Webscraping

Seizing Data Efficiently … HTML / CSS / JavaScript, Selenium, and Rvest

Lukas Schmoigl

lukas.schmoigl@wifo.ac.at

Data Scientist at WIFO

Introduction

Getting to know web technologies

Webscraping

• Webscraping is the process of extracting information on the web automatically and transform it into a structured dataset
• Everything you see is (somehow) accessible
• Requires some knowledge about HTML, CSS and JS
• And some help from your best friend is Ctrl+Shift+C

Web Technologies

• HTML and the DOM
  • Hyper Text Markup Language defines the skeleton of webpages and gives elements attributes
  • webpages are hierarchically structured in what is called the Document Object Model (DOM)
  • webscraping: HTML elements usually contain the information we want to scrape

• CSS
  • Cascaded Style Sheets style the appearance of HTML elements
  • webscraping: CSS selectors help you to locate the HTML elements in the DOM

• JS
  • JavaScript is used to manipulate and interact with content in the DOM
  • webscraping: JS helps understand how data is loaded and processed

A Simple Web App

HTML

<h4>The resulting website: </h4>

<form method="GET">
  What is your name: 
  <input type="text" id="myname" />
  <button id="mybutton">Submit</button>
</form>

<div id="hellomessage"></div>

CSS

#mybutton {
  color: red;
  border-radius: 4px;
}

#hellomessage {
  font-weight: bold;
  font-family: "Century Gothic";
}

JavaScript

function ShowHelloMessage() {
  var name = document
    .getElementById("myname");
  document
    .getElementById("hellomessage")
    .innerHTML = "Hi, " + name.value;
}

document
  .getElementById("mybutton")
  .onclick = ShowHelloMessage;

The resulting website:

What is your name:

Webscraping Libraries in R

• Rvest
  • read out whole (static) webpage
  • identify DOM elements via css selectors
  • extract contents and attributes of HTML nodes

• RSelenium
  • control browser from R
  • set step by step instructions of browser actions
  • access information that is not immediately rendered in DOM

Rvest

Reading out static pages

Edikte Data

• Interested in scraping the bankruptcy cases from edikte.gv
• “Data” is unstructured on thousands of different pages
• URL example

URL_base <- "https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/suchedi?"
URL_example <- paste0(
  URL_base,
  "SearchView&subf=vid&SearchOrder=4&SearchMax=4999",
  "&retfields=~SDatArt1=DATAF~SDatWert3=01.12.2022",
  "~SDatWert4=16.12.2022~SDatWert1=~SDatWert2=",
  "&ftquery=&query=_%26__01.12.2022_DATAF_16.12.2022_"
)

page_example <- read_html(URL_example)

example_texts <- page_example |>
  html_elements(".rowlink") |> 
  html_elements("a") |>
  html_text() |>
  as.data.frame() |>
  rename("example_texts" = 1)

example_texts
BG Schwechat, 19 S 39/22p
BG Bregenz, 19 S 27/22z
BG Telfs, 6 S 26/22v
BG Feldkirch, 16 S 16/22i
BG Favoriten, 66 S 47/22i
BG Villach, 18 S 125/22m
LGZ Graz, 25 S 123/22h
HG Wien, 28 S 148/22a
BG Meidling, 19 S 30/22z
BG Hall (in Tirol), 13 S 59/22i
HG Wien, 5 S 171/22i
BG Krems an der Donau, 4 S 52/22t
BG Spittal an der Drau, 6 S 27/22x
LG Wels, 20 S 76/22k

Extracting Attributes

example_links <- page_example |> 
  html_elements(".rowlink") |> 
  html_elements("a") |>
  html_attr("href") %>% # instead of the text, here we extract the attribute "href"
  as.data.frame() |>
  rename("example_links" = 1)

example_links
0/bd0c562652404672c12588cb0073b0cb!OpenDocument
0/913a533e7677528ac12588d20073a995!OpenDocument
0/52a71f7f14a4f52dc12588d100740aaa!OpenDocument
0/ffa869c17dbcb2fec12588de0073bd01!OpenDocument
0/b5bb715f8f8ab86ec12588e80073a334!OpenDocument
0/42f4e8b844359c6bc12588ef007931a9!OpenDocument
0/0ee773dc3a02e704c12588e60074baa7!OpenDocument
0/ba5dbd170e753e71c12588e20074236c!OpenDocument
0/0da7258f728f754dc12588da0073b299!OpenDocument
0/0a55a077a09aa1b6c12588ef007931b7!OpenDocument

