HolidayCheck Case Study
Austin Costello
June 15, 2022
First install needed Packages
##install.packages("taskscheduleR")
##library(taskscheduleR)
library(RCurl)
library(RJSONIO)
library(sqldf)## Loading required package: gsubfn## Loading required package: proto## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
## dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 6): Library not loaded: /opt/X11/lib/libSM.6.dylib
## Referenced from: /Library/Frameworks/R.framework/Resources/modules//R_X11.so
## Reason: image not found## Could not load tcltk. Will use slower R code instead.## Loading required package: RSQLitelibrary(ggplot2)
library(lubridate)## Warning: package 'lubridate' was built under R version 3.4.4##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## datelibrary(plyr)##
## Attaching package: 'plyr'## The following object is masked from 'package:lubridate':
##
## herelibrary(scales)
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(wordcloud)## Loading required package: RColorBrewerlibrary(tm)## Warning: package 'tm' was built under R version 3.4.3## Loading required package: NLP##
## Attaching package: 'NLP'## The following object is masked from 'package:ggplot2':
##
## annotatePart 1: Extract information about Justin Trudeau from the Guardian Media Group API
First we will grab the needed information from the API
## Grab information from the content page
# First build the URL
key<-"9d910bec-dee4-480b-b042-a806eb14738e"
##start_date<-"2018-01-01"
page_size<-"200" #number of results per page
url <- paste0("https://content.guardianapis.com/world/justin-trudeau?api-key=", key,
## "&from-date=", start_date,
"&page-size=", page_size,
"&page=1")
data_raw<-getURL(url)
data_reformat <- fromJSON(data_raw,nullValue = NA)
#grab results that we want from this data
data_articles<-data_reformat$response$results
##Now find how many pages of content there are (only first page is pulled above)
number_pages<-data_reformat$response$pages
## Now grab data for each following pages (since more than 1) and add to data_articles
for (p in 2:number_pages) #start from second page since first page is already pulled
{
url_new <- paste0("https://content.guardianapis.com/world/justin-trudeau?api-key=", key,
## "&from-date=", start_date,
"&page-size=", page_size,
"&page=",p)
data_raw_add<-getURL(url_new)
data_reformat_add<-fromJSON(data_raw_add,nullValue = NA)
data_articles<-append(data_articles,data_reformat_add$response$results)
}Next we will create a dataframe with the resulting data
article_info<-c()
for (a in 1:length(data_articles))
{
new_row<-cbind(data_articles[[a]]$id,data_articles[[a]]$type
,data_articles[[a]]$webPublicationDate, data_articles[[a]]$webTitle
,data_articles[[a]]$webUrl, data_articles[[a]]$apiUrl
,data_articles[[a]]$sectionId, data_articles[[a]]$sectionName
)
article_info<-rbind(article_info, new_row)
}
df.article_info<-as.data.frame(article_info)
col_names<-c("id","type","webPublicationDate","webTitle","webUrl","apiUrl", "sectionId","sectionName")
colnames(df.article_info) <-col_names
### Summarize dataframe to see what data is available
summary(df.article_info)## id
## artanddesign/2016/aug/19/syrian-bassel-mcleash-justin-trudeau : 1
## australia-news/2015/oct/23/putin-may-live-to-regret-intervention-in-syria-warns-malcolm-turnbull: 1
## business/2015/nov/01/cameron-falters-canada-understands-keynes-trudeau : 1
## business/2016/mar/15/trump-impact-on-canada-us-trade-relations-nafta-republicans : 1
## business/2016/oct/24/belgium-eu-ultimatum-canada-trade-deal-ceta-wallonia : 1
## business/2016/oct/30/eu-canada-sign-ceta-free-trade-deal-trudeau-juncker : 1
## (Other) :389
## type webPublicationDate
## article :388 2012-10-04T11:00:02Z: 1
## interactive: 1 2013-04-17T11:29:04Z: 1
## liveblog : 6 2015-10-20T12:02:57Z: 1
## 2015-10-20T16:17:02Z: 1
## 2015-10-20T16:27:29Z: 1
## 2015-10-20T16:49:28Z: 1
## (Other) :389
## webTitle
## 'Give her whatever she wants': readers on Sophie Grégoire Trudeau's staffing plea | Guardian readers and Sarah Marsh: 1
## 'He's not a bad person, but â\u0080¦' scandal-hit Justin Trudeau turns voters off : 1
## 'Heartbreaking': Canadians mourn Iran plane crash victims : 1
## 'I need help': Sophie Grégoire Trudeau's plea sparks anger in Canada : 1
## 'It's like night and day': Trudeau's and Trump's Covid-19 