The goal of ralger is to facilitate web scraping in R. For a quick video tutorial, I gave a talk at useR2020, which you can find here
You can install the ralger package from CRAN with:
install.packages("ralger")or you can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("feddelegrand7/ralger")scrap()This is an example which shows how to extract top ranked universities’ names according to the ShanghaiRanking Consultancy:
library(ralger)
my_link <- "http://www.shanghairanking.com/ARWU2020.html"
my_node <- "#UniversityRanking a" # The class ID , we recommend SelectorGadget
best_uni <- scrap(link = my_link, node = my_node)
head(best_uni, 10)
#> [1] "Harvard University"
#> [2] "Stanford University"
#> [3] "University of Cambridge"
#> [4] "Massachusetts Institute of Technology (MIT)"
#> [5] "University of California, Berkeley"
#> [6] "Princeton University"
#> [7] "Columbia University"
#> [8] "California Institute of Technology"
#> [9] "University of Oxford"
#> [10] "University of Chicago"Thanks to the robotstxt, you can set askRobot = T to ask the robots.txt file if it’s permitted to scrape a specific web page.
If you want to scrap multiple list pages, just use scrap() in conjunction with paste0().
table_scrap()If you want to extract an HTML Table, you can use the table_scrap() function. Take a look at this webpage which lists the highest gross revenues in the cinema industry. You can extract the HTML table as follows:
data <- table_scrap(link ="https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW")
head(data)
#> Rank Title Lifetime Gross Year
#> 1 1 Avengers: Endgame $2,797,800,564 2019
#> 2 2 Avatar $2,790,439,092 2009
#> 3 3 Titanic $2,471,754,307 1997
#> 4 4 Star Wars: Episode VII - The Force Awakens $2,068,454,310 2015
#> 5 5 Avengers: Infinity War $2,048,359,754 2018
#> 6 6 Jurassic World $1,670,516,444 2015When you deal with a web page that contains many HTML table you can use the choose argument to target a specific table
tidy_scrap()Sometimes you’ll find some useful information on the internet that you want to extract in a tabular manner however these information are not provided in an HTML format. In this context, you can use the tidy_scrap() function which returns a tidy data frame according to the arguments that you introduce. The function takes four arguments:
We’ll work on the famous IMDb website. Let’s say we need a data frame composed of:
We will need to use the tidy_scrap() function as follows:
my_link <- "https://www.imdb.com/search/title/?groups=top_250&sort=user_rating"
my_nodes <- c(
".lister-item-header a", # The title
".text-muted.unbold", # The year of release
".ratings-imdb-rating strong" # The rating)
)
names <- c("title", "year", "rating") # respect the nodes order
tidy_scrap(link = my_link, nodes = my_nodes, colnames = names)
#> # A tibble: 50 x 3
#> title year rating
#> <chr> <chr> <chr>
#> 1 The Shawshank Redemption (1994) 9.3
#> 2 The Godfather (1972) 9.2
#> 3 The Dark Knight (2008) 9.0
#> 4 The Godfather: Part II (1974) 9.0
#> 5 12 Angry Men (1957) 9.0
#> 6 The Lord of the Rings: The Return of the King (2003) 8.9
#> 7 Pulp Fiction (1994) 8.9
#> 8 Schindler's List (1993) 8.9
#> 9 Inception (2010) 8.8
#> 10 Fight Club (1999) 8.8
#> # ... with 40 more rowsNote that all columns will be of character class. you’ll have to convert them according to your needs.
titles_scrap()Using titles_scrap(), one can efficiently scrape titles which correspond to the h1, h2 & h3 HTML tags.
If we go to the New York Times, we can easily extract the titles displayed within a specific web page :
titles_scrap(link = "https://www.nytimes.com/")
#> [1] "Listen to ‘The Daily’"
#> [2] "Listen to ‘Sway’"
#> [3] "DealBook D.C. Policy Project"
#> [4] "Live"
#> [5] "Coronavirus Updates"
#> [6] "Business and Economy Updates"
#> [7] "How Confusion and Inaction at the Capitol Delayed a Troop Deployment"
#> [8] "The Pandemic Is Receding in the Worst Hotspots. Will It Last?"
#> [9] "Janet Yellen on Rebooting the U.S. Economy"
#> [10] "Uprising Grows Over Cuomo’s Bullying and ‘Brutalist Political Theater’"
#> [11] "How Texas’ Drive for Energy Independence Set It Up for Disaster"
#> [12] "Texans Needed Food and Comfort After the Storm. They Found It at H-E-B."
