if (!require(XML)) install.packages('XML')## Loading required package: XML## Warning: package 'XML' was built under R version 3.1.3if (!require(dplyr)) install.packages('dplyr')## Loading required package: dplyr## Warning: package 'dplyr' was built under R version 3.1.3##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionif (!require(rvest)) install.packages("rvest")## Loading required package: rvest## Warning: package 'rvest' was built under R version 3.1.3##
## Attaching package: 'rvest'
##
## The following object is masked from 'package:XML':
##
## xmlif (!require(tidyr)) install.packages('tidyr')## Loading required package: tidyr## Warning: package 'tidyr' was built under R version 3.1.3if (!require(RCurl)) install.packages('RCurl')## Loading required package: RCurl## Warning: package 'RCurl' was built under R version 3.1.3## Loading required package: bitopslibrary(XML)
library(dplyr)
library(rvest)
library(tidyr)
library(RCurl)You can also embed plots, for example:
theURL <- "http://www.jaredlander.com/2012/02/another-kind-of-super-bowl-pool/"
bowlPool <- readHTMLTable(theURL, which = 1, header = FALSE, stringsAsFactors = FALSE)
bowlPool[[1]]## [1] "Participant 1" "Participant 2" "Participant 3" "Participant 4"
## [5] "Participant 5" "Participant 6" "Participant 7" "Participant 8"
## [9] "Participant 9" "Participant 10"# 1. What type of data structure is bowlpool?
# it is data frame.
class(bowlPool)## [1] "data.frame"# Or using str() also provides the type and additional information
str(bowlPool)## 'data.frame': 10 obs. of 3 variables:
## $ V1: chr "Participant 1" "Participant 2" "Participant 3" "Participant 4" ...
## $ V2: chr "Giant A" "Giant B" "Giant C" "Giant D" ...
## $ V3: chr "Patriot Q" "Patriot R" "Patriot S" "Patriot T" ...length(bowlPool)## [1] 3bowlPool[[1]]## [1] "Participant 1" "Participant 2" "Participant 3" "Participant 4"
## [5] "Participant 5" "Participant 6" "Participant 7" "Participant 8"
## [9] "Participant 9" "Participant 10"bowlPool[[2]]## [1] "Giant A" "Giant B" "Giant C" "Giant D" "Giant E" "Giant F" "Giant G"
## [8] "Giant H" "Giant I" "Giant J"bowlPool[[3]]## [1] "Patriot Q" "Patriot R" "Patriot S" "Patriot T" "Patriot U"
## [6] "Patriot V" "Patriot W" "Patriot X" "Patriot Y" "Patriot Z"# 2. Suppose instead you call readHTMLTable() with just the URL argument,
# against the provided URL, as shown below
theURL <- "http://www.w3schools.com/html/html_tables.asp"
hvalues <- readHTMLTable(theURL)
# What is the type of variable returned in hvalues?
# the hvalues is of type list
str(hvalues)## List of 6
## $ NULL:'data.frame': 4 obs. of 4 variables:
## ..$ Number : Factor w/ 4 levels "1","2","3","4": 1 2 3 4
## ..$ First Name: Factor w/ 4 levels "Adam","Eve","Jill",..: 2 4 1 3
## ..$ Last Name : Factor w/ 4 levels "Doe","Jackson",..: 2 1 3 4
## ..$ Points : Factor w/ 4 levels "50","67","80",..: 4 3 2 1
## $ NULL: NULL
## $ NULL: NULL
## $ NULL: NULL
## $ NULL: NULL
## $ NULL:'data.frame': 10 obs. of 2 variables:
## ..$ Tag : Factor w/ 10 levels "<caption>","<col>",..: 4 8 10 6 1 3 2 9 5 7
## ..$ Description: Factor w/ 10 levels "Defines a cell in a table",..: 4 2 3 1 5 9 10 8 6 7class(hvalues)## [1] "list"table_count <- function(x) {
N= length(x)
count01=0
j=0
i=1
tbl_count=0
for (i in 1:N ) { if (class(x[[i]]) == "NULL") j=j+1
else i<- i+1 }
tbl_count <- i-1-j
return(tbl_count)
}table_count(hvalues)## [1] 2# Validating the resultins.. from below, only hvalues[[1]] and hvalues[[6]] return valid tables
hvalues[[1]]## Number First Name Last Name Points
## 1 1 Eve Jackson 94
## 2 2 John Doe 80
## 3 3 Adam Johnson 67
## 4 4 Jill Smith 50hvalues[[2]]## NULLhvalues[[3]]## NULLhvalues[[4]]## NULLhvalues[[5]]## NULLhvalues[[6]]## Tag
## 1 <table>
## 2 <th>
## 3 <tr>
## 4 <td>
## 5 <caption>
## 6 <colgroup>
## 7 <col>
## 8 <thead>
## 9 <tbody>
## 10 <tfoot>
## Description
## 1 Defines a table
## 2 Defines a header cell in a table
## 3 Defines a row in a table
## 4 Defines a cell in a table
## 5 Defines a table caption
## 6 Specifies a group of one or more columns in a table for formatting
## 7 Specifies column properties for each column within a <colgroup> element
## 8 Groups the header content in a table
## 9 Groups the body content in a table
## 10 Groups the footer content in a tabledoc <- htmlParse(theURL)
tableNodes <- getNodeSet(doc, "//table")
tb <- readHTMLTable(tableNodes[[1]])
tb## Number First Name Last Name Points
## 1 1 Eve Jackson 94
## 2 2 John Doe 80
## 3 3 Adam Johnson 67
## 4 4 Jill Smith 50# Or if you know which table has FirstName, LastName, # and Points, just do the below
df_tbl<- data.frame(hvalues[[1]])
df_tbl## Number First.Name Last.Name Points
## 1 1 Eve Jackson 94
## 2 2 John Doe 80
## 3 3 Adam Johnson 67
## 4 4 Jill Smith 50# 5. Modify the returned data frame so only the Last Name and Points columns are shown.
# Using select in dplyr package
select(df_tbl, 3, 4)## Last.Name Points
## 1 Jackson 94
## 2 Doe 80
## 3 Johnson 67
## 4 Smith 50tags or .png files).
url <- "http://en.wikipedia.org/wiki/List_of_countries_by_population"
html_tables = readHTMLTable(url, table_counter=TRUE)# 7 How many HTML tables does that page contain?