responses fuel wildly different outcomes : 1
## 'Leave Nato': Justin Trudeau pranked by Russian duo posing as Greta Thunberg : 1
## (Other) :389
## webUrl
## https://www.theguardian.com/artanddesign/2016/aug/19/syrian-bassel-mcleash-justin-trudeau : 1
## https://www.theguardian.com/australia-news/2015/oct/23/putin-may-live-to-regret-intervention-in-syria-warns-malcolm-turnbull: 1
## https://www.theguardian.com/business/2015/nov/01/cameron-falters-canada-understands-keynes-trudeau : 1
## https://www.theguardian.com/business/2016/mar/15/trump-impact-on-canada-us-trade-relations-nafta-republicans : 1
## https://www.theguardian.com/business/2016/oct/24/belgium-eu-ultimatum-canada-trade-deal-ceta-wallonia : 1
## https://www.theguardian.com/business/2016/oct/30/eu-canada-sign-ceta-free-trade-deal-trudeau-juncker : 1
## (Other) :389
## apiUrl
## https://content.guardianapis.com/artanddesign/2016/aug/19/syrian-bassel-mcleash-justin-trudeau : 1
## https://content.guardianapis.com/australia-news/2015/oct/23/putin-may-live-to-regret-intervention-in-syria-warns-malcolm-turnbull: 1
## https://content.guardianapis.com/business/2015/nov/01/cameron-falters-canada-understands-keynes-trudeau : 1
## https://content.guardianapis.com/business/2016/mar/15/trump-impact-on-canada-us-trade-relations-nafta-republicans : 1
## https://content.guardianapis.com/business/2016/oct/24/belgium-eu-ultimatum-canada-trade-deal-ceta-wallonia : 1
## https://content.guardianapis.com/business/2016/oct/30/eu-canada-sign-ceta-free-trade-deal-trudeau-juncker : 1
## (Other) :389
## sectionId sectionName
## world :233 World news :233
## commentisfree: 56 Opinion : 56
## environment : 24 Environment: 24
## us-news : 23 US news : 23
## politics : 11 Politics : 11
## business : 10 Business : 10
## (Other) : 38 (Other) : 38##Clean the data
# we can see entries which are not type = article --> drop these
df.article_info<-df.article_info[df.article_info$type=="article",]
#format date how we need it
df.article_info$webPublicationDate<-substr(df.article_info$webPublicationDate, 1, 10)
head(df.article_info)## id
## 1 world/2022/may/31/handgun-freeze-in-canada-and-five-round-limit-on-magazines
## 2 world/2022/may/08/putins-actions-in-ukraine-bring-shame-on-russia-says-g7
## 3 money/2022/mar/28/subsidized-childcare-canada-ontario-signs-on
## 4 world/2022/mar/22/justin-trudeau-power-sharing-deal-new-democrats
## 5 world/2022/feb/20/ottawa-protests-capital-police-clear-blockade
## 6 technology/2022/feb/17/elon-musk-criticised-for-comparing-justin-trudeau-to-adolf-hitler-tweet-auschwitz
## type webPublicationDate
## 1 article 2022-05-31
## 2 article 2022-05-08
## 3 article 2022-03-28
## 4 article 2022-03-22
## 5 article 2022-02-20
## 6 article 2022-02-17
## webTitle
## 1 Canada plans complete freeze on handgun ownership
## 2 Putinâ\u0080\u0099s invasion of Ukraine brings shame on Russia, G7 leaders say
## 3 â\u0080\u0098Historic momentâ\u0080\u0099 in Canada as Ontario strikes deal for $10-a-day childcare
## 4 Justin Trudeau strikes power-sharing deal with leftwing New Democrats
## 5 Ottawa protests: conspiracy theories and accusations of betrayal as police end blockade
## 6 Elon Musk criticised for likening Justin Trudeau to Adolf Hitler in tweet
## webUrl
## 1 https://www.theguardian.com/world/2022/may/31/handgun-freeze-in-canada-and-five-round-limit-on-magazines
## 2 https://www.theguardian.com/world/2022/may/08/putins-actions-in-ukraine-bring-shame-on-russia-says-g7
## 3 https://www.theguardian.com/money/2022/mar/28/subsidized-childcare-canada-ontario-signs-on
## 4 https://www.theguardian.com/world/2022/mar/22/justin-trudeau-power-sharing-deal-new-democrats
## 5 https://www.theguardian.com/world/2022/feb/20/ottawa-protests-capital-police-clear-blockade
## 6 https://www.theguardian.com/technology/2022/feb/17/elon-musk-criticised-for-comparing-justin-trudeau-to-adolf-hitler-tweet-auschwitz
## apiUrl
## 1 https://content.guardianapis.com/world/2022/may/31/handgun-freeze-in-canada-and-five-round-limit-on-magazines
## 2 https://content.guardianapis.com/world/2022/may/08/putins-actions-in-ukraine-bring-shame-on-russia-says-g7
## 3 https://content.guardianapis.com/money/2022/mar/28/subsidized-childcare-canada-ontario-signs-on
## 4 https://content.guardianapis.com/world/2022/mar/22/justin-trudeau-power-sharing-deal-new-democrats
## 5 https://content.guardianapis.com/world/2022/feb/20/ottawa-protests-capital-police-clear-blockade
## 6 https://content.guardianapis.com/technology/2022/feb/17/elon-musk-criticised-for-comparing-justin-trudeau-to-adolf-hitler-tweet-auschwitz
## sectionId sectionName
## 1 world World news
## 2 world World news
## 3 world World news
## 4 world World news
## 5 world World news
## 6 technology TechnologyPart 2: Count how many articles about Justin Trudeau have been posted since 01.01.2018 until today
#first we populate all dates within timeframe of interest, since not every day has content