Data Overview

example_data <- cbind(example_texts, example_links) |>
  filter(substr(example_texts, 1, 2) %in% c("LG", "BG", "HG")) |>
  mutate(example_links = paste0(
    "https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/", 
    example_links
    ))

example_texts	example_links
BG Schwechat, 19 S 39/22p	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/bd0c562652404672c12588cb0073b0cb!OpenDocument
BG Bregenz, 19 S 27/22z	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/913a533e7677528ac12588d20073a995!OpenDocument
BG Telfs, 6 S 26/22v	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/52a71f7f14a4f52dc12588d100740aaa!OpenDocument
BG Feldkirch, 16 S 16/22i	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/ffa869c17dbcb2fec12588de0073bd01!OpenDocument
BG Favoriten, 66 S 47/22i	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/b5bb715f8f8ab86ec12588e80073a334!OpenDocument
BG Villach, 18 S 125/22m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/42f4e8b844359c6bc12588ef007931a9!OpenDocument

Constructing An Extraction Strategy

date.end.month <- seq(
  as.Date("2007-02-01"), 
  length = 6, 
  by = "months"
  ) - 1
date.begin.month <- seq(
  as.Date("2007-01-01"),
  length = 6, 
  by = "months"
  )
date.end.month <- format(date.end.month, "%d.%m.%Y")
date.proper <- format(date.begin.month, "%Y-%m-%d")  
date.begin.month <- format(date.begin.month, "%d.%m.%Y")

dates <- as.data.frame(cbind(
  date.begin.month, 
  date.end.month, 
  date.proper
  ))

date.begin.month	date.end.month	date.proper
01.01.2007	31.01.2007	2007-01-01
01.02.2007	28.02.2007	2007-02-01
01.03.2007	31.03.2007	2007-03-01
01.04.2007	30.04.2007	2007-04-01
01.05.2007	31.05.2007	2007-05-01
01.06.2007	30.06.2007	2007-06-01

Looping Through Link Table

data_cases <- data.frame(
  case_nr = character(),
  case_link = character(),
  case_address = character(),
  month = character()
  )

for(i in 1:nrow(dates)) {

  temp <- NULL  
  
  URL_ext <- paste0(
    "SearchView&subf=vid&SearchOrder=4&SearchMax=4999&retfields=~SDatArt1=~SDatWert3=~SDatWert4=~SDatWert1=", 
    dates[i,1],
    "~SDatWert2=", 
    dates[i,2],
    "~BMAZ=2&ftquery=&query=_%26__", 
    dates[i,1],
    "_DATBM_", 
    dates[i,2],
    "_"
    )
  
  URL <- paste0(URL_base, URL_ext)
  pg <- read_html(URL)

  case_nr <- pg |>
    html_elements(".rowlink") |> 
    html_elements("a") |>
    html_text() |>
    as.data.frame() |>
    rename("case_nr" = 1)

  case_link <- pg |> 
    html_elements(".rowlink") |> 
    html_elements("a") |>
    html_attr("href") |>
    as.data.frame() |>
    rename("case_link" = 1)
  
  case_address <- pg |>
    html_element("table") |> 
    html_table() |>
    rename(case_debtor = 3) |>
    mutate(case_address = str_extract(case_debtor, "\\d{4}")) |>
    select(case_address) 
    
  temp <- cbind(case_nr, case_link, case_address) |>
    mutate(case_link = paste0("https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/", case_link)) |>
    mutate(month = dates[i,3])
  
  data_cases <- data_cases |> add_row(temp)
  
  if (i %% 1 == 0) {
    print(paste0("Total: ", nrow(dates), ", Current: ", i))
  }