# using the table_count function above, there are 4 tables.
length(html_tables)## [1] 5table_count(html_tables)## [1] 4# validating the results. only html_tables[[3]] has value of NULL.
html_tables[[1]]## Rank Country (or dependent territory) Population
## 1 1 China[Note 2] 1,369,040,000
## 2 2 India 1,269,090,000
## 3 3 United States 320,704,000
## 4 4 Indonesia 255,461,700
## 5 5 Brazil 204,100,000
## 6 6 Pakistan 189,388,000
## 7 7 Nigeria 183,523,000
## 8 8 Bangladesh 158,088,000
## 9 9 Russia[Note 3] 146,267,288
## 10 10 Japan 126,910,000
## 11 11 Mexico 121,005,815
## 12 12 Philippines 101,227,000
## 13 13 Vietnam 90,730,000
## 14 14 Ethiopia 90,076,012
## 15 15 Egypt 88,269,400
## 16 16 Germany 80,925,000
## 17 17 Iran 78,229,000
## 18 18 Turkey 77,695,904
## 19 19 Democratic Republic of the Congo 71,246,000
## 20 20 France[Note 4] 66,109,000
## 21 21 Thailand 64,871,000
## 22 22 United Kingdom 64,105,654
## 23 23 Italy 60,788,845
## 24 24 South Africa 54,002,000
## 25 25 Burma 51,419,420
## 26 26 South Korea 51,342,881
## 27 27 Colombia 48,063,200
## 28 28 Tanzania 47,421,786
## 29 29 Kenya 46,749,000
## 30 30 Spain 46,464,053
## 31 31 Argentina 43,131,966
## 32 32 Ukraine[Note 5] 42,928,900
## 33 33 Algeria 39,500,000
## 34 34 Poland 38,484,000
## 35 35 Sudan 38,435,252
## 36 36 Iraq 36,004,552
## 37 37 Canada 35,702,707
## 38 38 Uganda 34,856,813
## 39 39 Morocco[7] 33,337,529
## 40 40 Saudi Arabia 31,521,418
## 41 41 Peru 31,151,643
## 42 42 Venezuela 30,620,404
## 43 43 Malaysia 30,538,100
## 44 44 Uzbekistan 30,492,800
## 45 45 Nepal 28,037,904
## 46 46 Ghana 27,043,093
## 47 47 Afghanistan 26,556,800
## 48 48 Yemen 25,956,000
## 49 49 Mozambique 25,727,911
## 50 50 North Korea 25,155,000
## 51 51 Angola 24,383,301
## 52 52 Australia 23,795,300
## 53 53 Taiwan[Note 6] 23,445,534
## 54 54 Syria 23,126,857
## 55 55 Ivory Coast 22,671,331
## 56 56 Madagascar 21,842,167
## 57 57 Cameroon 21,143,237
## 58 58 Sri Lanka 20,675,000
## 59 59 Romania 19,942,642
## 60 60 Niger 19,268,000
## 61 61 Burkina Faso 18,450,494
## 62 62 Chile 18,006,407
## 63 63 Kazakhstan 17,439,300
## 64 64 Netherlands 16,896,200
## 65 65 Malawi 16,310,431
## 66 66 Mali 16,259,000
## 67 67 Ecuador 15,963,300
## 68 68 Guatemala 15,806,675
## 69 69 Zambia 15,473,905
## 70 70 Cambodia 15,405,157
## 71 71 Chad 13,606,000
## 72 72 Senegal 13,508,715
## 73 73 Zimbabwe 13,061,239
## 74 74 South Sudan 11,892,934
## 75 75 Bolivia 11,410,651
## 76 76 Belgium 11,239,755
## 77 77 Cuba 11,210,064
## 78 78 Somalia[Note 8] 11,123,000
## 79 79 Rwanda 10,996,891
## 80 80 Greece 10,992,589
## 81 81 Tunisia 10,982,754
## 82 82 Haiti 10,911,819
## 83 83 Guinea 10,628,972
## 84 84 Czech Republic 10,528,477
## 85 85 Portugal 10,477,800
## 86 86 Dominican Republic 10,378,267
## 87 87 Benin 10,315,244
## 88 88 Hungary 9,849,000
## 89 89 Burundi 9,823,827
## 90 90 Sweden 9,753,627
## 91 91 Azerbaijan 9,593,000
## 92 92 United Arab Emirates 9,577,000
## 93 93 Belarus 9,481,000
## 94 94 Honduras 8,725,111
## 95 95 Austria 8,579,747
## 96 96 Tajikistan 8,354,000
## 97 97 Israel 8,320,100
## 98 98 Switzerland 8,211,700
## 99 99 Papua New Guinea 7,398,500
## 100 100 Hong Kong (China) 7,264,100
## 101 101 Bulgaria 7,245,677
## 102 102 Togo 7,171,000
## 103 103 Serbia[Note 9] 7,146,759
## 104 104 Paraguay 7,003,406
## 105 105 Laos 6,802,000
## 106 106 Eritrea 6,738,000
## 107 107 Jordan 6,708,370
## 108 108 El Salvador 6,401,240
## 109 109 Sierra Leone 6,319,000
## 110 110 Libya 6,317,000
## 111 111 Nicaragua 6,134,270
## 112 112 Kyrgyzstan 5,895,100
## 113 113 Denmark 5,659,715
## 114 114 Finland 5,475,526
## 115 115 Singapore 5,469,700
## 116 116 Slovakia 5,421,034
## 117 117 Norway 5,165,802
## 118 118 Central African Republic 4,803,000
## 119 119 Costa Rica 4,773,130
## 120 120 Turkmenistan 4,751,120
## 121 121 Republic of the Congo 4,671,000
## 122 122 Ireland 4,609,600
## 123 123 New Zealand 4,572,100
## 124 124 Palestine 4,550,368
## 125 125 Liberia 4,503,000
## 126 126 Georgia[Note 10] 4,490,500
## 127 127 Croatia 4,267,558
## 128 128 Oman 4,146,558
## 129 129 Lebanon 4,104,000
## 130 130 Bosnia and Herzegovina 3,791,622
## 131 131 Panama 3,764,166
## 132 132 Mauritania 3,631,775
## 133 133 Moldova[Note 11] 3,557,600
## 134 134 Puerto Rico (U.S.) 3,548,397
## 135 135 Uruguay 3,404,189
## 136 136 Kuwait 3,268,431
## 137 137 Armenia 3,013,900
## 138 138 Mongolia 3,000,000
## 139 139 Lithuania 2,919,306
## 140 140 Albania 2,893,005
## 141 141 Jamaica 2,717,991
## 142 142 Qatar 2,334,029
## 143 143 Lesotho 