historical_dates<-seq(as.Date("2018-01-01"), Sys.Date(), by="days")
days <- length(historical_dates)
#now we can count how many articles appear each day
daily_article_count<-c()
for (d in 1:days) {
new_day<-cbind(as.character(historical_dates[d]), sum(df.article_info$webPublicationDate==historical_dates[d]))
daily_article_count<-rbind(daily_article_count,new_day)
}
df.daily_article_count<-as.data.frame(daily_article_count)
col_names2<- c("Date", "No. of articles")
colnames(df.daily_article_count) <-col_names2
df.daily_article_count$`No. of articles`<-as.integer(as.character(df.daily_article_count$`No. of articles`))
#show results
print(df.daily_article_count)## Date No. of articles
## 1 2018-01-01 0
## 2 2018-01-02 0
## 3 2018-01-03 0
## 4 2018-01-04 0
## 5 2018-01-05 0
## 6 2018-01-06 0
## 7 2018-01-07 0
## 8 2018-01-08 0
## 9 2018-01-09 0
## 10 2018-01-10 1
## 11 2018-01-11 0
## 12 2018-01-12 0
## 13 2018-01-13 0
## 14 2018-01-14 0
## 15 2018-01-15 0
## 16 2018-01-16 0
## 17 2018-01-17 0
## 18 2018-01-18 0
## 19 2018-01-19 0
## 20 2018-01-20 0
## 21 2018-01-21 0
## 22 2018-01-22 0
## 23 2018-01-23 1
## 24 2018-01-24 0
## 25 2018-01-25 0
## 26 2018-01-26 0
## 27 2018-01-27 0
## 28 2018-01-28 0
## 29 2018-01-29 0
## 30 2018-01-30 0
## 31 2018-01-31 0
## 32 2018-02-01 0
## 33 2018-02-02 0
## 34 2018-02-03 0
## 35 2018-02-04 0
## 36 2018-02-05 0
## 37 2018-02-06 0
## 38 2018-02-07 2
## 39 2018-02-08 1
## 40 2018-02-09 1
## 41 2018-02-10 0
## 42 2018-02-11 0
## 43 2018-02-12 0
## 44 2018-02-13 0
## 45 2018-02-14 0
## 46 2018-02-15 0
## 47 2018-02-16 0
## 48 2018-02-17 0
## 49 2018-02-18 1
## 50 2018-02-19 1
## 51 2018-02-20 0
## 52 2018-02-21 0
## 53 2018-02-22 1
## 54 2018-02-23 0
## 55 2018-02-24 0
## 56 2018-02-25 0
## 57 2018-02-26 0
## 58 2018-02-27 0
## 59 2018-02-28 0
## 60 2018-03-01 0
## 61 2018-03-02 1
## 62 2018-03-03 0
## 63 2018-03-04 0
## 64 2018-03-05 0
## 65 2018-03-06 0
## 66 2018-03-07 0
## 67 2018-03-08 0
## 68 2018-03-09 0
## 69 2018-03-10 0
## 70 2018-03-11 0
## 71 2018-03-12 1
## 72 2018-03-13 0
## 73 2018-03-14 0
## 74 2018-03-15 1
## 75 2018-03-16 0
## 76 2018-03-17 0
## 77 2018-03-18 0
## 78 2018-03-19 0
## 79 2018-03-20 0
## 80 2018-03-21 0
## 81 2018-03-22 0
## 82 2018-03-23 0
## 83 2018-03-24 0
## 84 2018-03-25 0
## 85 2018-03-26 1
## 86 2018-03-27 0
## 87 2018-03-28 0
## 88 2018-03-29 0
## 89 2018-03-30 0
## 90 2018-03-31 0
## 91 2018-04-01 0
## 92 2018-04-02 0
## 93 2018-04-03 0
## 94 2018-04-04 0
## 95 2018-04-05 0
## 96 2018-04-06 0
## 97 2018-04-07 0
## 98 2018-04-08 0
## 99 2018-04-09 0
## 100 2018-04-10 0
## 101 2018-04-11 0
## 102 2018-04-12 0
## 103 2018-04-13 0
## 104 2018-04-14 0
## 105 2018-04-15 0
## 106 2018-04-16 3
## 107 2018-04-17 0
## 108 2018-04-18 0
## 109 2018-04-19 0
## 110 2018-04-20 0
## 111 2018-04-21 0
## 112 2018-04-22 0
## 113 2018-04-23 0
## 114 2018-04-24 0
## 115 2018-04-25 0
## 116 2018-04-26 0
## 117 2018-04-27 0
## 118 2018-04-28 0
## 119 2018-04-29 0
## 120 2018-04-30 0
## 121 2018-05-01 0
## 122 2018-05-02 0
## 123 2018-05-03 0
## 124 2018-05-04 1
## 125 2018-05-05 0
## 126 2018-05-06 0
## 127 2018-05-07 0
## 128 2018-05-08 0
## 129 2018-05-09 0
## 130 2018-05-10 0
## 131 2018-05-11 0
## 132 2018-05-12 0
## 133 2018-05-13 0
## 134 2018-05-14 0
## 135 2018-05-15 0
## 136 2018-05-16 1
## 137 2018-05-17 0
## 138 2018-05-18 0
## 139 2018-05-19 0
## 140 2018-05-20 0
## 141 2018-05-21 0
## 142 2018-05-22 0
## 143 2018-05-23 0
## 144 2018-05-24 0
## 145 2018-05-25 0
## 146 2018-05-26 0
## 147 2018-05-27 0
## 148 2018-05-28 0
## 149 2018-05-29 0
## 150 2018-05-30 1
## 151 2018-05-31 1
## 152 2018-06-01 0
## 153 2018-06-02 0
## 154 2018-06-03 1
## 155 2018-06-04 0
## 156 2018-06-05 0
## 157 2018-06-06 