earn 50 WCX tokens"
## [2] "Great Buy Trading Opportunity of Dont miss it\n \n"
## [3] "Arizona May Soon Allow Residents To Pay Taxes Using Bitcoin via r"
## [4] "CCs are unlikely to disappear completely via r"
## [5] "Bitcoin mining incentives via r"
## [6] "Just found out I have BCH Wanna get rid of it But Im afraid via r"### nchar("simple text sample") ### Number of characters
### strsplit("simple text sample",' ') ### Splitting a string
### substr("simple text sample",1,3) ### Extracting a substring from a string
### paste("sample","text","sample", sep=' ') ### Concatenating strings
### tolower("SimPle TeXt sAmpLe") ### Converting letters to lower or upper-case
### toupper("SimPle TeXt sAmpLe") ### Converting letters to lower or upper-case2. Web scraping
if (!require("RCurl")) install.packages("RCurl", repos="https://cran.cnr.berkeley.edu/", dependencies = TRUE)
library(RCurl) # load the required libraryIn the second project, We use pattern matching functions to develop a web-scrapper to crawl the press release headlines from the Government Information Service website.
### Read Today's Hong Kong's GIS
today_gis <- getURL("http://www.info.gov.hk/gia/general/today.htm")
all_news_title <- unlist(str_extract_all(today_gis,'.htm">.+?</a></li>')) ## Extract the titles from the web page
all_news_title <- gsub('.htm">','',all_news_title) ### Removing heading
all_news_title <- gsub('</a></li>','',all_news_title) ### Removing tailing
head(all_news_title)## [1] "<span>HAD opens temporary cold shelters</span>"
## [2] "<span>New mattress and bedding start up from San Francisco launches in Hong Kong (with photos)</span>"
## [3] ""2016 Population By-census Thematic Report: Youths" published"
## [4] "Cancellation of LegCo subcommittee meeting"
## [5] "Government appoints directors to HKEX Board"
## [6] "SCMA visits TWGHs hotline service centre for sexual minorities (with photos)"#
# Create a function for title extraction
#
title_extraction <- function(url){
nt_gis <- getURL(url)
all_news_title <- unlist(str_extract_all(nt_gis,'.htm">.*</a></li>'))
all_news_title <- gsub('.htm">','',all_news_title)
all_news_title <- gsub('</a></li>','',all_news_title)
}
# Date sequence from Jan 01 to Feb 11 2018
d_seq <- seq(as.Date("2018-01-01"),as.Date("2018-02-11"),1)
# Formatting the sequence into the web url pattern
date_seq <- format(d_seq,format='%Y%m/%d')
#
# Headline containing CE/FS/CS?
#
for (day in date_seq){
url <- paste("http://www.info.gov.hk/gia/general/",day,".htm",sep="")
day_nt <- title_extraction(url)
ce <- length(grep('^CE | CE | CE$',day_nt,ignore.case = TRUE))
fs <- length(grep('^FS | FS | FS$',day_nt,ignore.case = TRUE))
cs <- length(grep('^CS | CS | CS$',day_nt,ignore.case = TRUE))
print(paste0(day,":","Chief Executive -",ce," | Financial Secretary -",fs," | Chief Secretary for Administration-",cs))
Sys.sleep(2)
} ## [1] "201801/01:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/02:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201801/03:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/04:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/05:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/06:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/07:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/08:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/09:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/10:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/11:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/12:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/13:Chief Executive -0 | Financial Secretary -1 | Chief Secretary for Administration-1"
## [1] "201801/14:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/15:Chief Executive -1 | Financial Secretary -2 | Chief Secretary for Administration-0"
## [1] "201801/16:Chief Executive -3 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201801/17:Chief Executive -1 | Financial Secretary -1 | Chief Secretary for Administration-1"
## [1] "201801/18:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/19:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201801/20:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/21:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/22:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/23:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/24:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/25:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/26:Chief Executive -3 | Financial Secretary -0 | Chief Secretary for Administration-2"
## [1] "201801/27:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201801/28:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/29:Chief Executive -0 | Financial Secretary -1 | Chief Secretary for Administration-0"
## [1] "201801/30:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201801/31:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/01:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/02:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201802/03:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201802/04:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/05:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201802/06:Chief Executive -2 | Financial Secretary -0 | Chief Secretary for Administration-1"
## [1] "201802/07:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/08:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/09:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/10:Chief Executive -1 | Financial Secretary -0 | Chief Secretary for Administration-0"
## [1] "201802/11:Chief Executive -0 | Financial Secretary -0 | Chief Secretary for Administration-1"