2,120,000
## 144 144 Namibia 2,113,077
## 145 145 Macedonia 2,065,769
## 146 146 Slovenia 2,066,143
## 147 147 Botswana 2,024,904
## 148 148 Latvia 1,986,700
## 149 149 The Gambia 1,882,450
## 150 150 Kosovo[Note 12] 1,827,231
## 151 151 Guinea-Bissau 1,788,000
## 152 152 Gabon 1,751,000
## 153 153 Equatorial Guinea 1,430,000
## 154 154 Trinidad and Tobago 1,328,019
## 155 155 Bahrain 1,316,500
## 156 156 Estonia 1,312,252
## 157 157 Mauritius 1,261,208
## 158 158 East Timor 1,212,107
## 159 159 Swaziland 1,119,375
## 160 160 Djibouti 900,000
## 161 161 Fiji 859,178
## 162 162 Cyprus[Note 13] 858,000
## 163 163 Réunion (France) 844,994
## 164 164 Comoros 763,952
## 165 165 Bhutan 758,880
## 166 166 Guyana 746,900
## 167 167 Macau (China) 636,200
## 168 168 Montenegro 620,029
## 169 169 Solomon Islands 581,344
## 170 170 Luxembourg 549,700
## 171 171 Suriname 534,189
## 172 172 Cape Verde 518,467
## 173 173 Western Sahara[Note 14] 510,713
## 174 174 Transnistria[Note 15] 505,153
## 175 175 Malta 425,384
## 176 176 Guadeloupe (France) 405,739
## 177 177 Brunei 393,372
## 178 178 Martinique (France) 381,326
## 179 179 The Bahamas 368,390
## 180 180 Belize 358,899
## 181 181 Maldives 341,256
## 182 182 Iceland 329,100
## 183 183 Northern Cyprus[Note 16] 294,906
## 184 184 Barbados 285,000
## 185 185 New Caledonia (France) 268,767
## 186 186 French Polynesia (France) 268,270
## 187 187 Vanuatu 264,652
## 188 188 Abkhazia[Note 17] 240,705
## 189 189 French Guiana (France) 239,648
## 190 190 Mayotte (France) 212,645
## 191 191 Samoa 187,820
## 192 192 São Tomé and Príncipe 187,356
## 193 193 Saint Lucia 185,000
## 194 194 Guam (U.S.) 159,358
## 195 195 Curaçao (Netherlands) 154,843
## 196 196 Saint Vincent and the Grenadines 109,000
## 197 197 Aruba (Netherlands) 107,394
## 198 198 Kiribati 106,461
## 199 199 United States Virgin Islands (U.S.) 106,405
## 200 200 Grenada 103,328
## 201 201 Tonga 103,252
## 202 202 Federated States of Micronesia 101,351
## 203 203 Jersey (UK) 99,000
## 204 204 Seychelles 89,949
## 205 205 Antigua and Barbuda 86,295
## 206 206 Isle of Man (UK) 84,497
## 207 207 Andorra 76,949
## 208 208 Dominica 71,293
## 209 209 Bermuda (UK) 64,237
## 210 210 Guernsey (UK) 63,085
## 211 211 Marshall Islands 56,086
## 212 212 Greenland (Denmark) 55,984
## 213 213 Cayman Islands (UK) 55,691
## 214 214 American Samoa (U.S.) 55,519
## 215 215 Saint Kitts and Nevis 55,000
## 216 216 Northern Mariana Islands (U.S.) 53,883
## 217 217 South Ossetia[Note 18] 51,547
## 218 218 Faroe Islands (Denmark) 48,724
## 219 219 Sint Maarten (Netherlands) 37,429
## 220 220 Liechtenstein 37,370
## 221 221 Monaco 36,950
## 222 222 Collectivity of Saint Martin (France) 35,742
## 223 223 San Marino 32,789
## 224 224 Turks and Caicos Islands (UK) 31,458
## 225 225 Gibraltar (UK) 30,001
## 226 226 Åland Islands (Finland) 28,875
## 227 227 British Virgin Islands (UK) 28,054
## 228 228 Caribbean Netherlands (Netherlands) 23,296
## 229 229 Palau 20,901
## 230 230 Cook Islands (New Zealand) 14,974
## 231 231 Anguilla (UK) 13,452
## 232 232 Wallis and Futuna (France) 13,135
## 233 233 Tuvalu 11,323
## 234 234 Nauru 10,084
## 235 235 Saint Barthélemy (France) 9,131
## 236 236 Saint Pierre and Miquelon (France) 6,069
## 237 237 Montserrat (UK) 4,922
## 238 238 Saint Helena, Ascension and Tristan da Cunha (UK) 4,000
## 239 239 Falkland Islands (UK) 3,000
## 240 240 Svalbard and Jan Mayen (Norway) 2,562
## 241 241 Norfolk Island (Australia) 2,302
## 242 242 Christmas Island (Australia) 2,072
## 243 243 Niue (New Zealand) 1,613
## 244 244 Tokelau (NZ) 1,411
## 245 245 Vatican City 839
## 246 246 Cocos (Keeling) Islands (Australia) 550
## 247 247 Pitcairn Islands (UK) 56
## Date % of world\npopulation
## 1 April 1, 2015 18.9%
## 2 April 1, 2015 17.5%
## 3 April 1, 2015 4.43%
## 4 July 1, 2015 3.53%
## 5 April 1, 2015 2.82%
## 6 April 1, 2015 2.62%
## 7 July 1, 2015 2.54%
## 8 April 1, 2015 2.19%
## 9 January 1, 2015 2.02%
## 10 March 1, 2015 1.75%
## 11 July 1, 2015 1.67%
## 12 April 1, 2015 1.4%
## 13 July 1, 2014 1.25%
## 14 July 1, 2015 1.25%
## 15 April 1, 2015 1.22%
## 16 June 30, 2014 1.12%
## 17 April 1, 2015 1.08%
## 18 December 31, 2014 1.07%
## 19 July 1, 2015 0.98%
## 20 March 1, 2015 0.91%
## 21 July 1, 2014 0.9%
## 22 July 1, 2013 0.89%
## 23 November 30, 2014 0.84%
## 24 July 1, 2014 0.75%
## 25 March 29, 2014 0.71%
## 26 January 1, 2015 0.71%
## 27 April 1, 2015 0.664%
## 28 July 1, 2014 0.66%
## 29 July 1, 2015 0.65%
## 30 July 1, 2014 0.64%
## 31 July 1, 2015 0.6%
## 32 January 