1
## 158 2018-06-07 2
## 159 2018-06-08 0
## 160 2018-06-09 1
## 161 2018-06-10 2
## 162 2018-06-11 6
## 163 2018-06-12 2
## 164 2018-06-13 0
## 165 2018-06-14 0
## 166 2018-06-15 0
## 167 2018-06-16 0
## 168 2018-06-17 0
## 169 2018-06-18 0
## 170 2018-06-19 2
## 171 2018-06-20 0
## 172 2018-06-21 1
## 173 2018-06-22 0
## 174 2018-06-23 0
## 175 2018-06-24 0
## 176 2018-06-25 0
## 177 2018-06-26 0
## 178 2018-06-27 0
## 179 2018-06-28 0
## 180 2018-06-29 1
## 181 2018-06-30 1
## 182 2018-07-01 0
## 183 2018-07-02 1
## 184 2018-07-03 0
## 185 2018-07-04 0
## 186 2018-07-05 0
## 187 2018-07-06 2
## 188 2018-07-07 0
## 189 2018-07-08 0
## 190 2018-07-09 0
## 191 2018-07-10 1
## 192 2018-07-11 0
## 193 2018-07-12 0
## 194 2018-07-13 0
## 195 2018-07-14 0
## 196 2018-07-15 0
## 197 2018-07-16 0
## 198 2018-07-17 0
## 199 2018-07-18 0
## 200 2018-07-19 0
## 201 2018-07-20 0
## 202 2018-07-21 0
## 203 2018-07-22 0
## 204 2018-07-23 0
## 205 2018-07-24 0
## 206 2018-07-25 0
## 207 2018-07-26 0
## 208 2018-07-27 0
## 209 2018-07-28 0
## 210 2018-07-29 0
## 211 2018-07-30 0
## 212 2018-07-31 0
## 213 2018-08-01 0
## 214 2018-08-02 0
## 215 2018-08-03 0
## 216 2018-08-04 0
## 217 2018-08-05 0
## 218 2018-08-06 1
## 219 2018-08-07 0
## 220 2018-08-08 0
## 221 2018-08-09 0
## 222 2018-08-10 0
## 223 2018-08-11 0
## 224 2018-08-12 0
## 225 2018-08-13 0
## 226 2018-08-14 0
## 227 2018-08-15 0
## 228 2018-08-16 0
## 229 2018-08-17 0
## 230 2018-08-18 0
## 231 2018-08-19 0
## 232 2018-08-20 0
## 233 2018-08-21 0
## 234 2018-08-22 0
## 235 2018-08-23 0
## 236 2018-08-24 0
## 237 2018-08-25 0
## 238 2018-08-26 0
## 239 2018-08-27 0
## 240 2018-08-28 0
## 241 2018-08-29 0
## 242 2018-08-30 0
## 243 2018-08-31 0
## 244 2018-09-01 0
## 245 2018-09-02 0
## 246 2018-09-03 0
## 247 2018-09-04 0
## 248 2018-09-05 0
## 249 2018-09-06 0
## 250 2018-09-07 0
## 251 2018-09-08 0
## 252 2018-09-09 0
## 253 2018-09-10 0
## 254 2018-09-11 0
## 255 2018-09-12 0
## 256 2018-09-13 0
## 257 2018-09-14 0
## 258 2018-09-15 0
## 259 2018-09-16 0
## 260 2018-09-17 0
## 261 2018-09-18 0
## 262 2018-09-19 0
## 263 2018-09-20 0
## 264 2018-09-21 0
## 265 2018-09-22 0
## 266 2018-09-23 0
## 267 2018-09-24 0
## 268 2018-09-25 0
## 269 2018-09-26 1
## 270 2018-09-27 0
## 271 2018-09-28 1
## 272 2018-09-29 0
## 273 2018-09-30 0
## 274 2018-10-01 0
## 275 2018-10-02 0
## 276 2018-10-03 0
## 277 2018-10-04 0
## 278 2018-10-05 0
## 279 2018-10-06 0
## 280 2018-10-07 0
## 281 2018-10-08 0
## 282 2018-10-09 0
## 283 2018-10-10 1
## 284 2018-10-11 0
## 285 2018-10-12 0
## 286 2018-10-13 0
## 287 2018-10-14 0
## 288 2018-10-15 0
## 289 2018-10-16 0
## 290 2018-10-17 0
## 291 2018-10-18 0
## 292 2018-10-19 0
## 293 2018-10-20 0
## 294 2018-10-21 0
## 295 2018-10-22 0
## 296 2018-10-23 0
## 297 2018-10-24 0
## 298 2018-10-25 0
## 299 2018-10-26 1
## 300 2018-10-27 0
## 301 2018-10-28 0
## 302 2018-10-29 0
## 303 2018-10-30 0
## 304 2018-10-31 0
## 305 2018-11-01 0
## 306 2018-11-02 0
## 307 2018-11-03 0
## 308 2018-11-04 0
## 309 2018-11-05 0
## 310 2018-11-06 0
## 311 2018-11-07 0
## 312 2018-11-08 0
## 313 2018-11-09 0
## 314 2018-11-10 0
## 315 2018-11-11 0
## 316 2018-11-12 1
## 317 2018-11-13 0
## 318 2018-11-14 0
## 319 2018-11-15 0
## 320 2018-11-16 0
## 321 2018-11-17 0
## 322 2018-11-18 0
## 323 2018-11-19 0
## 324 2018-11-20 0
## 325 2018-11-21 0
## 326 2018-11-22 0
## 327 2018-11-23 0
## 328 2018-11-24 0
## 329 2018-11-25 0
## 330 2018-11-26 0
## 331 2018-11-27 0
## 332 2018-11-28 1
## 333 2018-11-29 0
## 334 2018-11-30 1
## 335 2018-12-01 0
## 336 2018-12-02 0
## 337 2018-12-03 0
## 338 2018-12-04 0
## 339 2018-12-05 0
## 340 2018-12-06 0
## 341 2018-12-07 0
## 342 2018-12-08 0
## 343 2018-12-09 0
## 344 2018-12-10 0
## 345 2018-12-11 0
## 346 2018-12-12 0
## 347 2018-12-13 0
## 348 2018-12-14 0
## 349 2018-12-15 0
## 350 2018-12-16 0
## 351 2018-12-17 1
## 352 2018-12-18 0
## 353 2018-12-19 0
## 354 2018-12-20 0
## 355 2018-12-21 0
## 356 2018-12-22 0
## 357 2018-12-23 0
## 358 2018-12-24 0
## 359 2018-12-25 0
## 360 2018-12-26 0
## 361 2018-12-27 0
## 362 2018-12-28 0
## 363 2018-12-29 0
## 364 2018-12-30 0
## 365 2018-12-31 0
## 366 2019-01-01 0
## 367 2019-01-02 1
## 368 2019-01-03 0
## 369 2019-01-04 0
## 370 2019-01-05 0
## 371 2019-01-06 0
## 372 2019-01-07 0
## 373 2019-01-08 0
## 