Scraping

Misc

  • {rvest}
  • {rselenium}
  • {selenium}
  • {selenider} - Wrapper functions around {chromote} and {selenium} functions that utilize lazy element finding and automatic waiting to make scraping code more reliable
  • {shadowr} - For shadow DOMs
  • {rJavaEnv} - Quickly install Java Development Kit (JDK) without administrative privileges and set environment variables in current R session or project to solve common issues with ‘Java’ environment management in ‘R’.
  • {robotstxt} - Provides functions to download and parse ‘robots.txt’ files.

    • Unofficial Guidelines

      • Finding no robots.txt file at the server (e.g. HTTP status code 404) implies that everything is allowed
      • Subdomains should have there own robots.txt file if not it is assumed that everything is allowed
      • Redirects involving protocol changes - e.g. upgrading from http to https - are followed and considered no domain or subdomain change - so whatever is found at the end of the redirect is considered to be the robots.txt file for the original domain
      • Redirects from subdomain www to the domain is considered no domain change - so whatever is found at the end of the redirect is considered to be the robots.txt file for the subdomain originally requested

    • Example

      robotstxt::get_robotstxt("https://www.cimls.com")
#> [robots.txt]
#> --------------------------------------
#> 
#> Sitemap: https://www.cimls.com/sitemaps/sitemap-index.xml
#> 
#> User-agent: *
#> Disallow: /view-map.php
#> Disallow: /data_provider/broker_website.php
#> Disallow: /admin/*
#> Disallow: /login/*
#> Allow: /

robotstxt::paths_allowed(
  paths  = "external-data/datafiniti-api.php",
  domain = "www.cimls.com",
  bot    = "*"
)
#>  www.cimls.com                      
#> 
#> [1] TRUE
      • The robots.txt does seems to be okay with my calling its api
      • TRUE means bots have permission to access the page
  • {polite} (article) - Uses three principles of polite webscraping: seeking permission, taking slowly and never asking twice.

    • Specifically, it manages the http session, declares the user agent string and checks the site policies, and uses rate-limiting and response caching to minimize the impact on the webserver.

    • Creates a session with bow, requests a page with nod, and pulls the contents of the page with scrape.

    • Example

      library(polite)
library(rvest)

session <- bow("https://www.cheese.com/by_type", force = TRUE)
result <- scrape(session, 
                 query=list(t = "semi-soft", 
                            per_page = 100)) |> 
  html_node("#main-body") |> 
  html_nodes("h3") |> 
  html_text()

head(result)
#> [1] "3-Cheese Italian Blend"  "Abbaye de Citeaux"      
#> [3] "Abbaye du Mont des Cats" "Adelost"                
#> [5] "ADL Brick Cheese"        "Ailsa Craig"

  • Waiting for stuff to load

    • {selenider} fixes this problem

    • In loops, use Sys.sleep (probably) after EVERY selenium function. Sys.sleep(1) might be all that’s required.

      • See Projects > foe > gb-level-1_9-thread > scrape-gb-levels.R
      • Might not always be needed, but absolutely need if you’re filling out a form and submitting it.
      • Might even need one at the top of the loop
      • If a Selenium function stops working, adding Sys.sleeps are worth a try.

    • Using a while-loop in order to account for uncertain loading times

      • Example: (source)

        for (page_index in 1:2348) {

  # Try to find the buttons "Ver Mais"
  all_buttons_loaded <- FALSE
  iterations <- 0
  while(!all_buttons_loaded & iterations < 20) {
    tryCatch(
      {
        test <- remote_driver$
          findElements(using = 'id', value = "link_ver_detalhe")

        if (inherits(test, "list") && length(test) > 0)  {
          all_buttons_loaded <<- TRUE
        }
      },
      error = function(e) {
        iterations <<- iterations + 1 
        Sys.sleep(0.5)
      }
    )
  }

  if (!all_buttons_loaded & iterations == 20) {
    next
  }

  # ... more stuff

}

  • Keeping track of progress can help to find where the error occurred (source)

    • Areas that indicate stages within a scraping script

      • Show which page is being scraped;
      • Show which modal of this page is being scraped;
      • Show the status of this scraping (success/failure).

    • Example:

      # save calls to message() in an external file
log_appender(appender_file("data/modals/00_logfile"))
log_messages()

for (page_index in 1:2348) {

  message(paste("Start scraping of page", page_index))

  for (modal_index in buttons) {
    # open modal
    # get HTML and save it in an external file
    # leave modal

    message(paste("  Scraped modal", modal_index))
  }

  # Once all modals of a page have been scraped, go to the next page (except
  # if we're on the last page)

  message(paste("Finished scraping of page", page_index))
}

  • Best practice to scrape the html page and clean it in separate scripts

    • Keeping the raw html files helps with reproducibility

    • Example:

      buttons[[modal_index]]$clickElement()

Sys.sleep(1.5)

# Get the HTML and save it
tmp <- remote_driver$getPageSource()[[1]]
write(tmp, file = paste0("data/modals/page-", page_index, "-modal-", modal_index, ".html"))

# Leave the modal
body <- remote_driver$findElement(using = "xpath", value = "/html/body")
body$sendKeysToElement(list(key = "escape"))

  • Sometimes clickElement( ) stops working for no apparent reason. When this happens used sendKeysToElement(list("laptops",key="enter"))

  • In batch scripts (.bat), sometimes after a major windows update, the Java that selenium uses will trigger Windows Defender (WD) and cause the scraping script to fail (if you have it scheduled). If you run the .bat script manually and then when the WD box rears its ugly head, just click ignore. WD should remember after that and not to mess with it.

  • RSelenium findElement(using = "") options “class name” : Returns an element whose class name contains the search value; compound class names are not permitted.

    • “css selector” : Returns an element matching a CSS selector.

    • “id” : Returns an element whose ID attribute matches the search value.

    • “name” : Returns an element whose NAME attribute matches the search value.

    • “link text” : Returns an anchor element whose visible text matches the search value.

    • “partial link text” : Returns an anchor element whose visible text partially matches the search value.

    • “tag name” : Returns an element whose tag name matches the search value.

    • “xpath” : Returns an element matching an XPath expression.

Terms

  • Static Web Page: A web page (HTML page) that contains the same information for all users. Although it may be periodically updated, it does not change with each user retrieval.

  • Dynamic Web Page: A web page that provides custom content for the user based on the results of a search or some other request. Also known as “dynamic HTML” or “dynamic content”, the “dynamic” term is used when referring to interactive Web pages created for each user.

rvest

  • Misc

    • Notes from: Pluralsight.Advanced.Web.Scraping.Tactics.R.Playbook

  • Uses css selectors or xpath to find html nodes

    library(rvest)
page <- read_html("<url>")
node <- html_element(page, xpath = "<xpath>"
    • Find css selectors
      • selector gadget
        1. click selector gadget app icon in Chrome in upper right assuming you’ve installed it already
        2. click item on webpage you want to scrape
          • it will highlight other items as well
        3. click each item you DON’T want to deselect it
        4. copy the selector name in box at the bottom of webpage
        5. Use html_text to pull text or html_attr to pull a link or something
      • inspect
        1. right-click item on webpage
        2. click inspect
        3. html element should be highlighted in elements tab of right side pan
        4. right-click element –> copy –> copy selector or copy xpath

  • Example: Access data that needs authentication (also see RSelenium version)

    • navigate to login page

      session <- session("<login page url>")

    • Find “forms” for username and password

      form <- html_form(session)[[1]]
form
      • Evidently there are multiple forms on a webpage. He didn’t give a good explanation for why he chose the first one
      • “session_key” and “session_password” are the ones needed

    • Fill out the necessary parts of the form and send it

      filled_form <- html_form_set(form, session_key = "<username>", session_password = "<password>")
filled_form # shows values that inputed next the form sections
log_in <- session_submit(session, filled_form)

    • Confirm that your logged in

      log_in # prints url status = 200, type = text/html, size = 757813 (number of lines of html on page?)
browseURL(log_in$url) # think this maybe opens browser

  • Example: Filter a football stats table by selecting values from a dropdown menu on a webpage (also see RSelenium version)

    • After set-up and navigating to url, get the forms from the webpage

      forms <- html_form(session)
forms # prints all the forms
      • The fourth has all the filtering menu categories (team, week, position, year), so that one is chosen

    • Fill out the form to enter the values you want to use to filter the table and submit that form to filter the table

      filled_form <- html_form_set(forms[[4]], "team" = "DAL", "week" = "all", "position" = "QB", "year" = "2017")
submitted_session <- session_submit(session = session, form = filled_form)

    • Look for the newly filtered table

      tables <- html_elements(submitted_session, "table")
tables
      • Using inspect, you can see the 2nd one has <table class = “sortable stats-table…etc

    • Select the second table and convert it to a dataframe

      football_df <- html_table(tables[[2]], header = TRUE)

  • Retrieve Sidebar Content (source)

    chrome_session <- ChromoteSession$new()

# Retrieve the sidebar content
node <- chrome_session$DOM$querySelector(
  nodeId = chrome_session$DOM$getDocument()$root$nodeId,
  selector = ".sidebar"
)

# Get the outerHTML of the node
html_content <- chrome_session$DOM$getOuterHTML(
  nodeId = node$nodeId
)

## Parse the sidebar content with `rvest` ----

# Pull the node's HTML response
html_content$outerHTML |>      # Extract the HTML content
  rvest::minimal_html() |>     # Convert to XML document
  rvest::html_elements("a") |> # Obtain all anchor (i.e. links) tags
  rvest::html_text()           # Extract the text from the anchor tags

    • Every element in the sidebar pretty much has a link, so the text can extracted from them.

    • The CSS selector was much longer but he shortened it to “.sidebar”

RSelenium

  • Use Selenium if:

    • The HTML you want is not directly accessible, i.e needs some interactions (clicking on a button, connect to a website…),
    • The URL doesn’t change with the inputs,
    • You can’t access the data directly in the “network” tab of the console and you can’t reproduce the POST request.

  • Along with installing package you have to know the version of the browser driver of the browser you’re going to use

    • https://chromedriver.chromium.org/downloads

    • Find Chrome browser version

      • Through console

        system2(command = "wmic",
        args = 'datafile where name="C:\\\\Program Files         (x86)\\\\Google\\\\Chrome\\\\Application\\\\chrome.exe" get Version /value')

    • List available Chrome drivers

      binman::list_versions(appname = "chromedriver")
      • If no exact driver version matches your browser version,
        • Each version of the Chrome driver supports Chrome with matching major, minor, and build version numbers.
        • Example: Chrome driver 73.0.3683.20  supports all Chrome versions that start with 73.0.3683

  • Start server and create remote driver

    • a browser will pop up and say “Chrome is being controlled by automated test software”
    library(RSelenium)
driver <- rsDriver(browser = c("chrome"), chromever = "<driver version>", port = 4571L) # assume the port number is specified by chrome driver ppl.
remDr <- driver[['client']] # can also use $client

  • Navigate to a webpage

    remDr$navigate("<url>")

  • remDR$maxWindowSize(): Set the size of the browser window to maximum.

    • By default, the browser window size is small, and some elements of the website you navigate to might not be available right away

  • Grab the url of the webpage you’re on

    remDr$getCurrentUrl()

  • Go back and forth between urls

    remDr$goBack()
remDr$goForward()

  • Find html element (name, id, class name, etc.)

    webpage_element <- remDr$findElement(using = "name", value = "q")
    • See Misc section for selector options
    • Where “name” is the element class and “q” is the value e.g. name=“q” if you used the inspect method in chrome
    • Also see Other Stuff >> Shadow DOM elements >> Use {shadowr} for alternate syntax to search for web elements

  • Highlight element in pop-up browser to make sure you have the right thing

    webpage_element$highlightElement()

  • Example: you picked a search bar for your html element and now you want to use the search bar from inside R

    • Enter text into search bar

      webpage_element$sendKeysToElement(list("Scraping the web with R"))

    • Hit enter to execute search

      webpage_element$sendKeysToElement(list(key = "enter"))
      • You are now on the page with the results of the google search

    • Scrape all the links and titles on that page

      webelm_linkTitles <- remDr$findElement(using = "css selector", ".r")

      • Inspect showed ”

        . Notice he used “.r”. Says it will pick-up all elements with “r” as the class.

    • Get titles

      # first title
webelm_linkTitles[[1]]$getElementText()

# put them all into a list
titles <- purrr::map_chr(webelm_linkTitles, ~.x$getElementText())
titles <- unlist(lapply(
    webelm_linkTitles, 
    function(x) {x$getElementText()}

  • Example: Access data that needs user authentication (also see rvest version)

    • After set-up and navigating to webpage, find elements where you type in your username and password

      webelm_username <- remDr$findElement(using = "id", "Username")
webelm_pass <- remDr$findElement(using = "id, "Password")

    • Enter username and password

      webpage_username$sendKeysToElement(list("<username>"))
webpage_pass$sendKeysToElement(list("<password>"))

    • Click sign-in button and click it

      webelm_sbutt <- remDr$findElement(using = "class", "psds-button")
webelm_sbutt$clickElement()

  • Example: Filter a football stats table by selecting values from a dropdown menu on a webpage (also see rvest version)

    • This is tedious — use rvest to scrape this if possible (have to use rvest at the end anyways). html forms are the stuff.

    • After set-up and navigated to url, find drop down “team” menu element locator using inspect in the browser and use findElement

      webelem_team <- remDr$findElement(using = "name", value = "team") # conveniently has name="team" in the html
      • Also see Other Stuff >> Shadow DOM elements >> Use {shadowr} for alternate syntax to search for web elements

    • click team dropdown

      webelem_team$clickElement()

    • Go back to inspect in the browser, you should be able to expand the team menu element. Left click value that you want to filter team by to highlight it. Then right click the element and select “copy” –> “copy selector”. Paste selector into value arg

      webelem_DAL <- remDr$findElement(using = "css", value = "edit-filters-0-team > option:nth-child(22)")
webelem_DAL$clickElement()
      • Also see Other Stuff >> Shadow DOM elements >> Use {shadowr} for alternate syntax to search for web elements
      • Repeat process for week, position, and year drop down menu filters

    • After you’ve selected all the values in the dropdown, click the submit button to filter the table

      webelem_submit <- remDr$findElement(using = "css", value =     "edit-filters-0-actions-submit") 
webelem_submit$clickElement()
      • Finds element by using inspect on the submit button and copying the selector

    • Get the url of the html code of the page with the filtered table. Read html code into R with rvest.

      url <- remDr$getPageSource()[[1]]
html_page <- rvest::read_html(url)
      • If you want the header, getPageSource(header = TRUE)

    • Use rvest to scrape the table. Find the table with the stats

      all_tables <- rvest::html_elements(html_page, "table")
all_tables
      • Used the “html_elements” version instead of “element”
      • Third one has “<table class =”sortable stats-table full-width blah blah”

    • Save to table to dataframe

      football_df <- rvest::html_table(all_tables[[3]], header = TRUE)

Other Stuff

  • Clicking a semi-infinite scroll button (e.g. “See more”)

    • Example: For-Loop

      # Find Page Element for Body
webElem <- remDr$findElement("css", "body")

# Page to the End
for (i in 1:50) {
  message(paste("Iteration",i))
  webElem$sendKeysToElement(list(key = "end"))

  # Check for the Show More Button
  element<- try(unlist(
      remDr$findElement(
        "class name",
        "RveJvd")$getElementAttribute('class')), silent = TRUE)

  #If Button Is There Then Click It
  Sys.sleep(2)
  if(str_detect(element, "RveJvd") == TRUE){
    buttonElem <- remDr$findElement("class name", "RveJvd")
    buttonElem$clickElement()
  }

  # Sleep to Let Things Load
  Sys.sleep(3)
}
      • article
      • After scrolling to the “end” of the page, there’s a “show me more button” that loads more data on the page

    • Example: Recursive

      load_more <- function(rd) {
  # scroll to end of page
  rd$executeScript("window.scrollTo(0, document.body.scrollHeight);", args = list())

  # Find the "Load more" button by its CSS selector and ...
  load_more_button <- rd$findElement(using = "css selector", "button.btn-load.more")

  # ... click it
  load_more_button$clickElement()

  # give the website a moment to respond
  Sys.sleep(5)
}

load_page_completely <- function(rd) {
  # load more content even if it throws an error
  tryCatch({
    # call load_more()
    load_more(rd)
    # if no error is thrown, call the load_page_completely() function again
    Recall(rd)
  }, error = function(e) {
    # if an error is thrown return nothing / NULL
  })
}

load_page_completely(remote_driver)
      • article
      • Recall is a base R function that calls the same function it’s in.

    • Example: While-Loop with scroll height (source)

      progressive_scroll <- function(browser, scroll_step = 100) {
  # Get initial scroll height of the page
  current_height <- browser$executeScript("return document.body.scrollHeight")

  # Set a variable for the scrolling position
  scroll_position <- 0

  # Continue scrolling until the end of the page
  while (scroll_position < current_height) {
    # Scroll down by 'scroll_step' pixels
    browser$executeScript(paste0("window.scrollBy(0,", scroll_step, ");"))

    Sys.sleep(runif(1, max = 0.2)) # Wait for the content to load (adjust this if the page is slower to load)
    scroll_position <- scroll_position + scroll_step # Update the scroll position
    current_height <- browser$executeScript("return document.body.scrollHeight") # Get the updated scroll height after scrolling (in case more content is loaded)
  }
}

# Scroll the ECB page to ensure all dynamic content is visible
progressive_scroll(browser, scroll_step = 1000)

  • Shadow DOM elements

    • #shadow-root and shadow dom button elements

    • Misc

      • Two options: {shadowr} or JS script

    • Example: Use {shadowr}

      • My stackoverflow post

      • Set-up

        pacman::p_load(RSelenium, shadowr)
driver <- rsDriver(browser = c("chrome"), chromever = chrome_driver_version)
# chrome browser
chrome <- driver$client
shadow_rd <- shadow(chrome)

      • Find web element

        • Search for element using html tag
        wisc_dl_panel_button4 <- shadowr::find_elements(shadow_rd, 'calcite-button')
wisc_dl_panel_button4[[1]]$clickElement()
        • Shows web element located in #shadow-root
        • Since there might be more than one element with the “calcite-button” html tag, we use the plural, find_elements, instead of find_element
        • There’s only 1 element returned, so we use [[1]] index to subset the list before clicking it

      • Search for web element by html tag and attribute

        wisc_dl_panel_button3 <- find_elements(shadow_rd, 'button[aria-describedby*="tooltip"]')
wisc_dl_panel_button3[[3]]$clickElement()
        • “button” is the html tag which is subsetted by the brackets, and “aria-describedby” is the attribute
        • Only part of the attribute’s value is used, “tooltip,” so I think that’s why “*=” instead of just “=” is used. I believe the “*” may indicate partial-matching.
        • Since there might be more than one element with this  html tag + attribute combo, we use the plural, find_elements, instead of find_element
        • There are 3 elements returned, so we use [[3]] index to subset the list to element we want before clicking it

    • Example: Use a JS script and some webelement hacks to get a clickable element

      • Misc
        • “.class_name”
          • fill in spaces with periods
            • “.btn btn-default hidden-xs” becomes “.btn.btn-default.hidden-xs”
      • You can find the element path to use in your JS script by going step by step with JS commands in the Chrome console (bottom window)
      • Steps

        • Write JS script to get clickable element’s elementId

          1. Start with element right above first shadow-root element and use querySelector

          2. Move to the next element inside the next shadow-root element using shadowRoot.querySelector

          3. Continue to desired clickable element

            • If there’s isn’t another shadow-root that you have to open, then the next element can be selected usingquerySelector
            • If you do have to click on another shadow-root element to open another branch, then used shadowRoot.querySelector
            • Example
              • “hub-download-card” is just above shadow-root so it needs querySelector
              • “calcite-card” is an element that’s one-step removed from shadow-root, so it needs shadowRoot.querySelector
              • “calcite-dropdown” (type = “click”) is not directly (see div) next to shadow-root , so it can selected using querySelector

          4. Write and execute JS script

            wisc_dlopts_elt_id <- chrome$executeScript("return document.querySelector('hub-download-card').shadowRoot.querySelector('calcite-card').querySelector('calcite-dropdown');")

        • Make a clickable element or just click the damn thing

          • clickable element (sometimes this doesn’t work; needs to be a button or type=click)

            1. Use findElement to find a generic element class object that you can manipulate
            2. Use “@” ninja-magic to force elementId into the generic webElement to coerce it into your button element
            3. Use clickElement to click the button
            # think this is a generic element that can always be used
moose <- chrome$findElement("css", "html")
moose@.xData$elementId <- as.character(wisc_dlopts_elt_id)
moose$clickElement()

        • Click the button

          chrome$executeScript("document.querySelector('hub-download-card').shadowRoot.querySelector('calcite-card').querySelector('calcite-dropdown').querySelector('calcite-dropdown-group').querySelector('calcite-dropdown-item:nth-child(2)').click()")

  • Get data from a hidden input

    • article

    • HTML Element

      <input type="hidden" id="overview-about-text" value="%3Cp%3E100%25%20Plant-Derived%20Squalane%20hydrates%20your%20skin%20while%20supporting%20its%20natural%20moisture%20barrier.%20Squalane%20is%20an%20exceptional%20hydrator%20found%20naturally%20in%20the%20skin,%20and%20this%20formula%20uses%20100%25%20plant-derived%20squalane%20derived%20from%20sugar%20cane%20for%20a%20non-comedogenic%20solution%20that%20enhances%20surface-level%20hydration.%3Cbr%3E%3Cbr%3EOur%20100%25%20Plant-Derived%20Squalane%20formula%20can%20also%20be%20used%20in%20hair%20to%20increase%20heat%20protection,%20add%20shine,%20and%20reduce%20breakage.%3C/p%3E">

    • Extract value and decode the text

      overview_text <- webpage |>
  html_element("#overview-about-text") |>
  html_attr("value") |>
  URLdecode() |>
  read_html() |>
  html_text()

overview_text
#> [1] "100% Plant-Derived Squalane hydrates your skin while supporting its natural moisture barrier.

JS

  • Highlight an element on the page (source)

    chrome_session <- ChromoteSession$new()

# Launch chrome to view actions taken in the browser
chrome_session$view()

# Get the browser's version
chrome_session$Browser$getVersion()

# Open a new tab and navigate to a URL
chrome_session$Page$navigate("https://www.r-project.org/")

chrome_session$Runtime$evaluate(
  expression = "
  // Find the element
  element = document.querySelector('.sidebar');

  // Highlight it
  element.style.backgroundColor = 'yellow';
  element.style.border = '2px solid red';
  "
)

# Wait for the action to complete
Sys.sleep(0.5)

# Take a screenshot of the highlighted element
chrome_session$screenshot("r-project-sidebar.png", selector = ".sidebar")

# View the screenshot
browseURL("r-project-sidebar.png")
    • The css selector was some long string, but he shortened it to “.sidebar”

  • Search and extract table (source)

    # Start a new browser tab session
chrome_session_windy <- chrome_session$new_session()

# Open a new tab in the current browser
chrome_session_windy$view()

## Navigate to windy.com ----

# Navigate to windy.com
chrome_session_windy$Page$navigate("https://www.windy.com")

# Wait for the page to load
Sys.sleep(0.5) 
# First focus the input field
chrome_session_windy$Runtime$evaluate('
  document.querySelector("#q").focus();
')

# Brief pause to ensure focus is complete
Sys.sleep(0.5) 

# Enter search term and trigger search
search_query <- 'Stanford University Museum of Art'
chrome_session_windy$Runtime$evaluate(
  expression = sprintf('{
    // Get the search input
    const searchInput = document.getElementById("q");
    searchInput.value = "%s";

    // Focus the input
    searchInput.focus();

    // Trigger input event
    const inputEvent = new Event("input", { bubbles: true });
    searchInput.dispatchEvent(inputEvent);

    // Trigger change event
    const changeEvent = new Event("change", { bubbles: true });
    searchInput.dispatchEvent(changeEvent);

    // Force the search to update - this triggers the site\'s search logic
    const keyupEvent = new KeyboardEvent("keyup", {
      key: "a",
      code: "KeyA",
      keyCode: 65,
      bubbles: true
    });
    searchInput.dispatchEvent(keyupEvent);
  }', search_query)
)
# Wait for and, then, click the first search result
Sys.sleep(0.5) 
chrome_session_windy$Runtime$evaluate('
    document.querySelector(".results-data a").click();
  ')

## Extract weather data ----

# Wait for and, then, extract the weather data table
Sys.sleep(0.5) 
html <- chrome_session_windy$Runtime$evaluate('
    document.querySelector("table#detail-data-table").outerHTML
  ')$result$value

## Parse the table using `rvest` ----
raw_weather_table <- html |>
  read_html() |> 
  html_node('table') |> # Select the table to extract it without getting a node set
  html_table() |> # Convert the table to a data frame
  as.data.frame()

raw_weather_table

    • %s is replaced by search_query

    • #detail-data-table is the CSS selector but he added table in front of that — not sure why. Without the “#”, this is also the table id in the HTML, so maybe because it’s a table id (?).

    • Feel like this might’ve been solved by RSelenium and the JS wasn’t necessary

API

POST

  • Also see API >> Request Methods >> POST
  • Sometimes dynamically served html tables can be scraped via a simple POST request avoiding Selenium procedures
  • Example: Dynamic Table (source)

    • Get API Parameters From Network Tab

      • The user sets some inputs like city and date range, and clicks the hourglass button to submit. Then this table pops up below.

      • Input parameters and monitor the network tab

      • What we want is search_lib.php. It has a POST request method in the Headers tab, and the Preview tab shows table we want
      • The request URL is also under the Headers tab at the top.
      • The json file may look interesting since data often comes in json, but t’s just some table format settings I think.


      • Under the Payload tab with view parsed selected, there’s a nice clean list of parameters that are the inputs we set in the first step
        • Image appears to show view source selected, but that’s just because it changes to the other one once selected
      • With view source selected, we see the query string that’s used to encapsulate the inputs.

      fetch("http://historico.oepm.es/logica/search_lib.php", {
  "headers": {
    "accept": "*/*",
    "accept-language": "en-US,en;q=0.9",
    "content-type": "application/x-www-form-urlencoded; charset=UTF-8",
    "x-requested-with": "XMLHttpRequest"
  },
  "referrer": "http://historico.oepm.es/buscador.php",
  "referrerPolicy": "strict-origin-when-cross-origin",
  "body": "cadena=Madrid&tb=SPH_MATCH_ALL&rangoa=1826%2C1966&indexes%5B%5D=privilegios&indexes%5B%5D=patentes&indexes%5B%5D=patentes_upm&indexes%5B%5D=marcas&timestamp=Thu Dec 26 2024 08:49:42 GMT-0500 (Eastern Standard Time)",
  "method": "POST",
  "mode": "cors",
  "credentials": "include"
});
      • Right-clicking search_lib.php gives us some options. Select “Copy as fetch.”
      • Under “headers”, we see the necessary header parameters that are required: “accept”, “accept-language”, “content-type”, and “x-requested-with”.
      • No idea what these mean or why these are the particular ones required
      • There is a list of other parameters that could probably be added. You can find them in the Headers tab underneath Request Headers or by selecting Copy Request Headers as seen in the image.
        • By using “Copy as fetch,” we only get the absolutely necessary ones (I think).

    • Call API and Get the Data

      library(httr)       # make POST requests
library(polite)     # be polite when we scrape
library(rvest)      # extract HTML tables

city <- "madrid"
year1 <- 1850
year2 <- 1870

query <- paste0(
  "cadena=", city, "&tb=SPH_MATCH_ALL&rangoa=", year1, "%2C", year2,
  "&indexes%5B%5D=privilegios&indexes%5B%5D=patentes&indexes%5B%5D",
  "=patentes_upm&indexes%5B%5D=marcas"
      • We choose some input values that we desire and place them in appropriate spots of our query string.
      # 
polite_POST <- politely(POST, verbose=TRUE) 
POST_response <- polite_POST(
  "http://historico.oepm.es/logica/search_lib.php",
  add_headers(
    "accept" = "*/*",
    "accept-language" = "en-GB,en-US;q=0.9,en;q=0.8",
    "content-type" = "application/x-www-form-urlencoded; charset=UTF-8",
    "x-requested-with" = "XMLHttpRequest"
  ),
  body = query
)
      • politely tells the website who is performing the requests and to add a delay between requests (here we only do one)
      • In POST, we set the request URL, the header parameters, the finalized query string.
      content(POST_response, "parsed") |> 
  html_table() |> 
  head(n = 5)
#> [[1]]
#> # A tibble: 12 × 7
#>    ``    TIPO       SUBTIPO       EXPEDIENTE FECHA DENOMINACION_TITULO
#>    <lgl> <chr>      <chr>              <int> <chr> <chr>              
#>  1 NA    Marca      ""                   103 1870… La Deliciosa       
#>  2 NA    Marca      "Marca de Fá…         54 1867… Fuente de los Cana…
#>  3 NA    Marca      "Marca de Fá…         50 1868… Campanadas para in…
#>  4 NA    Marca      "Marca de Fá…         66 1868… Compañía Española  
#>  5 NA    Marca      "Marca de Fá…         76 1869… Tinta Universal   
#> # ℹ 1 more variable: SOLICITANTE <chr>
      • Clicking the “+” in the first row of the table results in a pop-up table with more information (See first image)
      • Click + and monitor Network tab
        • The name the pops up is “ficha.php?id=1030&db=maruam”
      • Get the request URL
        • You can right-click that name >> Copy >> Copy URL or Left-click the name and the Request URL will be in the Headers tab
        • “http://historico.oepm.es/logica/ficha.php?id=1030&db=maruam”
        • Notice that this was a GET request method
      • Issue: The id and db parameter values cannot be obtained from the primary table nor can be guessed.
        • By inspecting the “+” button, you can see that the id and db values are captured in a HTML a-tag
      Obtain values POST response
      1list_attrs <- content(POST_response, "parsed") |>
  html_nodes("td > a") |> 
  html_attrs()