1, 2015 0.59%
## 33 January 1, 2015 0.55%
## 34 December 31, 2014 0.53%
## 35 July 1, 2015 0.53%
## 36 July 1, 2014 0.5%
## 37 January 1, 2015 0.49%
## 38 August 28, 2014 0.48%
## 39 September 1, 2014 0.46%
## 40 July 1, 2015 0.44%
## 41 July 1, 2015 0.43%
## 42 July 1, 2015 0.42%
## 43 April 1, 2015 0.422%
## 44 January 1, 2014 0.42%
## 45 July 1, 2015 0.39%
## 46 July 1, 2014 0.37%
## 47 July 1, 2014 0.37%
## 48 July 1, 2014 0.36%
## 49 July 1, 2015 0.36%
## 50 July 1, 2015 0.35%
## 51 May 16, 2014 0.34%
## 52 April 1, 2015 0.329%
## 53 February 28, 2015 0.32%
## 54 April 1, 2015 0.32%
## 55 May 15, 2014 0.31%
## 56 July 1, 2013 0.3%
## 57 July 1, 2013 0.28%
## 58 July 1, 2014 0.28%
## 59 January 1, 2014 0.28%
## 60 July 1, 2015 0.27%
## 61 July 1, 2015 0.26%
## 62 July 1, 2015 0.25%
## 63 February 1, 2015 0.24%
## 64 April 1, 2015 0.234%
## 65 July 1, 2015 0.23%
## 66 July 1, 2015 0.22%
## 67 April 1, 2015 0.22%
## 68 July 1, 2014 0.22%
## 69 July 1, 2015 0.21%
## 70 July 1, 2015 0.21%
## 71 July 1, 2015 0.19%
## 72 November 19, 2013 0.19%
## 73 August 17, 2012 0.18%
## 74 July 1, 2015 0.16%
## 75 July 1, 2015 0.16%
## 76 February 1, 2015 0.16%
## 77 December 31, 2013 0.15%
## 78 July 1, 2015 0.15%
## 79 July 1, 2014 0.15%
## 80 January 1, 2014 0.15%
## 81 April 23, 2014 0.15%
## 82 July 1, 2015 0.15%
## 83 April 2, 2014 0.15%
## 84 September 30, 2014 0.15%
## 85 December 31, 2013 0.14%
## 86 July 1, 2014 0.14%
## 87 July 1, 2015 0.14%
## 88 December 31, 2014 0.14%
## 89 July 1, 2015 0.14%
## 90 January 31, 2015 0.13%
## 91 January 1, 2015 0.13%
## 92 July 1, 2015 0.13%
## 93 January 1, 2015 0.13%
## 94 July 1, 2014 0.12%
## 95 January 1, 2015 0.12%
## 96 January 1, 2015 0.12%
## 97 February 28, 2015 0.12%
## 98 September 30, 2014 0.11%
## 99 July 1, 2013 0.102%
## 100 December 31, 2014 0.1%
## 101 December 31, 2013 0.1%
## 102 July 1, 2015 0.099%
## 103 January 1, 2014 0.099%
## 104 2015 0.097%
## 105 July 1, 2015 0.094%
## 106 July 1, 2015 0.093%
## 107 April 1, 2015 0.0927%
## 108 2014 0.088%
## 109 July 1, 2015 0.087%
## 110 July 1, 2015 0.087%
## 111 2013 0.085%
## 112 January 1, 2015 0.081%
## 113 January 1, 2015 0.078%
## 114 February 1, 2015 0.076%
## 115 July 1, 2014 0.076%
## 116 September 30, 2014 0.075%
## 117 January 1, 2015 0.071%
## 118 July 1, 2015 0.066%
## 119 June 30, 2014 0.066%
## 120 December 26, 2012 0.066%
## 121 July 1, 2015 0.065%
## 122 April 1, 2014 0.064%
## 123 April 1, 2015 0.0632%
## 124 July 1, 2014 0.063%
## 125 July 1, 2015 0.062%
## 126 January 1, 2014 0.062%
## 127 July 1, 2012 0.059%
## 128 March 18, 2015 0.057%
## 129 July 1, 2012 0.057%
## 130 October 15, 2013 0.052%
## 131 July 1, 2015 0.05%
## 132 July 1, 2015 0.05%
## 133 January 1, 2014 0.049%
## 134 July 1, 2014 0.049%
## 135 June 30, 2014 0.047%
## 136 July 1, 2012 0.045%
## 137 September 30, 2014 0.042%
## 138 January 24, 2015 0.041%
## 139 February 1, 2014 0.04%
## 140 January 1, 2015 0.04%
## 141 December 31, 2013 0.038%
## 142 February 28, 2015 0.032%
## 143 July 1, 2015 0.029%
## 144 August 28, 2011 0.029%
## 145 December 31, 2013 0.029%
## 146 April 1, 2015 0.029%
## 147 August 22, 2011 0.028%
## 148 February 1, 2015 0.027%
## 149 April 15, 2013 0.026%
## 150 2015 0.025%
## 151 July 1, 2015 0.025%
## 152 July 1, 2015 0.024%
## 153 July 1, 2013 0.02%
## 154 January 9, 2011 0.018%
## 155 July 1, 2014 0.018%
## 156 January 1, 2015 0.018%
## 157 July 1, 2014 0.017%
## 158 July 1, 2014 0.017%
## 159 July 1, 2015 0.015%
## 160 July 1, 2015 0.012%
## 161 July 1, 2013 0.0119%
## 162 January 1, 2014 0.012%
## 163 January 1, 2014 0.012%
## 164 July 1, 2014 0.011%
## 165 April 1, 2015 0.0105%
## 166 July 1, 2013 0.01%
## 167 December 31, 2014 0.0088%
## 168 April 1, 2011 0.0086%
## 169 July 1, 2013 0.008%
## 170 December 31, 2013 0.0074%
## 171 August 13, 2012 0.0074%
## 172 July 1, 2014 0.0072%
## 173 September 2, 2014 0.0071%
## 174 January 1, 2014 0.007%
## 175 December 31, 2013 0.0059%
## 176 January 1, 2013 0.0056%
## 177 June 20, 2011 0.0054%
## 178 January 1, 2014 0.0053%
## 179 July 1, 2013 0.0051%
## 180 July 1, 2014 0.005%
## 181 September 20, 2014 0.0047%
## 182 January 1, 2015 0.0045%
## 183 April 30, 2006 0.004%
## 184 July 1, 2013 0.0039%
## 185 August 26, 2014 0.0037%
## 186 August 22, 2012 0.0037%
## 187 July 1, 2013 0.0037%
## 188 2011 0.0033%
## 189 January 1, 2012 0.0033%
## 190 August 21, 2012 0.0029%
## 191 November 7, 2011 0.0026%
## 192 May 13, 2012 0.0026%
## 193 July 1, 2015 0.0026%
## 194 April 1, 2010 0.0022%
## 195 January 1, 2014 0.0021%
## 