374 2019-01-09 0
## 375 2019-01-10 0
## 376 2019-01-11 1
## 377 2019-01-12 1
## 378 2019-01-13 0
## 379 2019-01-14 0
## 380 2019-01-15 0
## 381 2019-01-16 1
## 382 2019-01-17 0
## 383 2019-01-18 0
## 384 2019-01-19 0
## 385 2019-01-20 0
## 386 2019-01-21 1
## 387 2019-01-22 0
## 388 2019-01-23 0
## 389 2019-01-24 0
## 390 2019-01-25 0
## 391 2019-01-26 0
## 392 2019-01-27 0
## 393 2019-01-28 0
## 394 2019-01-29 0
## 395 2019-01-30 0
## 396 2019-01-31 0
## 397 2019-02-01 0
## 398 2019-02-02 1
## 399 2019-02-03 0
## 400 2019-02-04 0
## 401 2019-02-05 0
## 402 2019-02-06 0
## 403 2019-02-07 0
## 404 2019-02-08 0
## 405 2019-02-09 0
## 406 2019-02-10 0
## 407 2019-02-11 0
## 408 2019-02-12 1
## 409 2019-02-13 0
## 410 2019-02-14 0
## 411 2019-02-15 0
## 412 2019-02-16 0
## 413 2019-02-17 0
## 414 2019-02-18 0
## 415 2019-02-19 0
## 416 2019-02-20 0
## 417 2019-02-21 1
## 418 2019-02-22 0
## 419 2019-02-23 0
## 420 2019-02-24 0
## 421 2019-02-25 0
## 422 2019-02-26 0
## 423 2019-02-27 1
## 424 2019-02-28 2
## 425 2019-03-01 2
## 426 2019-03-02 0
## 427 2019-03-03 0
## 428 2019-03-04 0
## 429 2019-03-05 1
## 430 2019-03-06 0
## 431 2019-03-07 1
## 432 2019-03-08 0
## 433 2019-03-09 0
## 434 2019-03-10 0
## 435 2019-03-11 0
## 436 2019-03-12 0
## 437 2019-03-13 1
## 438 2019-03-14 0
## 439 2019-03-15 0
## 440 2019-03-16 0
## 441 2019-03-17 0
## 442 2019-03-18 1
## 443 2019-03-19 0
## 444 2019-03-20 0
## 445 2019-03-21 1
## 446 2019-03-22 0
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## 1209 2021-04-23 0
## 1210 2021-04-24 0
## 1211 2021-04-25 0
## 1212 2021-04-26 0
## 1213 2021-04-27 0
## 1214 2021-04-28 0
## 1215 2021-04-29 0
## 1216 2021-04-30 0
## 1217 2021-05-01 0
## 1218 2021-05-02 0
## 1219 2021-05-03 0
## 1220 2021-05-04 0
## 1221 2021-05-05 0
## 1222 2021-05-06 0
## 1223 2021-05-07 0
## 1224 2021-05-08 0
## 1225 2021-05-09 0
## 1226 2021-05-10 0
## 1227 2021-05-11 0
## 1228 2021-05-12 0
## 1229 2021-05-13 0
## 1230 2021-05-14 0
## 1231 2021-05-15 0
## 1232 2021-05-16 0
## 1233 2021-05-17 0
## 1234 2021-05-18 0
## 1235 2021-05-19 0
## 1236 2021-05-20 0
## 1237 2021-05-21 0
## 1238 2021-05-22 0
## 1239 2021-05-23 0
## 1240 2021-05-24 0
## 1241 2021-05-25 0
## 1242 2021-05-26 0
## 1243 2021-05-27 0
## 1244 2021-05-28 0
## 1245 2021-05-29 0
## 1246 2021-05-30 0
## 1247 2021-05-31 0
## 1248 2021-06-01 0
## 1249 2021-06-02 0
## 1250 2021-06-03 0
## 1251 2021-06-04 0
## 1252 2021-06-05 0
## 1253 2021-06-06 0
## 1254 2021-06-07 0
## 1255 2021-06-08 0
## 1256 2021-06-09 0
## 1257 2021-06-10 0
## 1258 2021-06-11 0
## 1259 2021-06-12 0
## 1260 2021-06-13 1
## 1261 2021-06-14 2
## 1262 2021-06-15 0
## 1263 2021-06-16 0
## 1264 2021-06-17 2
## 1265 2021-06-18 0
## 1266 2021-06-19 0
## 1267 2021-06-20 0
## 1268 2021-06-21 0
## 1269 2021-06-22 0
## 1270 2021-06-23 0
## 1271 2021-06-24 0
## 1272 2021-06-25 1
## 1273 2021-06-26 0
## 1274 2021-06-27 0
## 1275 2021-06-28 0
## 1276 2021-06-29 0
## 1277 2021-06-30 1
## 1278 2021-07-01 0
## 1279 2021-07-02 0
## 1280 2021-07-03 0
## 1281 2021-07-04 0
## 1282 2021-07-05 0
## 1283 2021-07-06 1
## 1284 2021-07-07 0
## 1285 2021-07-08 1
## 1286 2021-07-09 0
## 1287 2021-07-10 0
## 1288 2021-07-11 0
## 1289 2021-07-12 0
## 1290 2021-07-13 0
## 1291 2021-07-14 0
## 1292 2021-07-15 0
## 1293 2021-07-16 0
## 1294 2021-07-17 0
## 1295 2021-07-18 0
## 1296 2021-07-19 0
## 1297 2021-07-20 0
## 1298 2021-07-21 0
## 1299 2021-07-22 0
## 1300 2021-07-23 0
## 1301 2021-07-24 0
## 1302 2021-07-25 0
## 1303 2021-07-26 0
## 1304 2021-07-27 0
## 1305 2021-07-28 0
## 1306 2021-07-29 0
## 1307 2021-07-30 0
## 1308 2021-07-31 0
## 1309 2021-08-01 0
## 1310 2021-08-02 0
## 1311 2021-08-03 0
## 1312 2021-08-04 0
## 1313 2021-08-05 0
## 1314 2021-08-06 0
## 1315 2021-08-07 0
## 1316 2021-08-08 0
## 1317 2021-08-09 0
## 1318 2021-08-10 0
## 1319 2021-08-11 0
## 1320 2021-08-12 0
## 1321 2021-08-13 0
## 1322 2021-08-14 0
## 1323 2021-08-15 1
## 1324 2021-08-16 0
## 1325 2021-08-17 0
## 1326 2021-08-18 0
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## 1328 2021-08-20 0
## 1329 2021-08-21 0
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## 1331 2021-08-23 1
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## 1333 2021-08-25 0
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## 1336 2021-08-28 0
