Web Scraping in R
Haley Jeppson, Sam Tyner
June 15, 2017
A web of data
- In 2008, an estimated 154 million HTML tables (out of the 14.1 billion) contain ‘high quality relational data’!!!
- Hard to quantify how much more exists outside of HTML Tables, but there is an estimate of at least 30 million lists with ‘high quality relational data’.
- A growing number of websites/companies provide programmatic access to their data/services via web APIs (that data typically comes in XML/JSON format).
Before scraping, do some googling!
- If resource is well-known (e.g. Twitter, Fitbit, etc.), there is probably an existing R package for it.
- ropensci has a ton of R packages providing easy-to-use interfaces to open data.
- The Web Technologies and Services CRAN Task View is a great overview of various tools for working with data that lives on the web in R.
A web of messy data!
- Recall the concept of tidy data.
- Data is in a table where
- 1 row == 1 observation
- 1 column == 1 variable (observational attribute)
- Parsing web data (HTML/XML/JSON) is easy (for computers)
- Getting it in a tidy form is typically not easy.
- Knowing a bit about modern tools & web technologies makes it much easier.
What is webscraping?
- Extract data from websites
- Tables
- Links to other websites
- Text
Why webscrape?
- Because copy-paste is awful
- Because it’s fast
- Because you can automate it
Don’t abuse your power
- Before scraping a website, please read the terms and conditions!!
- It’s sometimes more efficient/appropriate to find the API.
- If a website public offers an API, USE IT (instead of scraping)!!! (more on this later)
http://www.wired.com/2014/01/how-to-hack-okcupid
http://www.vox.com/2016/5/12/11666116/70000-okcupid-users-data-release
Webscraping with rvest:
Step-by-Step Start Guide
Step 1: Find a URL
What data do you want?
- Information on Oscar-
nominatedwinning film Moonlight
Find it on the web!
# character variable containing the url you want to scrape
myurl <- "http://www.imdb.com/title/tt4975722/"Step 2: Read HTML into R
“Huh? What am I doing?” - some of you right now
- HTML is HyperText Markup Language. All webpages are written with it.
- Go to any website, right click, click “View Page Source” to see the HTML
library(tidyverse)
library(rvest)
myhtml <- read_html(myurl)
myhtml## {xml_document}
## <html xmlns:og="http://ogp.me/ns#" xmlns:fb="http://www.facebook.com/2008/fbml">
## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset= ...
## [2] <body id="styleguide-v2" class="fixed">\n<script>\n if (typeof ue ...Step 3: Figure out
where your data is
Need to find your data within the myhtml object.
Tags to look for:
<p>: paragraphs<h1>,<h2>, etc.: headers<a>: links<li>: item in a list<table>: tables
Use Selector Gadget to find the exact location. (Demo)
For more on HTML, I recommend W3schools’ tutorial
- You don’t need to be an expert in HTML to webscrape with
rvest!
Step 4: Tell rvest where to find your data
Copy-paste from Selector Gadget or give HTML tags into html_nodes() to extract your data of interest
myhtml %>% html_nodes(".summary_text") %>% html_text()## [1] "\n A chronicle of the childhood, adolescence and burgeoning adulthood of a young, African-American, gay man growing up in a rough neighborhood of Miami.\n "myhtml %>% html_nodes("table") %>% html_table(header = TRUE)## [[1]]