196 July 1, 2015 0.0015%
## 197 October 31, 2014 0.0015%
## 198 July 1, 2013 0.0015%
## 199 April 1, 2010 0.0015%
## 200 May 12, 2011 0.0014%
## 201 November 30, 2011 0.0014%
## 202 July 1, 2013 0.0014%
## 203 December 31, 2012 0.0014%
## 204 July 1, 2013 0.0012%
## 205 May 27, 2011 0.0012%
## 206 March 27, 2011 0.0012%
## 207 July 1, 2014 0.0011%
## 208 May 14, 2011 0.00099%
## 209 May 20, 2010 0.00089%
## 210 March 31, 2012 0.00087%
## 211 July 1, 2013 0.00078%
## 212 January 1, 2015 0.00077%
## 213 2013 0.00077%
## 214 April 1, 2010 0.00077%
## 215 July 1, 2015 0.00076%
## 216 April 1, 2010 0.00074%
## 217 January, 2013 0.00071%
## 218 January 1, 2015 0.00067%
## 219 January 1, 2010 0.00052%
## 220 December 31, 2014 0.00052%
## 221 December 31, 2013 0.00051%
## 222 January 1, 2012 0.00049%
## 223 December 31, 2014 0.00045%
## 224 January 25, 2012 0.00043%
## 225 December 31, 2012 0.00041%
## 226 September 30, 2014 0.0004%
## 227 July 12, 2010 0.00039%
## 228 January 1, 2013 0.00032%
## 229 July 1, 2013 0.00029%
## 230 December 1, 2011 0.00021%
## 231 May 11, 2011 0.00019%
## 232 July 1, 2013 0.00018%
## 233 July 1, 2013 0.00016%
## 234 October 30, 2011 0.00014%
## 235 January 1, 2012 0.00013%
## 236 January 1, 2012 0.000084%
## 237 May 12, 2011 0.000068%
## 238 July 1, 2015 0.000055%
## 239 July 1, 2015 0.000041%
## 240 July 1, 2014 0.000037%
## 241 August 9, 2011 0.000032%
## 242 August 9, 2011 0.000029%
## 243 September 10, 2011 0.000022%
## 244 October 18, 2011 0.000020%
## 245 July 1, 2012 0.000012%
## 246 August 9, 2011 0.0000076%
## 247 2013 0.00000077%
## Source
## 1 Official population clock
## 2 Population clock
## 3 Official population clock
## 4 Official estimate
## 5 Official population clock
## 6 Official population clock
## 7 UN projection[6]
## 8 Official population clock
## 9 Official estimate
## 10 Monthly official estimate
## 11 Official projection
## 12 Official population clock
## 13 Annual official estimate
## 14 Official projection
## 15 Official population clock
## 16 Official estimate
## 17 Official population clock
## 18 Annual official estimate
## 19 UN projection
## 20 Monthly official estimate
## 21 Official annual projection
## 22 Annual official estimate
## 23 Monthly official estimate
## 24 Official estimate
## 25 Preliminary 2014 census result
## 26 Official projection
## 27 Official population clock
## 28 Official Projection
## 29 UN projection
## 30 Official estimate
## 31 Official annual projection
## 32 Monthly official estimate
## 33 Official estimate
## 34 Official estimate
## 35 Official annual projection
## 36 Official annual projection
## 37 Official estimate
## 38 Preliminary 2014 census result
## 39 Preliminary 2014 census result
## 40 Official annual projection
## 41 Official annual projection
## 42 Official annual projection
## 43 Official population clock
## 44 Official estimate
## 45 Official annual projection
## 46 Official annual projection
## 47 Official estimate
## 48 Official estimate
## 49 Annual official projection
## 50 UN projection
## 51 Preliminary 2014 census result
## 52 Official population clock
## 53 Monthly official estimate
## 54 Official population clock[Note 7]
## 55 Preliminary 2014 Census Result
## 56 Annual official estimate
## 57 Official estimate
## 58 Official Estimate
## 59 Annual official estimate
## 60 UN projection
## 61 Official estimate
## 62 Official annual projection
## 63 Monthly official estimate
## 64 Official population clock
## 65 Official annual projection
## 66 UN projection
## 67 Official population clock
## 68 Official estimate
## 69 Official annual projection
## 70 Official annual projection
## 71 UN projection
## 72 2013 census result
## 73 2012 Census Result
## 74 Official annual projection
## 75 Official projection
## 76 Monthly official estimate
## 77 Annual official estimate
## 78 UN projection
## 79 Annual official estimate
## 80 Official estimate
## 81 Preliminary 2014 census result
## 82 Official projection
## 83 Preliminary 2014 census result
## 84 Official quarterly estimate
## 85 Annual official estimate
## 86 Official estimate
## 87 Official projection
## 88 Annual official estimate
## 89 Official annual projection
## 90 Monthly official estimate
## 91 Official estimate
## 92 UN projection
## 93 Quarterly official estimate
## 94 Annual official estimate
## 95 Quarterly provisional figure
## 96 Official estimate
## 97 Official monthly estimate
## 98 Quarterly provisional figure
## 99 Annual official estimate
## 100 Official estimate
## 101 Official estimate
## 