## 1337 2021-08-29 0
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## 1339 2021-08-31 0
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## 1341 2021-09-02 0
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## 1345 2021-09-06 0
## 1346 2021-09-07 0
## 1347 2021-09-08 0
## 1348 2021-09-09 1
## 1349 2021-09-10 1
## 1350 2021-09-11 0
## 1351 2021-09-12 1
## 1352 2021-09-13 0
## 1353 2021-09-14 1
## 1354 2021-09-15 1
## 1355 2021-09-16 0
## 1356 2021-09-17 1
## 1357 2021-09-18 0
## 1358 2021-09-19 1
## 1359 2021-09-20 1
## 1360 2021-09-21 4
## 1361 2021-09-22 0
## 1362 2021-09-23 0
## 1363 2021-09-24 0
## 1364 2021-09-25 0
## 1365 2021-09-26 0
## 1366 2021-09-27 0
## 1367 2021-09-28 0
## 1368 2021-09-29 0
## 1369 2021-09-30 0
## 1370 2021-10-01 0
## 1371 2021-10-02 0
## 1372 2021-10-03 0
## 1373 2021-10-04 0
## 1374 2021-10-05 0
## 1375 2021-10-06 0
## 1376 2021-10-07 0
## 1377 2021-10-08 0
## 1378 2021-10-09 0
## 1379 2021-10-10 0
## 1380 2021-10-11 0
## 1381 2021-10-12 0
## 1382 2021-10-13 0
## 1383 2021-10-14 1
## 1384 2021-10-15 0
## 1385 2021-10-16 0
## 1386 2021-10-17 0
## 1387 2021-10-18 0
## 1388 2021-10-19 0
## 1389 2021-10-20 0
## 1390 2021-10-21 0
## 1391 2021-10-22 0
## 1392 2021-10-23 0
## 1393 2021-10-24 0
## 1394 2021-10-25 0
## 1395 2021-10-26 1
## 1396 2021-10-27 0
## 1397 2021-10-28 0
## 1398 2021-10-29 1
## 1399 2021-10-30 0
## 1400 2021-10-31 0
## 1401 2021-11-01 0
## 1402 2021-11-02 0
## 1403 2021-11-03 0
## 1404 2021-11-04 0
## 1405 2021-11-05 0
## 1406 2021-11-06 0
## 1407 2021-11-07 0
## 1408 2021-11-08 0
## 1409 2021-11-09 0
## 1410 2021-11-10 0
## 1411 2021-11-11 0
## 1412 2021-11-12 0
## 1413 2021-11-13 0
## 1414 2021-11-14 0
## 1415 2021-11-15 0
## 1416 2021-11-16 0
## 1417 2021-11-17 0
## 1418 2021-11-18 0
## 1419 2021-11-19 0
## 1420 2021-11-20 0
## 1421 2021-11-21 0
## 1422 2021-11-22 0
## 1423 2021-11-23 0
## 1424 2021-11-24 0
## 1425 2021-11-25 0
## 1426 2021-11-26 0
## 1427 2021-11-27 0
## 1428 2021-11-28 0
## 1429 2021-11-29 0
## 1430 2021-11-30 0
## 1431 2021-12-01 0
## 1432 2021-12-02 0
## 1433 2021-12-03 0
## 1434 2021-12-04 0
## 1435 2021-12-05 0
## 1436 2021-12-06 0
## 1437 2021-12-07 0
## 1438 2021-12-08 0
## 1439 2021-12-09 0
## 1440 2021-12-10 0
## 1441 2021-12-11 0
## 1442 2021-12-12 0
## 1443 2021-12-13 0
## 1444 2021-12-14 0
## 1445 2021-12-15 0
## 1446 2021-12-16 0
## 1447 2021-12-17 0
## 1448 2021-12-18 0
## 1449 2021-12-19 0
## 1450 2021-12-20 0
## 1451 2021-12-21 0
## 1452 2021-12-22 0
## 1453 2021-12-23 0
## 1454 2021-12-24 0
## 1455 2021-12-25 0
## 1456 2021-12-26 0
## 1457 2021-12-27 0
## 1458 2021-12-28 0
## 1459 2021-12-29 0
## 1460 2021-12-30 0
## 1461 2021-12-31 0
## 1462 2022-01-01 0
## 1463 2022-01-02 0
## 1464 2022-01-03 0
## 1465 2022-01-04 0
## 1466 2022-01-05 0
## 1467 2022-01-06 0
## 1468 2022-01-07 0
## 1469 2022-01-08 0
## 1470 2022-01-09 0
## 1471 2022-01-10 0
## 1472 2022-01-11 0
## 1473 2022-01-12 0
## 1474 2022-01-13 0
## 1475 2022-01-14 0
## 1476 2022-01-15 0
## 1477 2022-01-16 0
## 1478 2022-01-17 0
## 1479 2022-01-18 0
## 1480 2022-01-19 0
## 1481 2022-01-20 0
## 1482 2022-01-21 0
## 1483 2022-01-22 0
## 1484 2022-01-23 0
## 1485 2022-01-24 0
## 1486 2022-01-25 0
## 1487 2022-01-26 0
## 1488 2022-01-27 0
## 1489 2022-01-28 0
## 1490 2022-01-29 0
## 1491 2022-01-30 0
## 1492 2022-01-31 0
## 1493 2022-02-01 0
## 1494 2022-02-02 0
## 1495 2022-02-03 0
## 1496 2022-02-04 0
## 1497 2022-02-05 0
## 1498 2022-02-06 0
## 1499 2022-02-07 0
## 1500 2022-02-08 0
## 1501 2022-02-09 0
## 1502 2022-02-10 0
## 1503 2022-02-11 0
## 1504 2022-02-12 0
## 1505 2022-02-13 0
## 1506 2022-02-14 1
## 1507 2022-02-15 0
## 1508 2022-02-16 1
## 1509 2022-02-17 1
## 1510 2022-02-18 0
## 1511 2022-02-19 0
## 1512 2022-02-20 1
## 1513 2022-02-21 0
## 1514 2022-02-22 0
## 1515 2022-02-23 0
## 1516 2022-02-24 0
## 1517 2022-02-25 0
## 1518 2022-02-26 0
## 1519 2022-02-27 0
## 1520 2022-02-28 0
## 1521 2022-03-01 0
## 1522 2022-03-02 0
## 1523 2022-03-03 0
## 1524 2022-03-04 0
## 1525 2022-03-05 0
## 1526 2022-03-06 0
## 1527 2022-03-07 0
## 1528 2022-03-08 0
## 1529 2022-03-09 0
## 1530 2022-03-10 0
## 1531 2022-03-11 0
## 1532 2022-03-12 0
## 1533 2022-03-13 0
## 1534 2022-03-14 0
## 1535 2022-03-15 0
## 1536 2022-03-16 0
## 1537 2022-03-17 0
## 1538 2022-03-18 0
## 1539 2022-03-19 0
## 1540 2022-03-20 0
## 1541 2022-03-21 