}

case_nr	case_link	case_address	month
BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01
LG Wiener Neustadt, 11 S 33/04w	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/3420fc6dc4dde959c1257a6c00635f6f!OpenDocument	2640	2007-01-01
BG Bregenz, 19 S 1/07d	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/b5fcc3e511121da9c1257a6c007055fb!OpenDocument	6921	2007-01-01
BG Favoriten, 41 S 20/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/2b41a64fdbc1d437c1257a6c0057f68b!OpenDocument	1100	2007-01-01
BG Linz, 26 S 101/06k	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/32ee2fcd7318d6e4c1257a6c006531d1!OpenDocument	4020	2007-01-01
BG Josefstadt, 11 Se 10/06w	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/139b5e83e35ed751c1257a6c005c4c39!OpenDocument	1090	2007-01-01

Extract Info from Case Pages

example_info <- read_html(example_data$example_links[2]) %>%
  html_elements("dl , div.zeilehead") %>%
  html_text() %>%
  as.data.frame() %>%
  mutate(Sub = sapply(str_split(., ":"), "[[", 1)) %>%
  mutate(Info = gsub("^.*?:", "", .)) %>%
  select(-.)

example_type <- read_html(example_data$example_links[2]) |>
  html_elements("#Verfahren") |>
  html_text()

Sub	Info
Bekannt gemacht am 5. Oktober 2022	Bekannt gemacht am 5. Oktober 2022
Schuldner	AktanVorname:MericLindauerstraße 95/126912 HörbranzGebdat: 06.08.1982
Beteiligter	Sylvester Inkert p.A. Verein Neuanfang Vertreter des Schuldners Rheinstr. 32 6900 Bregenz Tel.: 0699/88448467, Fax: 05574/61544
Eröffnung	Beginn der Wirkungen der Eröffnung: 06.10.2022 Anmeldungsfrist: 05.12.2022
Geringfügig	Das Schuldenregulierungsverfahren ist geringfügig.
Eigenverwaltung	Eigenverwaltung des Schuldners.

Looping Through Case Pages

data_info <- data.frame(
  info = character(),
  text = character(),
  type = character(),
  case_nr = character(),
  case_link = character(),
  case_address = character(),
  month = character()
  )

for(i in 1:nrow(data_cases)) {
  
  temp <- NULL
  
  temp <- read_html(data_cases$case_link[i]) %>%
    html_elements("dl , div.zeilehead") %>%
    html_text() %>%
    as.data.frame() %>%
    mutate(info = sapply(str_split(., ":"), "[[", 1)) %>%
    mutate(text = gsub("^.*?:", "", .)) %>%
    select(-.)
  
  type <- read_html(data_cases$case_link[i]) |>
    html_elements("#Verfahren") |>
    html_text() 

  temp$type <- type[length(type)] 
  temp$case_nr <- data_cases$case_nr[i]
  temp$case_link <- data_cases$case_link[i]
  temp$case_address <- data_cases$case_address[i]
  temp$month <- data_cases$month[i]
  
  data_info <- data_info |> add_row(temp)
  
  if (i %% 100 == 0) {
    print(paste0("Total: ", nrow(data_cases), ", Current: ", i))
  }
  
}

info	text	type	case_nr	case_link	case_address	month
Bekannt gemacht am 1. Juni 2006	Bekannt gemacht am 1. Juni 2006	Schuldenregulierungsverfahren	BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01
Schuldner	AbfaltererVorname:ChristophArbeiterWiesenweg 56410 TelfsGebdat: 24.06.1972	Schuldenregulierungsverfahren	BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01
Eröffnung	Eröffnung des Schuldenregulierungsverfahrens: 01.06.2006 Anmeldungsfrist: 01.08.2006	Schuldenregulierungsverfahren	BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01
Geringfügig	Das Schuldenregulierungsverfahren ist geringfügig.	Schuldenregulierungsverfahren	BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01
Eigenverwaltung	Eigenverwaltung des Schuldners.	Schuldenregulierungsverfahren	BG Telfs, 6 S 12/06m	https://edikte.justiz.gv.at/edikte/id/idedi8.nsf/0/5511ee0578636699c1257a6c006f0fd8!OpenDocument	6410	2007-01-01