102 UN projection
## 103 Annual official estimate
## 104 Official estimate
## 105 Annual official projection
## 106 UN projection
## 107 Official population clock
## 108 Official estimate
## 109 UN projection
## 110 UN projection
## 111 Official estimate
## 112 Official estimate
## 113 Quarterly official estimate
## 114 Official population clock
## 115 Official estimate
## 116 Official estimate
## 117 Quarterly official estimate
## 118 UN projection
## 119 Official estimate
## 120 Preliminary 2012 census result
## 121 UN projection
## 122 Annual official estimate
## 123 Official population clock
## 124 Official estimate
## 125 UN projection
## 126 Annual official estimate
## 127 Annual official estimate
## 128 Weekly official estimate
## 129 Official estimate
## 130 Preliminary 2013 census result
## 131 Official estimate
## 132 Annual official estimate
## 133 Official estimate
## 134 Official estimate
## 135 Annual official estimate
## 136 Official estimate
## 137 Quarterly official estimate
## 138 Official estimate
## 139 Monthly official estimate
## 140 Annual official estimate
## 141 Annual official estimate
## 142 Monthly official estimate
## 143 UN projection
## 144 Final 2011 census result
## 145 Official estimate
## 146 Official population clock
## 147 Final 2011 census result
## 148 Monthly official estimate
## 149 Preliminary 2013 census result
## 150 Official annual projection
## 151 UN projection
## 152 UN projection
## 153 Annual official estimate
## 154 2011 census result
## 155 Official annual projection
## 156 Official estimate
## 157 Official estimate
## 158 Official estimate
## 159 Official projection
## 160 UN projection
## 161 Annual official estimate
## 162 Official estimate
## 163 Official annual estimate
## 164 Official estimate
## 165 Official population clock
## 166 Official estimate
## 167 Official quarterly estimate
## 168 Final 2011 census result
## 169 Annual official estimate
## 170 Annual official estimate
## 171 Preliminary 2012 census result
## 172 Official annual projection
## 173 Preliminary 2014 census result
## 174 Official estimate
## 175 Official annual estimate
## 176 Official annual estimate
## 177 Preliminary 2011 census result
## 178 Official annual estimate
## 179 Annual official estimate
## 180 Official estimate
## 181 Preliminary 2014 census result
## 182 Annual official estimate
## 183 Official census
## 184 Official estimate
## 185 Preliminary 2014 census result
## 186 Preliminary 2012 census result
## 187 Annual official estimate
## 188 Official census
## 189 Annual official estimate
## 190 2012 census result
## 191 Final 2011 census result
## 192 2012 census result
## 193 UN projection
## 194 Final 2010 census result
## 195 Annual official estimate
## 196 UN projection
## 197 Official quarterly estimate
## 198 Annual official estimate
## 199 Final 2010 census result
## 200 2011 census result
## 201 2011 census result
## 202 Annual official estimate
## 203 Annual official estimate
## 204 Annual official estimate
## 205 Preliminary 2011 census result
## 206 2011 census result
## 207 Annual official estimate
## 208 Preliminary 2011 census result
## 209 Final 2010 census result
## 210 Annual official estimate
## 211 Annual official estimate
## 212 Annual official estimate
## 213 Official estimate
## 214 Final 2010 census result
## 215 UN projection
## 216 Final 2010 census result
## 217 Estimate
## 218 Monthly official estimate
## 219 Official estimate
## 220 Semi annual official estimate
## 221 Annual official estimate
## 222 Annual official estimate
## 223 Monthly official estimate
## 224 2012 census result
## 225 Annual official estimate
## 226 Official estimate
## 227 2010 census result
## 228 Official estimate
## 229 Annual official estimate
## 230 Final 2011 census result
## 231 Preliminary 2011 census result
## 232 Annual official estimate
## 233 Annual official estimate
## 234 2011 census result
## 235 Annual official estimate
## 236 Annual official estimate
## 237 2011 census result
## 238 UN projection
## 239 UN projection
## 240 Official estimate
## 241 2011 census result
## 242 2011 census result
## 243 Final 2011 census result
## 244 Final 2011 census result
## 245 Official estimate
## 246 2011 census result
## 247 Official estimatehtml_tables[[2]]## List of countries by population (United Nations)\nList of sovereign states and dependent territories by population density\nList of sovereign states and dependent territories by fertility rate\nList of countries by population growth rate\nList of contine ...
## 1
## Geography portal\n\nLists of countries by population\nContinental\n\nAfrica\nAsia\nEurope\nNorth America\nOceania\nSouth America\n\nIntercontinental\n\nArab states\nAmericas\nEurasia\nLatin America\nCommonwealth of Nations\nMiddle East\n\nSubcontinental\n\nCaribbean island ...
## 1 Geography portalhtml_tables[[3]]## NULLhtml_tables[[4]]## V1
## 1 v\nt\ne\n\nLists of countries by population statistics\n\n\nPopulation\n\n\nCurrent population\nCurrent population (inc. dependent territories)\nCurrent population (graphical)\nPopulation in 1900\nPopulation in 1907\nPopulation in 2000\nPopulation in 2010\nPopulation (United Nations statistics)\nPopulation growth rate\nnatural increase\npast and future population\npopulation milestones\ncitizen population (domestic)\n\n\n\nPopulation density\n\n\nCurrent density\nCurrent real density based on food growing capacity\n\n\n\n(Regional)\n\n\n\nPopulation of national capitals\nPopulation of cities proper\nPopulation of urban areas\n\n\n\n\nOther Demographics\n\n\nAge at first marriage\nDivorce rate\nEthnic and cultural diversity level\nForeign-born population\nImmigrant population\nMedian age\nNet migration rate\nNumber of households\nSex ratio\nUrban population\nUrbanization\n\n\n\nHealth\n\n\nAntiviral medications for pandemic influenza\nBirth rate\nMortality rate\nFertility rate\nHIV/AIDS adult prevalence rate\nInfant mortality rate\nLife expectancy\nObesity\nPercentage suffering from undernourishment\nHealth expenditure covered by government\nSuicide rate\nTotal health expenditure (PPP) per capita\n\n\n\nEducation and innovation\n\n\n25–34 year olds having a tertiary education degree\nBloomberg Innovation Index\nEducation Index\nInternational Innovation Index\nInnovation Union Scoreboard\nLiteracy rate\nProgramme for the International Assessment of Adult Competencies\nProgramme for International Student Assessment\nProgress in International Reading Literacy Study\nTrends in International Mathematics and Science Study\nWomen's average years in school\nWorld Intellectual Property Indicators\n\n\n\nEconomic\n\n\nDevelopment aid given\nForeign aid received\n\nEmployment rate\nIrrigated land area\nHuman Development Index\nby country\ninequality-adjusted\n\nHuman Poverty Index\nImports\nIncome equality\nJob security\nLabour force\nNumber of millionaires (US dollars)\nNumber of billionaires (US dollars)\nPercentage living in poverty\nSen social welfare function\nUnemployment rate\n\n\n\n\n\n\nList of international rankings\nList of top international rankings by country\nLists by country
## 2 v\nt\ne\n\nLists of countries by population statistics
## 3
## 4 Population
## 5
## 6 Population density
## 7
## 8 (Regional)
## 9
## 10 Other Demographics
## 11
## 12 Health
## 13
## 14 Education and innovation
## 15
## 16 Economic
## 17
## 18 List of international rankings\nList of top international rankings by country\nLists by country
## V2
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 Current population\nCurrent population (inc. dependent territories)\nCurrent population (graphical)\nPopulation in 1900\nPopulation in 1907\nPopulation in 2000\nPopulation in 2010\nPopulation (United Nations statistics)\nPopulation growth rate\nnatural increase\npast and future population\npopulation milestones\ncitizen population (domestic)
## 5 <NA>
## 6 Current density\nCurrent real density based on food growing capacity
## 7 <NA>
## 8 Population of national capitals\nPopulation of cities proper\nPopulation of urban areas
## 9 <NA>
## 10 Age at first marriage\nDivorce rate\nEthnic and cultural diversity level\nForeign-born population\nImmigrant population\nMedian age\nNet migration rate\nNumber of households\nSex ratio\nUrban population\nUrbanization
## 11 <NA>
## 12 Antiviral medications for pandemic influenza\nBirth rate\nMortality rate\nFertility rate\nHIV/AIDS adult prevalence rate\nInfant mortality rate\nLife expectancy\nObesity\nPercentage suffering from undernourishment\nHealth expenditure covered by government\nSuicide rate\nTotal health expenditure (PPP) per capita
## 13 <NA>
## 14 25–34 year olds having a tertiary education degree\nBloomberg Innovation Index\nEducation Index\nInternational Innovation Index\nInnovation Union Scoreboard\nLiteracy rate\nProgramme for the International Assessment of Adult Competencies\nProgramme for International Student Assessment\nProgress in International Reading Literacy Study\nTrends in International Mathematics and Science Study\nWomen's average years in school\nWorld Intellectual Property Indicators
## 15 <NA>
## 16 Development aid given\nForeign aid received\n\nEmployment rate\nIrrigated land area\nHuman Development Index\nby country\ninequality-adjusted\n\nHuman Poverty Index\nImports\nIncome equality\nJob security\nLabour force\nNumber of millionaires (US dollars)\nNumber of billionaires (US dollars)\nPercentage living in poverty\nSen social welfare function\nUnemployment rate
## 17 <NA>
## 18 <NA>html_tables[[5]]## V1
## 1
## 2 Population
## 3
## 4 Population density
## 5
## 6 (Regional)
## 7
## 8 Other Demographics
## 9
## 10 Health
## 11
## 12 Education and innovation
## 13
## 14 Economic
## 15
## 16 List of international rankings\nList of top international rankings by country\nLists by country
## V2
## 1 <NA>
## 2 Current population\nCurrent population (inc. dependent territories)\nCurrent population (graphical)\nPopulation in 1900\nPopulation in 1907\nPopulation in 2000\nPopulation in 2010\nPopulation (United Nations statistics)\nPopulation growth rate\nnatural increase\npast and future population\npopulation milestones\ncitizen population (domestic)
## 3 <NA>
## 4 Current density\nCurrent real density based on food growing capacity
## 5 <NA>
## 6 Population of national capitals\nPopulation of cities proper\nPopulation of urban areas
## 7 <NA>
## 8 Age at first marriage\nDivorce rate\nEthnic and cultural diversity level\nForeign-born population\nImmigrant population\nMedian age\nNet migration rate\nNumber of households\nSex ratio\nUrban population\nUrbanization
## 9 <NA>
## 10 Antiviral medications for pandemic influenza\nBirth rate\nMortality rate\nFertility rate\nHIV/AIDS adult prevalence rate\nInfant mortality rate\nLife expectancy\nObesity\nPercentage suffering from undernourishment\nHealth expenditure covered by government\nSuicide rate\nTotal health expenditure (PPP) per capita
## 11 <NA>
## 12 25–34 year olds having a tertiary education degree\nBloomberg Innovation Index\nEducation Index\nInternational Innovation Index\nInnovation Union Scoreboard\nLiteracy rate\nProgramme for the International Assessment of Adult Competencies\nProgramme for International Student Assessment\nProgress in International Reading Literacy Study\nTrends in International Mathematics and Science Study\nWomen's average years in school\nWorld Intellectual Property Indicators
## 13 <NA>
## 14 Development aid given\nForeign aid received\n\nEmployment rate\nIrrigated land area\nHuman Development Index\nby country\ninequality-adjusted\n\nHuman Poverty Index\nImports\nIncome equality\nJob security\nLabour force\nNumber of millionaires (US dollars)\nNumber of billionaires (US dollars)\nPercentage living in poverty\nSen social welfare function\nUnemployment rate
## 15 <NA>
## 16 <NA>I use both MS internet Explorer and Google Chrome MS internet Explorer. To view the page source, just go to the main menu, choose view, then select source. In Google Chrome, from main menu, choose more tools, then select view source
url_rvest <- html("http://www.jaredlander.com/2012/02/another-kind-of-super-bowl-pool/")
bowlPool_rvest <- html_table(url_rvest)
class(bowlPool_rvest)## [1] "list"length(bowlPool_rvest)## [1] 1str(bowlPool_rvest)## List of 1
## $ :'data.frame': 10 obs. of 3 variables:
## ..$ X1: chr [1:10] "Participant 1" "Participant 2" "Participant 3" "Participant 4" ...
## ..$ X2: chr [1:10] "Giant A" "Giant B" "Giant C" "Giant D" ...
## ..$ X3: chr [1:10] "Patriot Q" "Patriot R" "Patriot S" "Patriot T" ...# using the table_count function above. there is only 1 table.
table_count(bowlPool_rvest)## [1] 1# Validating the data. only html_tbl_9[1] is a table.
bowlPool_rvest[[1]]## X1 X2 X3
## 1 Participant 1 Giant A Patriot Q
## 2 Participant 2 Giant B Patriot R
## 3 Participant 3 Giant C Patriot S
## 4 Participant 4 Giant D Patriot T
## 5 Participant 5 Giant E Patriot U
## 6 Participant 6 Giant F Patriot V
## 7 Participant 7 Giant G Patriot W
## 8 Participant 8 Giant H Patriot X
## 9 Participant 9 Giant I Patriot Y
## 10 Participant 10 Giant J Patriot Zstr(bowlPool_rvest)## List of 1
## $ :'data.frame': 10 obs. of 3 variables:
## ..$ X1: chr [1:10] "Participant 1" "Participant 2" "Participant 3" "Participant 4" ...
## ..$ X2: chr [1:10] "Giant A" "Giant B" "Giant C" "Giant D" ...
## ..$ X3: chr [1:10] "Patriot Q" "Patriot R" "Patriot S" "Patriot T" ...str(bowlPool)## 'data.frame': 10 obs. of 3 variables:
## $ V1: chr "Participant 1" "Participant 2" "Participant 3" "Participant 4" ...
## $ V2: chr "Giant A" "Giant B" "Giant C" "Giant D" ...
## $ V3: chr "Patriot Q" "Patriot R" "Patriot S" "Patriot T" ...length(bowlPool_rvest)## [1] 1length(bowlPool)## [1] 3