Functions Overview"

In ralger: Easy Web Scraping

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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)

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)

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:

• 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.

Example

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)

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/")

Further, it's possible to filter the results using the contain argument:

titles_scrap(link = "https://www.nytimes.com/", contain = "TrUMp", case_sensitive = FALSE)

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/")

If needed, it's possible to collapse the paragraphs into one bag of words:

paragraphs_scrap(link = "https://ropensci.org/", collapse = TRUE)

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)

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/")

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").

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.

ralger documentation built on March 18, 2021, 1:06 a.m.