RSelenium

Taking control of your browser via R

Opening a Selenium Client

• Versioning often critical
• Running from Docker container is recommended

selenium_server <- rsDriver(
  browser = "firefox", 
  chromever = NULL,
  verbose = FALSE, 
  port = 3333L,
  check = FALSE
  )

client <- selenium_server$client

• Some basic controls

client$open()
client$setWindowSize(600, 600)
client$navigate("https://edikte.justiz.gv.at")
client$refresh()
client$close()

Revisiting Edikte Data

• Automate actions in browser with Selenium

client$navigate(URL_base)

element <- client$findElement(using = "link text", "Erweiterte Suche")
element$getElementAttribute("href")
element$clickElement()

search_field <- client$findElement(using = "name", "SchuldnerS")
search_field$sendKeysToElement(list("Lukas Schmoigl", key = "enter"))

client$executeScript("window.scrollTo(0,document.body.scrollHeight);")

STATatlas

• Scraping of STATatlas more difficult
• Data is rendered on canvas rather than directly in DOM
• However data point rendered into tooltip on hover

Extraction Strategy

Sys.sleep(2)
client$navigate("https://www.statistik.at/atlas/")

element <- client$findElement(
  using = "css selector", 
  ".them_icon_wrap:nth-child(5) img"
  )
element$clickElement()

element <- client$findElement(
  using = "css selector", 
  ".map_link:nth-child(4) p"
  )
element$clickElement()

search_field <- client$findElement(
  using = "id", 
  "filterBB"
  )
search_field$sendKeysToElement(list(
  "4820", 
  key = "enter")
  )

search_prompt <- client$findElement(
  using = "link text", 
  "4820"
  )
search_prompt$clickElement()

element <- client$findElement(
  using = "css selector", 
  ".ol-unselectable"
  )
element$clickElement()

hover_info <- client$findElement(
  using = "id", 
  "div_feature_info_title"
  )
hover_info$getElementText()

Looping

for(i in 1:length(postcodes)) {

  client$navigate("https://www.statistik.at/atlas/")

  element <- client$findElement(
    using = "css selector", 
    ".them_icon_wrap:nth-child(5) img"
    )
  
  element$clickElement()
  
  element <- client$findElement(
    using = "css selector", 
    ".map_link:nth-child(4) p"
    )
  
  element$clickElement()
  
  search_field <- client$findElement(
    using = "id", 
    "filterBB"
    )
  
  search_field$sendKeysToElement(
    list(
      postcodes[i], 
      key = "enter"
      )
    )
  
  Sys.sleep(1) # zoom takes some time
  
  search_prompt <- client$findElement(
    using = "link text", 
    postcodes[i]
    )
  
  search_prompt$clickElement()
  
  element <- client$findElement(
    using = "css selector", 
    ".ol-viewport > .ol-unselectable"
    )
  
  element$clickElement()
  
  hover_info <- client$findElement(
    using = "id", 
    "div_feature_info_title"
    )
  
  Sys.sleep(1)
  datum <- hover_info$getElementText()[[1]]
  print(datum)
  data_debt <- rbind(data_debt, datum)

}

Gemeinde
Bad Ischl (40703): 1 783 €/EW
Hallwang (50316): 0 €/EW
Schwarzenau (32524): 1 920 €/EW
Türnitz (31414): 1 697 €/EW
Mittelberg (80228): 3 182 €/EW
Lienz (70716): 895 €/EW
Gmunden (40705): 2 272 €/EW
Salzburg (50101): 168 €/EW
Neulengbach (31926): 3 097 €/EW
Güssing (10405): 3 212 €/EW

Legal and Ethical Aspects

Trying not to get into trouble

Considerations in Advance

• Check if full data is loaded / linked
• Ask the owner for the data or API access
• Look for robots.txt or use polite
• Scraping may impede access to important services

Legality

• No specific laws prohibiting scraping of (openly available) info from web
  • Terms of use of internet platforms and services etc. may be violated
  • Data protection laws may be an issue
  • Intellectual property rights may be an issue

Running Into Problems

• You might get some unpleasant mail

Assignment I

• Webscraping - your turn
  1. Find data on the internet you are interested in
  2. Check if you are allowed to scrape it (robots.txt)
  3. Scrape the website
  4. Prepare the data so that it is ready for data analysis (descriptives and or viz)

Due date: November 7th