Functions Overview

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

Installation

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 $2,471,751,922 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 $1,670,401,444 2015

When 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:

Example

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 rows

Note 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.

Example

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 ‘The Argument’"                                                                                                
#>  [3] "In the ‘At Home’ Newsletter"                                                                                             
#>  [4] "Indonesian Jetliner Crashes Into the Sea After Takeoff"                                                                  
#>  [5] "He Dreamed of Being a Police Officer, Then Was Killed by a Pro-Trump Mob"                                                
#>  [6] "11 Journalists on Covering the Capitol Siege: ‘This Could Get Ugly’"                                                     
#>  [7] "Trump’s Legacy: Voters Who Reject Democracy and Any Politics but Their Own"                                              
#>  [8] "Bravery or reputation management? The resignations of some Trump officials are drawing skepticism."                      
#>  [9] "Here are the Trump aides who plan to stay to the end."                                                                   
#> [10] "As Coronavirus Mutates, the World Stumbles Again to Respond"                                                             
#> [11] "More Than 150,000 Are Fully Vaccinated in the U.S."                                                                      
#> [12] "False Reports of a New ‘U.S. Variant’ Came from White House Task Force"                                                  
#> [13] "Four Reasons the N.F.L. Shattered Its Scoring Record in 2020"                                                            
#> [14] "Covid-19 is forcing N.F.L. players and other pro athletes to make unusually hard decisions about work-life balance."     
#> [15] "The Weekender: The Last Two Northern White Rhinos"                                                                       
#> [16] "Did you follow the headlines this week? Take our quiz to find out."                                                      
#> [17] "Awe and Shock"                                                                                                           
#> [18] "Can Donald Trump Survive Without Twitter?"                                                                               
#> [19] "Far-Right Protesters Stormed Germany’s Parliament. What Can America Learn?"                                              
#> [20] "Listen to ‘Sway’: If You Were on Parler, You Saw the Mob Coming"                                                         
#> [21] "Impeach Now. Running Out the Clock on Trump Is Cowardly and Dangerous."                                                  
#> [22] "Stop Pretending ‘This Is Not Who We Are’"                                                                                
#> [23] "Neil Sheehan Forced an American Reckoning"                                                                               
#> [24] "Appeasement Got Us Where We Are"                                                                                         
#> [25] "This Is When the Fever Breaks"                                                                                           
#> [26] "How to Ensure This Never Happens Again"                                                                                  
#> [27] "More Immigrants Will Come to the U.S. Under President Biden. That’s a Good Thing."                                       
#> [28] "He Was Going to Close the Family Diner. Then He Got a Sign."                                                             
#> [29] "Louise Linton Has Made a Movie"                                                                                          
#> [30] "The Man Who Turned Credit-Card Points Into an Empire"                                                                    
#> [31] "Site Index"                                                                                                              
#> [32] "Site Information Navigation"                                                                                             
#> [33] "Democrats Lay Groundwork for Impeaching Trump Again"                                                                     
#> [34] "‘I Want Him Out’: Murkowski Is First G.O.P. Senator to Call for Removal"                                                 
#> [35] "Twitter Permanently Bans Trump, Capping Online Revolt"                                                                   
#> [36] "Google and Apple told Parler, a popular platform for conservatives, to step up its policing to stay in their app stores."
#> [37] "Can a president be impeached in 12 days? Here’s how the process might work."                                             
#> [38] "In Capital, a G.O.P. Crisis. At the R.N.C. Meeting, a Trump Celebration."                                                
#> [39] "Senator Josh Hawley, who drew condemnation for challenging the election results, defended his decision."                 
#> [40] "Seeing the Confederate flag in the Capitol was a jarring first in U.S. history. Historians weighed in on the moment."    
#> [41] "For those who survived the Nazi death camp, pictures of a man in a “Camp Auschwitz” sweatshirt were painful."            
#> [42] "Opinion"                                                                                                                 
#> [43] "Editors’ Picks"                                                                                                          
#> [44] "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] "He Dreamed of Being a Police Officer, Then Was Killed by a Pro-Trump Mob"                          
#>  [2] "Trump’s Legacy: Voters Who Reject Democracy and Any Politics but Their Own"                        
#>  [3] "Bravery or reputation management? The resignations of some Trump officials are drawing skepticism."
#>  [4] "Here are the Trump aides who plan to stay to the end."                                             
#>  [5] "Trump’s Capitol Offense"                                                                           
#>  [6] "Can Donald Trump Survive Without Twitter?"                                                         
#>  [7] "Impeach Now. Running Out the Clock on Trump Is Cowardly and Dangerous."                            
#>  [8] "Democrats Lay Groundwork for Impeaching Trump Again"                                               
#>  [9] "Twitter Permanently Bans Trump, Capping Online Revolt"                                             
#> [10] "In Capital, a G.O.P. Crisis. At the R.N.C. Meeting, a Trump Celebration."

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"

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/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:

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").

Code of Conduct

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.