#> [13] "She Beat Cancer at 10. Now She’s Set to Be the Youngest American in Space."
#> [14] "Is This the End of Tipping?"
#> [15] "Just When You Thought Politics Couldn’t Unravel Any Further"
#> [16] "What Happens When People Stop Going to the Doctor? We’re About to Find Out"
#> [17] "50 Million Americans Are Unpaid Caregivers. We Need Help."
#> [18] "Storm Victims Didn’t Bring It on Themselves"
#> [19] "Why Texas Republicans Fear the Green New Deal"
#> [20] "Can We Stop Fighting About Charter Schools?"
#> [21] "The 4 Great Migrations"
#> [22] "What Are Sperm Telling Us?"
#> [23] "The Tale of the Untamable Shrew"
#> [24] "Republican Party’s Future: Stay Loyal to Trump, or Disavow Him?"
#> [25] "What This Wave of Anti-Asian Violence Reveals About America"
#> [26] "On Horseback With Eagle Hunters and Herders of the Mongolian Altai"
#> [27] "Amy Poehler Is Into What Gen Z Is Selling"
#> [28] "Farewell, Serena Williams? Not So Fast"
#> [29] "Site Index"
#> [30] "Site Information Navigation"
#> [31] "Supreme Court Denies Trump’s Final Bid to Shield Financial Records"
#> [32] "Senators Question Attorney General Nominee Merrick Garland"
#> [33] "England will reopen schools in two weeks, but pubs and restaurants will stay shut for now, Johnson says."
#> [34] "The F.D.A. tells companies that vaccines adapted for new variants won’t need lengthy clinical trials."
#> [35] "To help tiny companies, the Biden administration adjusts the rules for pandemic loans."
#> [36] "Stocks drop as rising bond yields intensify the debate about inflation."
#> [37] "Here’s What’s Next in the Trump Taxes Investigation"
#> [38] "In 2020, The Times obtained decades’ worth of tax information for former President Trump. Here’s what we learned."
#> [39] "Opinion"
#> [40] "Editors’ Picks"
#> [41] "Advertisement"Further, it’s possible to filter the results using the contain argument:
titles_scrap(link = "https://www.nytimes.com/", contain = "TrUMp", case_sensitive = FALSE)
#> [1] "Republican Party’s Future: Stay Loyal to Trump, or Disavow Him?"
#> [2] "Supreme Court Denies Trump’s Final Bid to Shield Financial Records"
#> [3] "Here’s What’s Next in the Trump Taxes Investigation"
#> [4] "In 2020, The Times obtained decades’ worth of tax information for former President Trump. Here’s what we learned."paragraphs_scrap()In the same way, we can use the paragraphs_scrap() function to extract paragraphs. This function relies on the p HTML tag.
Let’s get some paragraphs from the lovely ropensci.org website:
paragraphs_scrap(link = "https://ropensci.org/")
#> [1] ""
#> [2] "We help develop R packages for the sciences via community driven learning, review and\nmaintenance of contributed software in the R ecosystem"
#> [3] "Use our carefully vetted, staff- and community-contributed R software tools that lower barriers to working with local and remote scientific data sources. Combine our tools with the rich ecosystem of R packages."
#> [4] "Workflow Tools for Your Code and Data"
#> [5] "Get Data from the Web"
#> [6] "Convert and Munge Data"
#> [7] "Document and Release Your Data"
#> [8] "Visualize Data"
#> [9] "Work with Databases From R"
#> [10] "Access, Manipulate, Convert Geospatial Data"
#> [11] "Interact with Web Resources"
#> [12] "Use Image & Audio Data"
#> [13] "Analyze Scientific Papers (and Text in General)"
#> [14] "Secure Your Data and Workflow"
#> [15] "Handle and Transform Taxonomic Information"
#> [16] "Get inspired by real examples of how our packages can be used."
#> [17] "Or browse scientific publications that cited our packages."
#> [18] "Our suite of packages is comprised of contributions from staff engineers and the wider R\ncommunity via a transparent, constructive and open review process utilising GitHub's open\nsource infrastructure."
#> [19] "We combine academic peer reviews with production software code reviews to create a\ntransparent, collaborative & more efficient review process\n "
#> [20] "Based on best practices of software development and standards of R, its\napplications and user base."
#> [21] "Our diverse community of academics, data scientists and developers provide a\nplatform for shared learning, collaboration and reproducible science"
#> [22] "We welcome you to join us and help improve tools and practices available to\nresearchers while receiving greater visibility to your contributions. You can\ncontribute with your packages, resources or post questions so our members will help\nyou along your process."
#> [23] "Discover, learn and get involved in helping to shape the future of Data Science"
#> [24] "Join in our quarterly Community Calls with fellow developers and scientists - open\nto all"
#> [25] "Upcoming events including meetings at which our team members are speaking."
#> [26] "The latest developments from rOpenSci and the wider R community"
#> [27] "Release notes, updates and package related developements"
#> [28] "A digest of R package and software review news, use cases, blog posts, and events, curated every two weeks. Subscribe to get it in your inbox, or check the archive."
#> [29] "Happy rOpenSci users can be found at"
#> [30] "Except where otherwise noted, content on this site is licensed under the CC-BY license •\nPrivacy Policy"If needed, it’s possible to collapse the paragraphs into one bag of words:
paragraphs_scrap(link = "https://ropensci.org/", collapse = TRUE)
#> [1] " We help develop R packages for the sciences via community driven learning, review and\nmaintenance of contributed software in the R ecosystem Use our carefully vetted, staff- and community-contributed R software tools that lower barriers to working with local and remote scientific data sources. Combine our tools with the rich ecosystem of R packages. Workflow Tools for Your Code and Data Get Data from the Web Convert and Munge Data Document and Release Your Data Visualize Data Work with Databases From R Access, Manipulate, Convert Geospatial Data Interact with Web Resources Use Image & Audio Data Analyze Scientific Papers (and Text in General) Secure Your Data and Workflow Handle and Transform Taxonomic Information Get inspired by real examples of how our packages can be used. Or browse scientific publications that cited our packages. Our suite of packages is comprised of contributions from staff engineers and the wider R\ncommunity via a transparent, constructive and open review process utilising GitHub's open\nsource infrastructure. We combine academic peer reviews with production software code reviews to create a\ntransparent, collaborative & more efficient review process\n Based on best practices of software development and standards of R, its\napplications and user base. Our diverse community of academics, data scientists and developers provide a\nplatform for shared learning, collaboration and reproducible science We welcome you to join us and help improve tools and practices available to\nresearchers while receiving greater visibility to your contributions. You can\ncontribute with your packages, resources or post questions so our members will help\nyou along your process. Discover, learn and get involved in helping to shape the future of Data Science Join in our quarterly Community Calls with fellow developers and scientists - open\nto all Upcoming events including meetings at which our team members are speaking. The latest developments from rOpenSci and the wider R community Release notes, updates and package related developements A digest of R package and software review news, use cases, blog posts, and events, curated every two weeks. Subscribe to get it in your inbox, or check the archive. Happy rOpenSci users can be found at Except where otherwise noted, content on this site is licensed under the CC-BY license •\nPrivacy Policy"weblink_scrap()weblink_scrap() is used to srape the web links available within a web page. Useful in some cases, for example, getting a list of the available PDFs:
weblink_scrap(link = "https://www.worldbank.org/en/access-to-information/reports/",
contain = "PDF",
case_sensitive = FALSE)
#> [1] "http://pubdocs.worldbank.org/en/304561593192266592/pdf/A2i-2019-annual-report-FINAL.pdf"
#> [2] "http://pubdocs.worldbank.org/en/539071573586305710/pdf/A2I-annual-report-2018-Final.pdf"
#> [3] "http://pubdocs.worldbank.org/en/742661529439484831/WBG-AI-2017-annual-report.pdf"
#> [4] "http://pubdocs.worldbank.org/en/814331507317964642/A2i-annualreport-2016.pdf"
#> [5] "http://pubdocs.worldbank.org/en/229551497905271134/Experience-18-month-report-Dec-2012.pdf"
#> [6] "http://pubdocs.worldbank.org/en/835741505831037845/pdf/2016-AI-Survey-Report-Final.pdf"
#> [7] "http://pubdocs.worldbank.org/en/698801505831644664/pdf/AI-Survey-written-comments-Final-2016.pdf"
#> [8] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2016/3/150501459179518612/Write-in-comments-in-2015-AI-Survey.pdf"
#> [9] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/766701433971800319/Written-comments-in-2014-AI-Survey.pdf"
#> [10] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/512551434127742109/2013-AI-Survey-Written-comments.pdf"
#> [11] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/5361434129036318/2012-AI-Survey-Written-comments.pdf"
#> [12] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/168151434129035939/2011-AI-Survey-Written-comments.pdf"
#> [13] "https://ppfdocuments.azureedge.net/e5c12f4e-7f50-44f7-a0d8-78614350f97cAnnex2.pdf"
#> [14] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2016/4/785921460482892684/PPF-Mapping-AI-Policy.pdf"
#> [15] "http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/453041434139030640/AI-Interpretations.pdf"
#> [16] "http://pubdocs.worldbank.org/en/157711583443319835/pdf/Access-to-Information-Policy-Spanish.pdf"
#> [17] "http://pubdocs.worldbank.org/en/270371588347691497/pdf/Access-to-Information-Policy-Arabic.pdf"
#> [18] "http://pubdocs.worldbank.org/en/939471588348288176/pdf/Access-to-Information-Directive-Procedure-Arabic.pdf"
#> [19] "http://pubdocs.worldbank.org/en/248301574182372360/World-Bank-consultations-guidelines.pdf"images_scrap() and images_preview()images_preview() allows you to scrape the URLs of the images available within a web page so that you can choose which images extension (see below) you want to focus on.