## Cast overview, first billed only: Cast overview, first billed only:
## 1 NA Mahershala Ali
## 2 NA Shariff Earp
## 3 NA Duan Sanderson
## 4 NA Alex R. Hibbert
## 5 NA Janelle Monáe
## 6 NA Naomie Harris
## 7 NA Jaden Piner
## 8 NA Herman 'Caheei McGloun
## 9 NA Kamal Ani-Bellow
## 10 NA Keomi Givens
## 11 NA Eddie Blanchard
## 12 NA Rudi Goblen
## 13 NA Ashton Sanders
## 14 NA Edson Jean
## 15 NA Patrick Decile
## Cast overview, first billed only:
## 1 ...
## 2 ...
## 3 ...
## 4 ...
## 5 ...
## 6 ...
## 7 ...
## 8 ...
## 9 ...
## 10 ...
## 11 ...
## 12 ...
## 13 ...
## 14 ...
## 15 ...
## Cast overview, first billed only:
## 1 Juan
## 2 Terrence
## 3 Azu \n \n \n (as Duan 'Sandy' Sanderson)
## 4 Little \n \n \n (as Alex Hibbert)
## 5 Teresa
## 6 Paula
## 7 Kevin age 9
## 8 Longshoreman \n \n \n (as Herman 'Caheej' McCloun)
## 9 Portable Boy 1
## 10 Portable Boy 2
## 11 Portable Boy 3
## 12 Gee
## 13 Chiron
## 14 Mr. Pierce
## 15 Terrel
##
## [[2]]
## Amazon Affiliates
## 1 Amazon VideoWatch Movies &TV Online
## Amazon Affiliates
## 1 Prime VideoUnlimited Streamingof Movies & TV
## Amazon Affiliates
## 1 Amazon GermanyBuy Movies onDVD & Blu-ray
## Amazon Affiliates
## 1 Amazon ItalyBuy Movies onDVD & Blu-ray
## Amazon Affiliates
## 1 Amazon FranceBuy Movies onDVD & Blu-ray
## Amazon Affiliates Amazon Affiliates
## 1 Amazon IndiaBuy Movie andTV Show DVDs DPReviewDigitalPhotography
## Amazon Affiliates
## 1 AudibleDownloadAudio BooksStep 5: Save & tidy data
library(stringr)
library(magrittr)
mydat <- myhtml %>%
html_nodes("table") %>%
extract2(1) %>%
html_table(header = TRUE)
mydat <- mydat[,c(2,4)]
names(mydat) <- c("Actor", "Role")
mydat <- mydat %>%
mutate(Actor = Actor,
Role = str_replace_all(Role, "\n ", ""))
mydat## Actor Role
## 1 Mahershala Ali Juan
## 2 Shariff Earp Terrence
## 3 Duan Sanderson Azu (as Duan 'Sandy' Sanderson)
## 4 Alex R. Hibbert Little (as Alex Hibbert)
## 5 Janelle Monáe Teresa
## 6 Naomie Harris Paula
## 7 Jaden Piner Kevin age 9
## 8 Herman 'Caheei McGloun Longshoreman (as Herman 'Caheej' McCloun)
## 9 Kamal Ani-Bellow Portable Boy 1
## 10 Keomi Givens Portable Boy 2
## 11 Eddie Blanchard Portable Boy 3
## 12 Rudi Goblen Gee
## 13 Ashton Sanders Chiron
## 14 Edson Jean Mr. Pierce
## 15 Patrick Decile TerrelYour Turn #1
Using rvest, scrape a table from Wikipedia. You can pick your own table or you can get one of the tables in the country GDP per capita example from earlier.
Your result should be a data frame with one observation per row and one variable per column.
Your Turn #1 Solution
library(rvest)
library(magrittr)
myurl <- "https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita"
myhtml <- read_html(myurl)
myhtml %>%
html_nodes("table") %>%
extract2(2) %>%
html_table(header = TRUE) %>%
mutate(`Int$` = parse_number(`Int$`)) %>%
head## Rank Country Int$
## 1 1 Qatar 127660
## 2 2 Luxembourg 104003
## 3 — Macau 95151
## 4 3 Singapore 87855
## 5 4 Brunei 76884
## 6 5 Kuwait 71887Deeper dive into rvest
Key Functions: html_nodes
html_nodes(x, "path")extracts all elements from the pagexthat have the tag / class / idpath. (Use SelectorGadget to determinepath.)html_node()does the same thing but only returns the first matching element.- Can be chained
myhtml %>%
html_nodes("p") %>% # first get all the paragraphs
html_nodes("a") # then get all the links in those paragraphs## {xml_nodeset (24)}