0
## 1542 2022-03-22 1
## 1543 2022-03-23 0
## 1544 2022-03-24 0
## 1545 2022-03-25 0
## 1546 2022-03-26 0
## 1547 2022-03-27 0
## 1548 2022-03-28 1
## 1549 2022-03-29 0
## 1550 2022-03-30 0
## 1551 2022-03-31 0
## 1552 2022-04-01 0
## 1553 2022-04-02 0
## 1554 2022-04-03 0
## 1555 2022-04-04 0
## 1556 2022-04-05 0
## 1557 2022-04-06 0
## 1558 2022-04-07 0
## 1559 2022-04-08 0
## 1560 2022-04-09 0
## 1561 2022-04-10 0
## 1562 2022-04-11 0
## 1563 2022-04-12 0
## 1564 2022-04-13 0
## 1565 2022-04-14 0
## 1566 2022-04-15 0
## 1567 2022-04-16 0
## 1568 2022-04-17 0
## 1569 2022-04-18 0
## 1570 2022-04-19 0
## 1571 2022-04-20 0
## 1572 2022-04-21 0
## 1573 2022-04-22 0
## 1574 2022-04-23 0
## 1575 2022-04-24 0
## 1576 2022-04-25 0
## 1577 2022-04-26 0
## 1578 2022-04-27 0
## 1579 2022-04-28 0
## 1580 2022-04-29 0
## 1581 2022-04-30 0
## 1582 2022-05-01 0
## 1583 2022-05-02 0
## 1584 2022-05-03 0
## 1585 2022-05-04 0
## 1586 2022-05-05 0
## 1587 2022-05-06 0
## 1588 2022-05-07 0
## 1589 2022-05-08 1
## 1590 2022-05-09 0
## 1591 2022-05-10 0
## 1592 2022-05-11 0
## 1593 2022-05-12 0
## 1594 2022-05-13 0
## 1595 2022-05-14 0
## 1596 2022-05-15 0
## 1597 2022-05-16 0
## 1598 2022-05-17 0
## 1599 2022-05-18 0
## 1600 2022-05-19 0
## 1601 2022-05-20 0
## 1602 2022-05-21 0
## 1603 2022-05-22 0
## 1604 2022-05-23 0
## 1605 2022-05-24 0
## 1606 2022-05-25 0
## 1607 2022-05-26 0
## 1608 2022-05-27 0
## 1609 2022-05-28 0
## 1610 2022-05-29 0
## 1611 2022-05-30 0
## 1612 2022-05-31 1
## 1613 2022-06-01 0
## 1614 2022-06-02 0
## 1615 2022-06-03 0
## 1616 2022-06-04 0
## 1617 2022-06-05 0
## 1618 2022-06-06 0
## 1619 2022-06-07 0
## 1620 2022-06-08 0
## 1621 2022-06-09 0
## 1622 2022-06-10 0
## 1623 2022-06-11 0
## 1624 2022-06-12 0
## 1625 2022-06-13 0
## 1626 2022-06-14 0
## 1627 2022-06-15 0
## 1628 2022-06-16 0Part 3: Calculate the average of all days for the above-mentioned period from “No. of articles”.
print(paste0("The average number of daily articles between ", as.character(historical_dates[1])
, " and ", as.character(historical_dates[days]), " is ", as.character(mean(df.daily_article_count$`No. of articles`))))## [1] "The average number of daily articles between 2018-01-01 and 2022-06-16 is 0.128992628992629"Part 4: In which section are most articles written?
sqldf('SELECT sectionName, count(*)
FROM "df.article_info"
WHERE webPublicationDate>="2018-01-01"
GROUP BY sectionName
ORDER BY 2 desc')## sectionName count(*)
## 1 World news 146
## 2 Opinion 23
## 3 US news 12
## 4 Environment 5
## 5 Politics 5
## 6 Business 4
## 7 UK news 4
## 8 Fashion 2
## 9 Global development 2
## 10 News 2
## 11 Cities 1
## 12 Culture 1
## 13 Membership 1
## 14 Sport 1
## 15 Technology 1answer_4<- sqldf('SELECT sectionName, count(*)
FROM "df.article_info"
WHERE webPublicationDate>="2018-01-01"
GROUP BY sectionName
ORDER BY 2 desc
LIMIT 1')
print(paste0("The section with the most number of articles in the period of interest is " ,answer_4[[1]],
" with ", answer_4[[2]], " total articles."))## [1] "The section with the most number of articles in the period of interest is World news with 146 total articles."Part 5: Show the evolution of the “No. of articles” over time for the above period.
##theme_set(theme_classic())
# Plot
g <- ggplot(df.daily_article_count, aes(Date, `No. of articles`))
g + geom_bar(stat="identity", width = 0.5, fill="darkblue") +
labs(title="No. of Daily Articles Since 2018-01-01",
subtitle="Justin Trudeau",
caption="Source: Guardian Media Group API") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
## The above chart is hard to read since there are so many dates on the x axis--> let's also look at the data grouped by month
## first create a new month column for this plot
df.daily_article_count$Year_Month<-paste0(lubridate::year(df.daily_article_count$Date), " " ,month.abb[month(df.daily_article_count$Date)])
## Plot the data
g2 <- ggplot(df.daily_article_count, aes(Year_Month, `No. of articles`))
g2 + geom_bar(stat="sum", width = 0.5, fill="darkblue") +
labs(title="No. of Daily Articles Since 2018-01-01",
subtitle="Justin Trudeau",
caption="Source: Guardian Media Group API") +
theme(axis.text.x = element_text(angle=65, vjust=0.6), legend.position="none") ## makes x axis readable
Part 6: Are there any unusual events in the time series under investigation?