2info <- lapply(list_attrs, function(x) {
  out <- x[names(x) %in% c("data-id", "data-db")]
  if (length(out) == 0) return(NULL)
  data.frame(id = out[1], db = out[2])
})

3info <- Filter(Negate(is.null), info)

4out <- data.table::rbindlist(info)
head(out)
#>     id     db
#> 1:   6 maruam
#> 2: 130 maruam
#> 3: 461 maruam
#> 4: 523 maruam
#> 5: 560 maruam
#> 6: 581 maruam
      1
      Get all the attributes for all “+” buttons
      2
      For each “+” button, extract only the id and db attributes
      3
      Remove cases where there are no attributes
      4
      Transform the list into a clean dataframe
      Loop values through url string
      read_html(
  paste0("http://historico.oepm.es/logica/ficha.php?id=", 
         6, "&db=", "maruam")
) |> 
  html_table() |> 
  head(n = 5)
#> [[1]]
#>  # A tibble: 14 × 2
#>     X1                             X2                                   
#>     <chr>                          <chr>                                
#>   1 Número de Marca                "103"                                
#>   2 Denominación Breve             "La Deliciosa"                       
#>   3 Fecha Solicitud                "27-10-1870"                         
#>   4 Fecha Concesión                "24-03-1871"                         
#>   5 Fecha de publicación Concesión ""
      • Values can now be looped through the GET request url string
      • Data is in long format and needs cleaned. See repo for complete code.

GET

  • Example: Real Estate Addresses

    • The website, https://www.cimls.com/, is a free listing for commercial real estate

    • Get API Parameters From Network Tab

      • After navigating to the “For Sale” page from the navbar, you fill out an HTML form with whichever query parameters your interested in.
      • For this example, I chose city, state, search range (miles), and property type.

      • To find the GET (or POST) request, look for an object with Size in kb — which there are two here.
      • The ones I’ve seen also have .php extensions (popular API programming language).
      • Looking for xhr in the Type column can also be an indicator. If there’s a lot of stuff that gets loaded, there’s xhr filter button in the tool bar
        • XMLHttpRequest (xhr) is an API in the form of a JavaScript object whose methods transmit HTTP requests from a web browser to a web server

      • For each suspected object, click it. Then, click the Preview tab to see if that object is loading the data you want.

      • Copy the request url from the Headers tab and the query parameters that were used from the Payload tab

      fetch("https://www.cimls.com/external-data/datafiniti-api.php?city=Louisville&state=Kentucky&prop-type=Retail&range=25", {
  "headers": {
    "accept": "*/*",
    "accept-language": "en-US,en;q=0.9",
    "priority": "u=1, i",
    "sec-ch-ua": "\"Chromium\";v=\"134\", \"Not:A-Brand\";v=\"24\", \"Google Chrome\";v=\"134\"",
    "sec-ch-ua-mobile": "?0",
    "sec-ch-ua-platform": "\"Windows\"",
    "sec-fetch-dest": "empty",
    "sec-fetch-mode": "cors",
    "sec-fetch-site": "same-origin"
  },
  "referrer": "https://www.cimls.com/external-data/external-search.php?city=Louisville&state=Kentucky&prop-type=Retail&range=25",
  "referrerPolicy": "strict-origin-when-cross-origin",
  "body": null,
  "method": "GET",
  "mode": "cors",
  "credentials": "include"
});
      • Right-clicking datafiniti-api.php?city=Louisville&state=Kentucky&prop-type=Retail&range=25 gives us some options. Select “Copy as fetch.”
      • Under “headers”, we see the necessary header parameters that are required: “accept”, “accept-language”, “content-type”, and “x-requested-with”.
      • No idea what these mean or why these are the particular ones required
      • There is a list of other parameters that could probably be added. You can find them in the Headers tab underneath Request Headers or by selecting Copy Request Headers as seen in the image.
        • By using “Copy as fetch,” we only get the absolutely necessary ones (I think)

      • Unlike the POST example, you’ll also need the Cookie string and the Referer url in the Headers tab.
      • The cookie string could be valid months or a year. From the Cookies tab, check the dates for the various values in the Expires field. After which, you’ll have to come to the site and renew it.
        • There are some cookie functions in {httr2}, so maybe there’s a way to programmatically do this.
      Click for More Pages

      • Only 4 properties are listed. So, if we want more, we have to click the More Results button.
      • Monitoring the network activity shows that the request url has changed slightly.
      Get New Parameters

      • Viewing the Payload tab shows the new query parameters that we’ll need to get the data from the rest of the pages.