## [1] <a href="/wiki/Purchasing_power_parity" title="Purchasing power par ...
## [2] <a href="/wiki/Goods_and_services" title="Goods and services">goods ...
## [3] <a href="/wiki/Gross_domestic_product" title="Gross domestic produc ...
## [4] <a href="/wiki/Per_capita" title="Per capita">per capita</a>
## [5] <a href="/wiki/International_Monetary_Fund" title="International Mo ...
## [6] <a href="/wiki/World_Bank" title="World Bank">World Bank</a>
## [7] <a href="/wiki/National_wealth" title="National wealth">national we ...
## [8] <a href="/wiki/Savings" class="mw-redirect" title="Savings">savings ...
## [9] <a href="/wiki/Cost_of_living" title="Cost of living">cost of livin ...
## [10] <a href="/wiki/List_of_countries_by_GDP_(nominal)_per_capita" title ...
## [11] <a href="https://en.wiktionary.org/wiki/generalized" class="extiw" ...
## [12] <a href="/wiki/Living_standards" class="mw-redirect" title="Living ...
## [13] <a href="/wiki/Inflation_rates" class="mw-redirect" title="Inflatio ...
## [14] <a href="/wiki/Exchange_rates" class="mw-redirect" title="Exchange ...
## [15] <a href="#cite_note-2">[2]</a>
## [16] <a href="#cite_note-3">[3]</a>
## [17] <a href="/wiki/Personal_income" title="Personal income">personal in ...
## [18] <a href="/wiki/Gross_domestic_product#Standard_of_living_and_GDP:_W ...
## [19] <a href="/wiki/Geary%E2%80%93Khamis_dollar" title="Geary–Khamis dol ...
## [20] <a href="/wiki/Rounding" title="Rounding">rounded</a>
## ...Key Functions: html_text
html_text(x)extracts all text from the nodesetx- Good for cleaning output
myhtml %>%
html_nodes("p") %>% # first get all the paragraphs
html_nodes("a") %>% # then get all the links in those paragraphs
html_text() # get the linked text only ## [1] "purchasing power parity"
## [2] "goods and services"
## [3] "gross domestic product"
## [4] "per capita"
## [5] "International Monetary Fund"
## [6] "World Bank"
## [7] "national wealth"
## [8] "savings"
## [9] "cost of living"
## [10] "List of countries by GDP (nominal) per capita"
## [11] "generalized"
## [12] "living standards"
## [13] "inflation rates"
## [14] "exchange rates"
## [15] "[2]"
## [16] "[3]"
## [17] "personal income"
## [18] "Standard of living and GDP"
## [19] "Geary–Khamis dollars"
## [20] "rounded"
## [21] "whole number"
## [22] "economies"
## [23] "sovereign states"
## [24] "dependent territories"Key Functions: html_table
html_table(x, header, fill)- parse html table(s) fromxinto a data frame or list of data frames- Structure of HTML makes finding and extracting tables easy!
myhtml %>%
html_nodes("table") %>% # get the tables
head(2) # look at first 2## {xml_nodeset (2)}
## [1] <table style="font-size:95%;">\n<tr>\n<td width="30%" align="center" ...
## [2] <table class="wikitable sortable" style="margin-left:auto;margin-rig ...myhtml %>%
html_nodes("table") %>% # get the tables
extract2(2) %>% # pick the second one to parse
html_table(header = TRUE) # parse table ## Rank Country Int$
## 1 1 Qatar 127,660
## 2 2 Luxembourg 104,003
## 3 — Macau 95,151
## 4 3 Singapore 87,855
## 5 4 Brunei 76,884
## 6 5 Kuwait 71,887
## 7 6 Norway 69,249
## 8 7 Ireland 69,231
## 9 8 United Arab Emirates 67,871
## 10 9 Switzerland 59,561
## 11 10 San Marino 59,058
## 12 — Hong Kong 58,322
## 13 11 United States 57,436
## 14 12 Saudi Arabia 55,158
## 15 13 Netherlands 51,049
## 16 14 Bahrain 50,704
## 17 15 Sweden 49,836
## 18 16 Iceland 49,136
## 19 17 Australia 48,899
## 20 18 Germany 48,111
## 21 — Taiwan 48,095
## 22 19 Austria 48,005
## 23 20 Denmark 47,985
## 24 21 Oman 46,698
## 25 22 Canada 46,437
## 26 23 Belgium 45,047
## 27 24 United Kingdom 42,481
## 28 25 France 42,314
## 29 26 Finland 42,165
## 30 27 Japan 41,275
## 31 28 Malta 39,834
## 32 29 Equatorial Guinea 38,639
## 33 — Puerto Rico 38,393
## 34 30 South Korea 37,740
## 35 31 New Zealand 37,294
## 36 32 Italy 36,833
## 37 33 Spain 36,416
## 38 34 Israel 35,179
## 39 35 Cyprus 34,970
## 40 36 Czech Republic 33,232
## 41 37 Slovenia 32,085
## 42 38 Trinidad and Tobago 31,870
## 43 39 Slovakia 31,339
## 44 40 Lithuania 29,972
## 45 41 Estonia 29,313
## 46 42 Portugal 28,933
## 47 43 Poland 27,764
## 48 44 Seychelles 27,602
## 49 45 Hungary 27,482
## 50 46 Malaysia 27,267
## 51 47 Greece 26,669
## 52 48 Russia 26,490
## 53 49 Saint Kitts and Nevis 25,940
## 54 50 Latvia 25,710
## 55 51 Antigua and Barbuda 25,157
## 56 52 Kazakhstan 25,145
## 57 53 Turkey 24,912
## 58 54 Bahamas, The 24,555
## 59 55 Chile 24,113
## 60 56 Panama 23,024
## 61 57 Croatia 22,795
## 62 58 Romania 22,348
## 63 59 Uruguay 21,527
## 64 60 Mauritius 20,422
## 65 61 Bulgaria 20,327
## 66 62 Argentina 20,047
## 67 63 Gabon 19,056
## 68 64 Mexico 18,938
## 69 65 Lebanon 18,525
## 70 66 Iran 18,077
## 71 67 Belarus 18,000
## 72 68 Iraq 17,944
## 73 69 Turkmenistan 17,485
## 74 70 Azerbaijan 17,439
## 75 71 Barbados 17,100
## 76 72 Botswana 17,042
## 77 73 Thailand 16,888
## 78 74 Montenegro 16,643
## 79 75 Costa Rica 16,436
## 80 — World[7][8] 16,318
## 81 76 Dominican Republic 16,049
## 82 77 Maldives 15,553
## 83 78 China 15,399
## 84 79 Palau 15,319
## 85 80 Brazil 15,242
## 86 81 Algeria 15,026
## 87 82 Macedonia 14,597
## 88 83 Serbia 14,493
## 89 84 Colombia 14,130
## 90 85 Grenada 14,116
## 91 86 Suriname 13,988
## 92 87 Venezuela 13,761
## 93 88 South Africa 13,225
## 94 89 Peru 12,903
## 95 90 Egypt 12,554
## 96 91 Jordan 12,278
## 97 92 Mongolia 12,275
## 98 93 Sri Lanka 12,262
## 99 94 Albania 11,840
## 100 95 Saint Lucia 11,783
## 101 96 Indonesia 11,720
## 102 97 Tunisia 11,634
## 103 98 Nauru 11,539
## 104 99 Dominica 11,375
## 105 100 Namibia 11,290
## 106 101 Saint Vincent and the Grenadines 11,271
## 107 102 Ecuador 11,109
## 108 103 Bosnia and Herzegovina 10,958
## 109 — Kosovo[9][10] 10,235
## 110 104 Georgia 10,044
## 111 105 Swaziland 9,776
## 112 106 Paraguay 9,396
## 113 107 Fiji 9,268
## 114 108 Jamaica 8,976
## 115 109 El Salvador 8,909
## 116 110 Libya 8,678
## 117 111 Armenia 8,621
## 118 112 Morocco 8,330
## 119 113 Ukraine 8,305
## 120 114 Bhutan 8,227
## 121 115 Belize 8,220
## 122 116 Guatemala 7,899
## 123 117 Guyana 7,873
## 124 118 Philippines 7,728
## 125 119 Bolivia 7,218
## 126 120 Angola 6,844
## 127 121 Congo, Rep. 6,676
## 128 122 Cape Verde 6,662
## 129 123 India 6,616
## 130 124 Uzbekistan 6,563
## 131 125 Vietnam 6,429
## 132 126 Nigeria 5,942
## 133 127 Myanmar 5,832
## 134 128 Laos 5,710
## 135 129 Samoa 5,553
## 136 130 Nicaragua 5,452
## 137 131 Tonga 5,386
## 138 132 Moldova 5,328
## 139 133 Honduras 5,271
## 140 134 Pakistan 4,906
## 141 135 Sudan 4,447
## 142 136 Ghana 4,412
## 143 137 Mauritania 4,328
## 144 138 Timor-Leste 4,187
## 145 139 Bangladesh 3,891
## 146 140 Zambia 3,880
## 147 141 Cambodia 3,737
## 148 142 Côte d'Ivoire 3,609
## 149 143 Lesotho 3,601
## 150 144 Tuvalu 3,567
## 151 145 Papua New Guinea 3,541
## 152 146 Kyrgyzstan 3,521
## 153 147 Djibouti 3,370
## 154 148 Kenya 3,361
## 155 149 Marshall Islands 3,301
## 156 150 Cameroon 3,249
## 157 151 Micronesia 3,234
## 158 152 Tanzania 3,080
## 159 153 São Tomé and Príncipe 3,072
## 160 154 Tajikistan 3,008
## 161 155 Vanuatu 2,631
## 162 156 Senegal 2,577
## 163 157 Nepal 2,479
## 164 158 Chad 2,445
## 165 159 Yemen 2,375
## 166 160 Mali 2,266
## 167 161 Benin 2,119
## 168 162 Uganda 2,068
## 169 163 Rwanda 1,977
## 170 164 Solomon Islands 1,973
## 171 165 Zimbabwe 1,970
## 172 166 Ethiopia 1,946
## 173 167 Afghanistan 1,919
## 174 168 Kiribati 1,823
## 175 169 Haiti 1,784
## 176 170 Burkina Faso 1,782
## 177 171 Guinea-Bissau 1,730
## 178 172 Sierra Leone 1,672
## 179 173 Gambia, The 1,667
## 180 174 South Sudan 1,657
## 181 175 Togo 1,550
## 182 176 Comoros 1,529
## 183 177 Madagascar 1,505
## 184 178 Eritrea 1,410
## 185 179 Guinea 1,265
## 186 180 Mozambique 1,215
## 187 181 Malawi 1,134
## 188 182 Niger 1,107
## 189 183 Liberia 855
## 190 184 Burundi 814
## 191 185 Congo, Dem. Rep. 773
## 192 186 Central African Republic 652Key functions: html_attrs
html_attrs(x)- extracts all attribute elements from a nodesetxhtml_attr(x, name)- extracts thenameattribute from all elements in nodesetx- Attributes are things in the HTML like
href,title,class,style, etc. - Use these functions to find and extract your data
myhtml %>%
html_nodes("table") %>% extract2(2) %>%
html_attrs()## class
## "wikitable sortable"
## style
## "margin-left:auto;margin-right:auto;text-align: right"myhtml %>%
html_nodes("p") %>% html_nodes("a") %>%
html_attr("href")## [1] "/wiki/Purchasing_power_parity"
## [2] "/wiki/Goods_and_services"
## [3] "/wiki/Gross_domestic_product"
## [4] "/wiki/Per_capita"
## [5] "/wiki/International_Monetary_Fund"
## [6] "/wiki/World_Bank"
## [7] "/wiki/National_wealth"
## [8] "/wiki/Savings"
## [9] "/wiki/Cost_of_living"
## [10] "/wiki/List_of_countries_by_GDP_(nominal)_per_capita"
## [11] "https://en.wiktionary.org/wiki/generalized"
## [12] "/wiki/Living_standards"
## [13] "/wiki/Inflation_rates"
## [14] "/wiki/Exchange_rates"
## [15] "#cite_note-2"
## [16] "#cite_note-3"
## [17] "/wiki/Personal_income"
## [18] "/wiki/Gross_domestic_product#Standard_of_living_and_GDP:_Wealth_distribution_and_externalities"
## [19] "/wiki/Geary%E2%80%93Khamis_dollar"
## [20] "/wiki/Rounding"
## [21] "/wiki/Integer"
## [22] "/wiki/Economy"
## [23] "/wiki/Sovereign_state"
## [24] "/wiki/Dependent_territories"Other functions
html_children- list the “children” of the HTML page. Can be chained likehtml_nodeshtml_name- gives the tags of a nodeset. Use in a chain withhtml_children
myhtml %>%
html_children() %>%
html_name()## [1] "head" "body"html_form- parses HTML forms (checkboxes, fill-in-the-blanks, etc.)html_session- simulate a session in an html browser; use the functionsjump_to,backto navigate through the page
Your Turn #2
Find another website you want to scrape (ideas: all bills in the house so far this year, video game reviews, anything Wikipedia) and use at least 3 different rvest functions in a chain to extract some data.
