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")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 = TRUE 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(). Suppose that you want to scrape all
RStudio::conf 2021 speakers:
base_link <- "https://global.rstudio.com/student/catalog/list?category_ids=1796-speakers&page="
links <- paste0(base_link, 1:3) # the speakers are listed from page 1 to 3
node <- ".mediablock__link"
head(scrap(links, node), 10) # printing the first 10 speakers
#> [1] "Aaron Jacobs" "Ahmadou Dicko"
#> [3] "Alan Feder" "Alex Cookson"
#> [5] "Allison Horst" "Andrew Ba Tran"
#> [7] "Athanasia M. Mowinckel" "Barret Schloerke"
#> [9] "Carson Sievert" "Chelsea Parlett-Pelleriti"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 $2,471,751,922 1997
#> 4 4 Star Wars: Episode VII - The Force Awakens $2,068,454,133 2015
#> 5 5 Avengers: Infinity War $2,048,359,754 2018
#> 6 6 Jurassic World $1,670,426,444 2015When you deal with a web page that contains many HTML table you can
use the choose argument to target a specific table
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:
- link : the link of the website you’re interested for;
- nodes: a vector of CSS elements that you want to extract. These elements will form the columns of your data frame;
- colnames: this argument represents the vector of names you want to assign to your columns. Note that you should respect the same order as within the nodes vector;
- clean: if true the function will clean the tibble’s columns;
- askRobot: ask the robots.txt file if it’s permitted to scrape the web page.
We’ll work on the famous IMDb website. Let’s say we need a data frame composed of:
- The title of the 50 best ranked movies of all time
- Their release year
- Their rating
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.
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] "The Book Review Podcast"
#> [3] "Listen to ‘Sway’ With Kara Swisher"
#> [4] "Trump Will Leave Office With Worst Approval Rating of His Presidency"
#> [5] "Inside Twitter’s Decision to Cut Off Trump"
#> [6] "A far-right activist known as “Baked Alaska” is among the latest Capitol rioters who have been arrested."
#> [7] "Virus Updates: India Starts Vaccinating Its 1.3 Billion People"
#> [8] "Children’s Screen Time Has Soared in the Pandemic, Alarming Parents"
#> [9] "How to (Literally) Drive the Coronavirus Away"
#> [10] "Analysis: The Deceptive Tactic Behind Trump’s ‘Law and Order’"
#> [11] "A First for an American President, and a First for Donald Trump"
#> [12] "The Pariah Post-Presidency"
#> [13] "Longtime Ugandan President Defeats Ex-Rapper in Disputed Election"
#> [14] "Merkel’s Party Chooses a New Leader"
#> [15] "The Weekender: True Stories of Hooking Up During Covid-19"
#> [16] "Did you follow the news this week? Take our quiz."
#> [17] "Why Are There So Few Courageous Senators?"
#> [18] "Images From the Capitol Under Siege"
#> [19] "Trump Ignites a War Within the Church"
#> [20] "We Need to Vaccinate Faster. Here’s How."
#> [21] "A Ray of Hope in a Slimy Southern Cave"
#> [22] "Trump’s Inevitable End"
#> [23] "‘Stop the Steal’ Didn’t Start With Trump"
#> [24] "Secretary of State Pompeo Leaves No Bridges Unburned"
#> [25] "Listen to ‘The Argument’: Will impeachment change Republican minds?"
#> [26] "Big Tech Has Helped Trash America"
#> [27] "No, the Chaos in America Is Not a Gift to China and Russia"
#> [28] "Go Ahead. Fantasize."
#> [29] "A Farewell to the Trump Aesthetic"
#> [30] "Can’t Measure Heart? N.F.L. Teams Are Trying"
#> [31] "Site Index"
#> [32] "Site Information Navigation"
#> [33] "Republicans Are Headed for a Bitter Internal Showdown"
#> [34] "Before Capitol Riot, Calls for Cash and Talk of Revolution"
#> [35] "Capitol Attack Could Fuel Extremist Recruitment For Years, Experts Warn"
#> [36] "Opinion"
#> [37] "Editors’ Picks"
#> [38] "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] "Trump Will Leave Office With Worst Approval Rating of His Presidency"
#> [2] "Inside Twitter’s Decision to Cut Off Trump"
#> [3] "Analysis: The Deceptive Tactic Behind Trump’s ‘Law and Order’"
#> [4] "A First for an American President, and a First for Donald Trump"
#> [5] "Trump Ignites a War Within the Church"
#> [6] "Trump’s Inevitable End"
#> [7] "‘Stop the Steal’ Didn’t Start With Trump"
#> [8] "A Farewell to the Trump Aesthetic"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() 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_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/f255381cf5fd8f44b899f01761a82ad1f149382d/ade3a/assets/img/2021-logo.png"
#> [5] "https://d33wubrfki0l68.cloudfront.net/8bd479afc1037554e6218c41015a8e047b6af0f2/d1330/assets/img/libertymutual-logo-regular.png"
#> [6] "https://d33wubrfki0l68.cloudfront.net/089844d0e19d6176a5c8ddff682b3bf47dbcb3dc/9ba69/assets/img/walmart-logo.png"
#> [7] "https://d33wubrfki0l68.cloudfront.net/a4ebff239e3de426fbb43c2e34159979f9214ce2/fabff/assets/img/janssen-logo-2.png"
#> [8] "https://d33wubrfki0l68.cloudfront.net/6fc5a4a8c3fa96eaf7c2dc829416c31d5dbdb514/0a559/assets/img/accenture-logo.png"
#> [9] "https://d33wubrfki0l68.cloudfront.net/d66c3b004735d83f205bc8a1c08dc39cc1ca5590/2b90b/assets/img/nasa-logo.png"
#> [10] "https://d33wubrfki0l68.cloudfront.net/521a038ed009b97bf73eb0a653b1cb7e66645231/8e3fd/assets/img/rstudio-icon.png"
#> [11] "https://d33wubrfki0l68.cloudfront.net/19dbfe44f79ee3249392a5effaa64e424785369e/91a7c/assets/img/connect-icon.png"
#> [12] "https://d33wubrfki0l68.cloudfront.net/edf453f69b61f156d1d303c9ebe42ba8dc05e58a/213d1/assets/img/icon-rspm.png"
#> [13] "https://d33wubrfki0l68.cloudfront.net/62bcc8535a06077094ca3c29c383e37ad7334311/a263f/assets/img/logo.svg"
#> [14] "https://d33wubrfki0l68.cloudfront.net/9249ca7ba197318b488c0b295b94357694647802/6d33b/assets/img/logo-lockup.svg"
#> [15] "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 .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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent