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https://github.com/feddelegrand7/ralger

ralger makes it easy to scrape a website. Built on the shoulders of titans: rvest, xml2.
https://github.com/feddelegrand7/ralger

dataextraction r rstats webcrawling webscraper-website webscraping

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ralger makes it easy to scrape a website. Built on the shoulders of titans: rvest, xml2.

Awesome Lists containing this project

README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# ralger 



[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/ralger)](https://cran.r-project.org/package=ralger)

[![CRAN_time_from_release](https://www.r-pkg.org/badges/ago/ralger)](https://cran.r-project.org/package=ralger)

[![CRAN_latest_release_date](https://www.r-pkg.org/badges/last-release/ralger)](https://cran.r-project.org/package=ralger)

[![metacran downloads](https://cranlogs.r-pkg.org/badges/ralger)](https://cran.r-project.org/package=ralger)

[![metacran downloads](https://cranlogs.r-pkg.org/badges/grand-total/ralger)](https://cran.r-project.org/package=ralger)



[![R badge](https://img.shields.io/badge/Build%20with-♥%20and%20R-blue)](https://github.com/feddelegrand7/ralger)

[![R badge](https://img.shields.io/badge/-Sponsor-brightgreen)](https://www.buymeacoffee.com/Fodil)

[![R build status](https://github.com/feddelegrand7/ralger/workflows/R-CMD-check/badge.svg)](https://github.com/feddelegrand7/ralger/actions)

[![Codecov test coverage](https://codecov.io/gh/feddelegrand7/ralger/branch/master/graph/badge.svg)](https://codecov.io/gh/feddelegrand7/ralger?branch=master)



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](https://www.youtube.com/watch?v=OHi6E8jegQg)



## Installation



You can install the `ralger` package from [CRAN](https://cran.r-project.org/) with:



```{r eval=FALSE}

install.packages("ralger")



```



or you can install the development version from [GitHub](https://github.com/) with:



``` r

# install.packages("devtools")

devtools::install_github("feddelegrand7/ralger")

```

## `scrap()`



This is an example which shows how to extract [top ranked universities' names](http://www.shanghairanking.com/rankings/arwu/2021) according to the ShanghaiRanking Consultancy:



```{r example}

library(ralger)



my_link <- "http://www.shanghairanking.com/rankings/arwu/2021"



my_node <- "a span" # The element ID , I recommend SelectorGadget if you're not familiar with CSS selectors



clean <- TRUE # Should the function clean the extracted vector or not ? Default is FALSE



best_uni <- scrap(link = my_link, node = my_node, clean = clean)



head(best_uni, 10)



```



Thanks to the [robotstxt](https://github.com/ropensci/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:



```{r}



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 <- ".pr-1"



head(scrap(links, node), 10) # printing the first 10 speakers



```



## `attribute_scrap()`



If you need to scrape some elements' attributes, you can use the `attribute_scrap()` function as in the following example:



```{r}

# Getting all classes' names from the anchor elements

# from the ropensci website



attributes <- attribute_scrap(link = "https://ropensci.org/",

                node = "a", # the a tag

                attr = "class" # getting the class attribute

                )



head(attributes, 10) # NA values are a tags without a class attribute

```



Another example, let's we want to get all javascript dependencies within the same web page:



```{r}



js_depend <- attribute_scrap(link = "https://ropensci.org/",

                             node = "script",

                             attr = "src")



js_depend



```



## `table_scrap()`



If you want to extract an __HTML Table__, you can use the `table_scrap()` function. Take a look at this [webpage](https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW) which lists the highest gross revenues in the cinema industry. You can extract the HTML table as follows:



```{r}



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](https://www.imdb.com/). 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:



```{r example3, message=FALSE, warning=FALSE}



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](https://www.nytimes.com/), we can easily extract the titles displayed within a specific web page :



```{r example4}



titles_scrap(link = "https://www.nytimes.com/")



```



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



```{r}



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](https://ropensci.org/) website:



```{r}



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



```



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



```{r}



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:



```{r}



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](https://rstudio.com/) website:



```{r}



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](https://rstudio.com/)  :



```{r, eval=FALSE}



# 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 .



```



# Accessibility related functions



## `images_noalt_scrap()`



`images_noalt_scrap()` can be used to get the images within a specific web page that don't have an `alt` attribute which can be annoying for people using a screen reader:



```{r}



images_noalt_scrap(link = "https://www.r-consortium.org/")



```

If no images without `alt` attributes are found, the function returns `NULL` and displays an indication message:



```{r}

# WebAim is the reference website for web accessibility



images_noalt_scrap(link = "https://webaim.org/techniques/forms/controls")

```



## Code of Conduct



Please note that the ralger project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.