Advanced Example: Inaugural Addresses
The Data
- The Avalon Project has most of the U.S. Presidential inaugural addresses.
- Obama & Trump’s (’13, ’17), VanBuren 1837, Buchanan 1857, Garfield 1881, and Coolidge 1925 are missing, but are easily found elsewhere. I have them saved as text files on the website.
- Let’s scrape all of them from The Avalon Project!
Get data frame of addresses
- Could use another source to get this data of President names and years of inaugurations, but we’ll use The Avalon Project’s site because it’s a good example of data that needs tidying.
url <- "http://avalon.law.yale.edu/subject_menus/inaug.asp"
# even though it's called "all inaugs" some are missing
all_inaugs <- (url %>%
read_html() %>%
html_nodes("table") %>%
html_table(fill=T, header = T)) %>% extract2(3)
# tidy table of addresses
all_inaugs_tidy <- all_inaugs %>%
gather(term, year, -President) %>%
filter(!is.na(year)) %>%
select(-term) %>%
arrange(year)
head(all_inaugs_tidy)## President year
## 1 George Washington 1789
## 2 George Washington 1793
## 3 John Adams 1797
## 4 Thomas Jefferson 1801
## 5 Thomas Jefferson 1805
## 6 James Madison 1809Get links to visit & scrape
# get the links to the addresses
inaugadds_adds <- (url %>%
read_html() %>%
html_nodes("a") %>%
html_attr("href"))[12:66]
# create the urls to scrape
urlstump <- "http://avalon.law.yale.edu/"
inaugurls <- paste0(urlstump, str_replace(inaugadds_adds, "../", ""))
all_inaugs_tidy$url <- inaugurls
head(all_inaugs_tidy)## President year
## 1 George Washington 1789
## 2 George Washington 1793
## 3 John Adams 1797
## 4 Thomas Jefferson 1801
## 5 Thomas Jefferson 1805
## 6 James Madison 1809
## url
## 1 http://avalon.law.yale.edu/18th_century/wash1.asp
## 2 http://avalon.law.yale.edu/18th_century/wash2.asp
## 3 http://avalon.law.yale.edu/18th_century/adams.asp
## 4 http://avalon.law.yale.edu/19th_century/jefinau1.asp
## 5 http://avalon.law.yale.edu/19th_century/jefinau2.asp
## 6 http://avalon.law.yale.edu/19th_century/madison1.aspAutomate scraping
- A function to read the addresses and get the text of the speeches, with a catch for a read error
get_inaugurations <- function(url){
test <- try(url %>% read_html(), silent=T)
if ("try-error" %in% class(test)) {
return(NA)
} else
url %>% read_html() %>%
html_nodes("p") %>%
html_text() -> address
return(unlist(address))
}
# takes about 30 secs to run
all_inaugs_text <- all_inaugs_tidy %>%
mutate(address_text = (map(url, get_inaugurations)))
all_inaugs_text$address_text[[1]]## [1] " Fellow-Citizens of the Senate and of the House of Representatives: "
## [2] "Among the vicissitudes incident to life no event could have filled me with greater anxieties than that of which the notification was transmitted by your order, and received on the 14th day of the present month. On the one hand, I was summoned by my Country, whose voice I can never hear but with veneration and love, from a retreat which I had chosen with the fondest predilection, and, in my flattering hopes, with an immutable decision, as the asylum of my declining years--a retreat which was rendered every day more necessary as well as more dear to me by the addition of habit to inclination, and of frequent interruptions in my health to the gradual waste committed on it by time. On the other hand, the magnitude and difficulty of the trust to which the voice of my country called me, being sufficient to awaken in the wisest and most experienced of her citizens a distrustful scrutiny into his qualifications, could not but overwhelm with despondence one who (inheriting inferior endowments from nature and unpracticed in the duties of civil administration) ought to be peculiarly conscious of his own deficiencies. In this conflict of emotions all I dare aver is that it has been my faithful study to collect my duty from a just appreciation of every circumstance by which it might be affected. All I dare hope is that if, in executing this task, I have been too much swayed by a grateful remembrance of former instances, or by an affectionate sensibility to this transcendent proof of the confidence of my fellow-citizens, and have thence too little consulted my incapacity as well as disinclination for the weighty and untried cares before me, my error will be palliated by the motives which mislead me, and its consequences be judged by my country with some share of the partiality in which they originated. "
## [3] "Such being the impressions under which I have, in obedience to the public summons, repaired to the present station, it would be peculiarly improper to omit in this first official act my fervent supplications to that Almighty Being who rules over the universe, who presides in the councils of nations, and whose providential aids can supply every human defect, that His benediction may consecrate to the liberties and happiness of the people of the United States a Government instituted by themselves for these essential purposes, and may enable every instrument employed in its administration to execute with success the functions allotted to his charge. In tendering this homage to the Great Author of every public and private good, I assure myself that it expresses your sentiments not less than my own, nor those of my fellow- citizens at large less than either. No people can be bound to acknowledge and adore the Invisible Hand which conducts the affairs of men more than those of the United States. Every step by which they have advanced to the character of an independent nation seems to have been distinguished by some token of providential agency; and in the important revolution just accomplished in the system of their united government the tranquil deliberations and voluntary consent of so many distinct communities from which the event has resulted can not be compared with the means by which most governments have been established without some return of pious gratitude, along with an humble anticipation of the future blessings which the past seem to presage. These reflections, arising out of the present crisis, have forced themselves too strongly on my mind to be suppressed. You will join with me, I trust, in thinking that there are none under the influence of which the proceedings of a new and free government can more auspiciously commence. "
## [4] "By the article establishing the executive department it is made the duty of the President \"to recommend to your consideration such measures as he shall judge necessary and expedient.\" The circumstances under which I now meet you will acquit me from entering into that subject further than to refer to the great constitutional charter under which you are assembled, and which, in defining your powers, designates the objects to which your attention is to be given. It will be more consistent with those circumstances, and far more congenial with the feelings which actuate me, to substitute, in place of a recommendation of particular measures, the tribute that is due to the talents, the rectitude, and the patriotism which adorn the characters selected to devise and adopt them. In these honorable qualifications I behold the surest pledges that as on one side no local prejudices or attachments, no separate views nor party animosities, will misdirect the comprehensive and equal eye which ought to watch over this great assemblage of communities and interests, so, on another, that the foundation of our national policy will be laid in the pure and immutable principles of private morality, and the preeminence of free government be exemplified by all the attributes which can win the affections of its citizens and command the respect of the world. I dwell on this prospect with every satisfaction which an ardent love for my country can inspire, since there is no truth more thoroughly established than that there exists in the economy and course of nature an indissoluble union between virtue and happiness; between duty and advantage; between the genuine maxims of an honest and magnanimous policy and the solid rewards of public prosperity and felicity; since we ought to be no less persuaded that the propitious smiles of Heaven can never be expected on a nation that disregards the eternal rules of order and right which Heaven itself has ordained; and since the preservation of the sacred fire of liberty and the destiny of the republican model of government are justly considered, perhaps, as deeply, as finally, staked on the experiment entrusted to the hands of the American people. "
## [5] "Besides the ordinary objects submitted to your care, it will remain with your judgment to decide how far an exercise of the occasional power delegated by the fifth article of the Constitution is rendered expedient at the present juncture by the nature of objections which have been urged against the system, or by the degree of inquietude which has given birth to them. Instead of undertaking particular recommendations on this subject, in which I could be guided by no lights derived from official opportunities, I shall again give way to my entire confidence in your discernment and pursuit of the public good; for I assure myself that whilst you carefully avoid every alteration which might endanger the benefits of an united and effective government, or which ought to await the future lessons of experience, a reverence for the characteristic rights of freemen and a regard for the public harmony will sufficiently influence your deliberations on the question how far the former can be impregnably fortified or the latter be safely and advantageously promoted. "
## [6] "To the foregoing observations I have one to add, which will be most properly addressed to the House of Representatives. It concerns myself, and will therefore be as brief as possible. When I was first honored with a call into the service of my country, then on the eve of an arduous struggle for its liberties, the light in which I contemplated my duty required that I should renounce every pecuniary compensation. From this resolution I have in no instance departed; and being still under the impressions which produced it, I must decline as inapplicable to myself any share in the personal emoluments which may be indispensably included in a permanent provision for the executive department, and must accordingly pray that the pecuniary estimates for the station in which I am placed may during my continuance in it be limited to such actual expenditures as the public good may be thought to require. "
## [7] "Having thus imparted to you my sentiments as they have been awakened by the occasion which brings us together, I shall take my present leave; but not without resorting once more to the benign Parent of the Human Race in humble supplication that, since He has been pleased to favor the American people with opportunities for deliberating in perfect tranquillity, and dispositions for deciding with unparalleled unanimity on a form of government for the security of their union and the advancement of their happiness, so His divine blessing may be equally conspicuous in the enlarged views, the temperate consultations, and the wise measures on which the success of this Government must depend. "Add Missings
all_inaugs_text$President[is.na(all_inaugs_text$address_text)]## [1] "Martin Van Buren" "James Buchanan" "James A. Garfield"
## [4] "Calvin Coolidge"# there are 7 missing at this point: obama's and trump's, plus coolidge, garfield, buchanan, and van buren, which errored in the scraping.
obama09 <- get_inaugurations("http://avalon.law.yale.edu/21st_century/obama.asp")
obama13 <- readLines("speeches/obama2013.txt")
trump17 <- readLines("speeches/trumpinaug.txt")
vanburen1837 <- readLines("speeches/vanburen1837.txt") # row 13
buchanan1857 <- readLines("speeches/buchanan1857.txt") # row 18
garfield1881 <- readLines("speeches/garfield1881.txt") # row 24
coolidge1925 <- readLines("speeches/coolidge1925.txt") # row 35
all_inaugs_text$address_text[c(13,18,24,35)] <- list(vanburen1837,buchanan1857, garfield1881, coolidge1925)
# lets combine them all now
recents <- data.frame(President = c(rep("Barack Obama", 2),
"Donald Trump"),
year = c(2009, 2013, 2017),
url = NA,
address_text = NA)
all_inaugs_text <- rbind(all_inaugs_text, recents)
all_inaugs_text$address_text[c(56:58)] <- list(obama09, obama13, trump17)Check-in: What did we do?
- We found some interesting data to scrape from the web using
rvest. - We used
tidyrto create tidy data: A data frame of President and year. One observation per row! - We used the consistent HTML structure of the urls we wanted to scrape to automate collection of web data
- Way faster than copy-paste!
- Though we had to do some by hand, we took advantage of the tidy data and added the missing data manually without much pain.
- We now have a tidy data set of Presidential inaugural addresses for text analysis!
A (Small) Text Analysis
Now, I use the tidytext package to get the words out of each inaugural address.
# install.packages("tidytext")
library(tidytext)
all_inaugs_text %>%
select(-url) %>%
unnest() %>%
unnest_tokens(word, address_text) -> presidential_words
head(presidential_words)## President year word
## 1 George Washington 1789 fellow
## 1.1 George Washington 1789 citizens
## 1.2 George Washington 1789 of
## 1.3 George Washington 1789 the
## 1.4 George Washington 1789 senate
## 1.5 George Washington 1789 andLongest speeches
presidential_words %>%
group_by(President,year) %>%
summarize(num_words = n()) %>%
arrange(desc(num_words)) -> presidential_wordtotals
Web APIs
Web APIs
- Server-side Web APIs (Application Programming Interfaces) are a popular way to provide easy access to data and other services.
- If you (the client) want data from a server, you typically need one HTTP verb –
GET.
library(httr)
sam <- GET("https://api.github.com/users/sctyner")
content(sam)[c("name", "company")]## $name
## [1] "Sam Tyner"
##
## $company
## [1] "Iowa State University"- Other HTTP verbs –
POST,PUT,DELETE, etc…
Request/response model
- When you (the client) requests a resource from the server. The server responds with a bunch of additional information.
sam$header[1:3]## $server
## [1] "GitHub.com"
##
## $date
## [1] "Thu, 15 Jun 2017 03:48:17 GMT"
##
## $`content-type`
## [1] "application/json; charset=utf-8"- Nowadays content-type is usually XML or JSON (HTML is great for sharing content between people, but it isn’t great for exchanging data between machines.)
Non-HTML Data Formats
What is XML?
XML is a markup language that looks very similar to HTML.
<mariokart>
<driver name="Bowser" occupation="Koopa">
<vehicle speed="55" weight="25"> Wario Bike </vehicle>
<vehicle speed="40" weight="67"> Piranha Prowler </vehicle>
</driver>
<driver name="Peach" occupation="Princess">
<vehicle speed="54" weight="29"> Royal Racer </vehicle>
<vehicle speed="50" weight="34"> Wild Wing </vehicle>
</driver>
</mariokart>- This example shows that XML can (and is) used to store inherently tabular data (thanks Jeroen Ooms)
- What is are the observational units here? How many observations in total?
- 2 units and 6 total observations (4 vehicles and 2 drivers).
XML2R
XML2R is a framework to simplify acquistion of tabular/relational XML.
## # A tibble: 6 x 1
## obs
## <list>
## 1 <chr [1 x 3]>
## 2 <chr [1 x 3]>
## 3 <chr [1 x 2]>
## 4 <chr [1 x 3]>
## 5 <chr [1 x 3]>
## 6 <chr [1 x 2]>##
## mariokart//driver mariokart//driver//vehicle
## 2 4- XML2R coerces XML into a flat list of observations.
- The list names track the “observational unit”.