    • Call API and Get the Data

      pacman::p_load(
  dplyr,
  stringr,
  httr2,
  rvest
)

req_prop <- 
  request("https://www.cimls.com/external-data/datafiniti-api.php") |>
  req_throttle(capacity = 1, fill_time_s = 10) |>
  req_url_query(
    "city" = "Louisville",
    "state" = "Kentucky",
    "page" = "1",
    "cend" = "3",
    "prop-type" = "Retail",
    "range" = "25"
  ) |> 
  req_headers(
    "accept" = "*/*",
    "accept-language" = "en-US,en;q=0.9",
    "priority" = "u=1, i",
    "sec-ch-ua" = "\"Chromium\";v=\"134\", \"Not:A-Brand\";v=\"24\", \"Google Chrome\";v=\"134\"",
    "sec-ch-ua-mobile" = "?0",
    "sec-ch-ua-platform" = "\"Windows\"",
    "sec-fetch-dest" = "empty",
    "sec-fetch-mode" = "cors",
    "sec-fetch-site" = "same-origin",
    "referer" = "https://www.cimls.com/external-data/external-search.php?city=Louisville&state=Kentucky&prop-type=Retail&range=25",
    "cookie" = cookies
  )

resps_prop <- 
  req_perform_iterative(
    req = req_prop,
    next_req = iterate_with_offset(param_name = "page"),
    max_reqs = 4
  )

length(resps_prop)
#> [1] 4
      • Also see API >> {httr2} >> Paginated Request for another example
      • request takes the request url without the query parameters
      • req_throttle says only call the API once every 10 seconds
        • If capacity = 5, then at most five API calls can be made consecutively every 10 seconds
      • req_query_url fills out the request url with the parameters and values
        • Even though “page 1” didn’t include page and cend parameters, the query fortunately still works. This makes iterating calls to the API simpler
        • If the parameters couldn’t be included in the first iteration (page 1), I think {httr2} provides a function to alter the request url in the iteration loop.
      • req_headers takes the headers we got from the “copy as fetch” along with the cookie string (not shown) and the referer url.
      • req_perform_iterative performs the call loop.
        • param_name = “page” specifies which query parameter(s) to use in the iteration (e.g. page = “1”, page = “2”, page = “3”, etc.)
        • max_reqs = 4 is used to specify that only want 4 pages. Although if 1 call failed, I’d only get 3.
        • You can also a provide a user-defined function that returns a logical to determine when the iteration stops.
      • The combined response is a list where each element is the response from that iterations API call. (4 elements in this case)
      pull_addrs <- function(rsp) {
  addrs <- 
    rsp |> 
    resp_body_html() |> 
    html_elements(".feature-text") |> 
    html_text() |> 
    str_remove_all("\\$.+")

  tib_addr <- 
    tibble(addrs_full = addrs) |> 
    mutate(addrs_full = str_replace_all(addrs_full,
                                        pattern = "Vly",
                                        replacement = "Valley"))
}


tib_addrs <- 
  resps_data(resps = resps_prop, resp_data = pull_addrs) |> 
  distinct()
tib_addrs
#> # A tibble: 13 × 1
#>    addrs_full                           
#>    <chr>                                
#>  1 10405 Southpointe Blvd, Louisville KY
#>  2 3621 Fern Valley Rd, Louisville KY   
#>  3 3600 Bardstown Rd, Louisville KY     
#>  4 7724 Bardstown Rd, Louisville KY     
#>  5 11601 Plantside Dr, Louisville KY    
#>  6 4926 Cane Run Rd, Louisville KY      
#>  7 5000 Maple Spg Dr, Louisville KY     
#>  8 552 S 4th St, Louisville KY          
#>  9 10435 Southpointe Blvd, Louisville KY
#> 10 10415 Southpointe Blvd, Louisville KY
#> 11 7701 Preston Hwy, Louisville KY      
#> 12 1905 Bardstown Rd, Louisville KY     
#> 13 714 E 10th St, Jeffersonville IN

      • pull_addrs is wrangling function that takes a response, pulls address text from .feature-text class. Then it’s cleaned.

      • resps_data takes the response list and loops each element though pull_addrs

        • Acts like a {purrr:map} function

      • Note that there are only 13 addresses. That’s because there were only 13 unique properties fitting my criteria. Once I got the end of the available properties, it looped around to the beginning and gave me duplicate properties. (Hence, the distinct at the end.)

      • To grap the property characteristics, you can adjust the pull_addrs function

        prop_chars <-
  resp |>
  resp_body_html() |> 
  html_nodes("li") |>
  html_text()
        • Each characteristic is a html list element. It would take some additional wrangling to get it tidy. I didn’t look at this result too closely, but it shouldn’t be too much trouble.