In the daily plot (although hard to read), we can see there are specific dates that have higher numbers of articles. In the monthly plot, we can see which months these spikes occur in. Let’s create a heatmap for easier identification.
## first let's create a new dataframe with added date variables we will need
df.daily_article_count_plot<-df.daily_article_count
df.daily_article_count_plot$Date <- as.Date(df.daily_article_count_plot$Date) # format date
# Create Month Week
df.daily_article_count_plot$year <- year(df.daily_article_count_plot$Date)
df.daily_article_count_plot$month <- month(df.daily_article_count_plot$Date)
df.daily_article_count_plot$monthf <- factor(month(df.daily_article_count_plot$Date))
df.daily_article_count_plot$yearmonth <- as.yearmon(df.daily_article_count_plot$Date)
df.daily_article_count_plot$yearmonthf <- factor(df.daily_article_count_plot$yearmonth)
df.daily_article_count_plot$week<- lubridate::week(ymd(df.daily_article_count_plot$Date))
df.daily_article_count_plot <- ddply(df.daily_article_count_plot,.(yearmonthf), transform, monthweek=1+week-min(week)) # compute week number of month
df.daily_article_count_plot$weekday<-weekdays(as.Date(df.daily_article_count_plot$Date))
df.daily_article_count_plot$weekdayf<-factor(df.daily_article_count_plot$weekday)
# Plot
ggplot(df.daily_article_count_plot, aes(monthweek, weekdayf, fill = No..of.articles)) +
geom_tile(colour = "white") +
facet_grid(year~monthf) +
scale_fill_gradient(low="green", high="red") +
labs(x="Week of Month",
y="",
title="No. of Monthly Articles Since 2018-01-01",
subtitle="Justin Trudeau",
fill="Daily Articles")
Part 7: If so, show these. Why are these unusual? (Define for yourself what you want to show by ordinary or unusual).
From the Heat Map above we can define “ordinary” by the colors which are most prevelant. We can see that most squares are green and many are also blank, meaning the typical behavior is for only 1 or no articles being published on a given day. Then we can see that on specific days the squares are shades of red, indicating that there are more articles then typical on this day. The darker the square, the more unusual.
There is one day in particular that is the darkest red of all! This day is in June 2018 and has 6 articles on a single day. There are also a few other unusual days where we can see there are 4 or 3 articles a day.
Part 8: Based on question one. Show the cause of the unusual event.
Now that we know which time and number of articles are related to the most unusal day, we can look at the original data pulled in part 1 to find out what could be the cause. Please note, the same logic could be used to look into the other adnormal days (i.e. days with 4 articles).
## check the date with 6 articles
df.daily_article_count[df.daily_article_count$`No. of articles`==6,][1]## Date
## 162 2018-06-11# Pull the Titles of the articles on this day for a good overview of the content
events<-subset(df.article_info,df.article_info$webPublicationDate=='2018-06-11')
print(events$webTitle)## [1] 'Prepare for the worst': souring Canada-US relations fuel worries of trade war
## [2] Hanger-on May is no more than a bit player at the G7 summit | John Crace
## [3] Canada and America are cousins. We don't stab each other in the back | Margaret MacMillan
## [4] Trump, Merkel, Macron: the G7 photos worth a thousand words | Hannah Jane Parkinson
## [5] Trudeau 'stabbed us in back' on trade, says Trump chief economic adviser
## [6] Trump is a bully who thought Canada was weak. He was wrong about us | Jen Gerson
## 395 Levels: 'Give her whatever she wants': readers on Sophie Grégoire Trudeau's staffing plea | Guardian readers and Sarah Marsh ...From the titles, we can already see that the G7 summit had taking place and that there was conflict between Trump and Trudeau (Us v Canada).
*Should we want to know more specifics (i.e. to find the conflict was regaurding Trudeau declaring he would fight back against the US’s new tarrifs on aluminum and steel), we could pull the full content of these 6 articles and do a text analysis.
Bonus: Let’s say there had been more than 6 articles (i.e. 100+) and we wanted to process all the information easier. We could create a word cloud of the titles to find the main topics. Below is an example:
## first we need to prepare the text for processing
titles<-c()
for (a in 1:length(events$webTitle))
{
titles_level<-events$webTitle[[a]]
titles<-cbind(titles,levels(events$webTitle)[titles_level])
}
ctext = Corpus(VectorSource(titles))
## next let's remove potential words and punctuation which are not helpful
dropWords = c("is","and","then","a","the","in","he","she","are","was", "were","of","for")
ctext = tm_map(ctext,tolower)
ctext = tm_map(ctext,removePunctuation)
ctext = tm_map(ctext,removeWords,stopwords("english"))
inspect(ctext)## <<SimpleCorpus>>
## Metadata: corpus specific: 1, document level (indexed): 0
## Content: documents: 6
##
## [1] prepare worst souring canadaus relations fuel worries trade war
## [2] hangeron may bit player g7 summit john crace
## [3] canada america cousins dont stab back margaret macmillan
## [4] trump merkel macron g7 photos worth thousand words hannah jane parkinson
## [5] trudeau stabbed us back trade says trump chief economic adviser
## [6] trump bully thought canada weak wrong us jen gersontdm = TermDocumentMatrix(ctext,control=list(minWordLength=1))
tdm2 = as.matrix(tdm)
wordcount = sort(rowSums(tdm2),decreasing=TRUE)
tdm_names = names(wordcount)
wordcloud(tdm_names,wordcount,min.freq = 2)
Using the wordcloud we can already see the unusual event was related to Trump and trade! However, G7 is not apear because it is not a real word, which is a downside of this method.
Automation
Create a daily automated job that updates question 5 daily and creates an output that could be sent
##devtools::install_github("rstudio/addinexamples", type = "source")