- The list values track the “observational attributes”.
obs # named list of observations## $`mariokart//driver//vehicle`
## speed weight XML_value
## [1,] "55" "25" " Wario Bike "
##
## $`mariokart//driver//vehicle`
## speed weight XML_value
## [1,] "40" "67" " Piranha Prowler "
##
## $`mariokart//driver`
## name occupation
## [1,] "Bowser" "Koopa"
##
## $`mariokart//driver//vehicle`
## speed weight XML_value
## [1,] "54" "29" " Royal Racer "
##
## $`mariokart//driver//vehicle`
## speed weight XML_value
## [1,] "50" "34" " Wild Wing "
##
## $`mariokart//driver`
## name occupation
## [1,] "Peach" "Princess"collapse_obs(obs) # group into table(s) by observational name/unit## $`mariokart//driver`
## name occupation
## [1,] "Bowser" "Koopa"
## [2,] "Peach" "Princess"
##
## $`mariokart//driver//vehicle`
## speed weight XML_value
## [1,] "55" "25" " Wario Bike "
## [2,] "40" "67" " Piranha Prowler "
## [3,] "54" "29" " Royal Racer "
## [4,] "50" "34" " Wild Wing "- What information have I lost?
- I can’t map vehicles to the drivers!
library(dplyr)
obs <- add_key(obs, parent = "mariokart//driver", recycle = "name")## A key for the following children will be generated for the mariokart//driver node:
## mariokart//driver//vehiclecollapse_obs(obs)## $`mariokart//driver`
## name occupation
## [1,] "Bowser" "Koopa"
## [2,] "Peach" "Princess"
##
## $`mariokart//driver//vehicle`
## speed weight XML_value name
## [1,] "55" "25" " Wario Bike " "Bowser"
## [2,] "40" "67" " Piranha Prowler " "Bowser"
## [3,] "54" "29" " Royal Racer " "Peach"
## [4,] "50" "34" " Wild Wing " "Peach"Now (if I want) I can merge the tables into a single table…
tabs <- collapse_obs(obs)
left_join(as.data.frame(tabs[[1]]), as.data.frame(tabs[[2]])) ## Joining, by = "name"## name occupation speed weight XML_value
## 1 Bowser Koopa 55 25 Wario Bike
## 2 Bowser Koopa 40 67 Piranha Prowler
## 3 Peach Princess 54 29 Royal Racer
## 4 Peach Princess 50 34 Wild WingWhat about JSON?
- JSON is the format for data on the web.
- JavaScript Object Notation (JSON) is comprised of two components:
- arrays => [value1, value2]
- objects => {“key1”: value1, “key2”: [value2, value3]}
- jsonlite is the preferred R package for parsing JSON (it’s even used by Shiny!)
Back to Mariokart
[
{
"driver": "Bowser",
"occupation": "Koopa",
"vehicles": [
{
"model": "Wario Bike",
"speed": 55,
"weight": 25
},
{
"model": "Piranha Prowler",
"speed": 40,
"weight": 67
}
]
},
{
"driver": "Peach",
"occupation": "Princess",
"vehicles": [
{
"model": "Royal Racer",
"speed": 54,
"weight": 29
},
{
"model": "Wild Wing",
"speed": 50,
"weight": 34
}
]
}
]library(jsonlite)##
## Attaching package: 'jsonlite'## The following object is masked from 'package:purrr':
##
## flattenmario <- fromJSON("http://bit.ly/mario-json")
str(mario) ## 'data.frame': 2 obs. of 3 variables:
## $ driver : chr "Bowser" "Peach"
## $ occupation: chr "Koopa" "Princess"
## $ vehicles :List of 2
## ..$ :'data.frame': 2 obs. of 3 variables:
## .. ..$ model : chr "Wario Bike" "Piranha Prowler"
## .. ..$ speed : int 55 40
## .. ..$ weight: int 25 67
## ..$ :'data.frame': 2 obs. of 3 variables:
## .. ..$ model : chr "Royal Racer" "Wild Wing"
## .. ..$ speed : int 54 50
## .. ..$ weight: int 29 34mario$driver## [1] "Bowser" "Peach"mario$vehicles## [[1]]
## model speed weight
## 1 Wario Bike 55 25
## 2 Piranha Prowler 40 67
##
## [[2]]
## model speed weight
## 1 Royal Racer 54 29
## 2 Wild Wing 50 34How do we get two tables (with a common id) like the XML example?
vehicles <- rbind(mario$vehicles[[1]], mario$vehicles[[2]])
vehicles <- cbind(driver = mario$driver, vehicles)Your Turn
- Get the json data for our R workshop GitHub commit history:
workshop_commits_raw <- fromJSON("https://api.github.com/repos/heike/rwrks/commits")Find the table of commits contained in this list. Hint: It’s all about the
$Plot the total number of commits (number of rows) by user as a bar chart
Web Scraping in R
Haley Jeppson, Sam Tyner
June 15, 2017