Let’s say we want to list all the images from the official RStudio website:
images_preview(link = "https://rstudio.com/")
#> [1] "https://dc.ads.linkedin.com/collect/?pid=218281&fmt=gif"
#> [2] "https://www.facebook.com/tr?id=151855192184380&ev=PageView&noscript=1"
#> [3] "https://d33wubrfki0l68.cloudfront.net/08b39bfcd76ebaf8360ed9135a50a2348fe2ed83/75738/assets/img/logo-white.svg"
#> [4] "https://d33wubrfki0l68.cloudfront.net/8bd479afc1037554e6218c41015a8e047b6af0f2/d1330/assets/img/libertymutual-logo-regular.png"
#> [5] "https://d33wubrfki0l68.cloudfront.net/089844d0e19d6176a5c8ddff682b3bf47dbcb3dc/9ba69/assets/img/walmart-logo.png"
#> [6] "https://d33wubrfki0l68.cloudfront.net/a4ebff239e3de426fbb43c2e34159979f9214ce2/fabff/assets/img/janssen-logo-2.png"
#> [7] "https://d33wubrfki0l68.cloudfront.net/6fc5a4a8c3fa96eaf7c2dc829416c31d5dbdb514/0a559/assets/img/accenture-logo.png"
#> [8] "https://d33wubrfki0l68.cloudfront.net/d66c3b004735d83f205bc8a1c08dc39cc1ca5590/2b90b/assets/img/nasa-logo.png"
#> [9] "https://d33wubrfki0l68.cloudfront.net/521a038ed009b97bf73eb0a653b1cb7e66645231/8e3fd/assets/img/rstudio-icon.png"
#> [10] "https://d33wubrfki0l68.cloudfront.net/19dbfe44f79ee3249392a5effaa64e424785369e/91a7c/assets/img/connect-icon.png"
#> [11] "https://d33wubrfki0l68.cloudfront.net/edf453f69b61f156d1d303c9ebe42ba8dc05e58a/213d1/assets/img/icon-rspm.png"
#> [12] "https://d33wubrfki0l68.cloudfront.net/62bcc8535a06077094ca3c29c383e37ad7334311/a263f/assets/img/logo.svg"
#> [13] "https://d33wubrfki0l68.cloudfront.net/9249ca7ba197318b488c0b295b94357694647802/6d33b/assets/img/logo-lockup.svg"
#> [14] "https://d33wubrfki0l68.cloudfront.net/30ef84abbbcfbd7b025671ae74131762844e90a1/3392d/assets/img/bcorps-logo.svg"images_scrap() on the other hand download the images. It takes the following arguments:
link: The URL of the web page;
imgpath: The destination folder of your images. It defaults to getwd()
extn: the extension of the image: jpg, png, jpeg … among others;
askRobot: ask the robots.txt file if it’s permitted to scrape the web page.
In the following example we extract all the png images from RStudio :
# Suppose we're in a project which has a folder called my_images:
images_scrap(link = "https://rstudio.com/",
imgpath = here::here("my_images"),
extn = "png") # without the .The images will be downloaded into the folder here::here("myimages").
Please note that the ralger project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.