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htmldf

codecov CRAN status cran checks

Overview

The package htmldf contains a single function html_df() which accepts a vector of urls as an input and from each will attempt to download each page, extract and parse the html. The result is returned as a tibble where each row corresponds to a document, and the columns contain page attributes and metadata extracted from the html, including:

Installation

To install the CRAN version of the package:

install.packages('htmldf')

To install the development version of the package:

remotes::install_github('alastairrushworth/htmldf')

Usage

First define a vector of URLs you want to gather information from. The function html_df() returns a tibble where each row corresponds to a webpage, and each column corresponds to an attribute of that webpage:

library(htmldf)
library(dplyr)

# An example vector of URLs to fetch data for
urlx <- c("https://alastairrushworth.github.io/Visualising-Tour-de-France-data-in-R/",
          "https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c",
          "https://www.tensorflow.org/tutorials/images/cnn", 
          "https://www.analyticsvidhya.com/blog/2019/09/introduction-to-pytorch-from-scratch/")

# use html_df() to gather data
z <- html_df(urlx, show_progress = FALSE)

# have a quick look at the first page
glimpse(z[1, ])
## Rows: 1
## Columns: 17
## $ url       <chr> "https://alastairrushworth.github.io/Visualising-Tour-de-Fra…
## $ title     <chr> "Visualising Tour De France Data In R -"
## $ lang      <chr> "en"
## $ url2      <chr> "https://alastairrushworth.github.io/Visualising-Tour-de-Fra…
## $ links     <list> [<tbl_df[27 x 2]>]
## $ rss       <chr> "https://alastairrushworth.github.io/feed.xml"
## $ tables    <list> NA
## $ images    <list> [<tbl_df[8 x 3]>]
## $ social    <list> [<tbl_df[3 x 3]>]
## $ code_lang <dbl> 1
## $ size      <int> 38445
## $ server    <chr> "GitHub.com"
## $ accessed  <dttm> 2022-07-09 16:01:32
## $ published <dttm> 2019-11-24
## $ generator <chr> NA
## $ status    <int> 200
## $ source    <chr> "<!DOCTYPE html>\n<!--\n  Minimal Mistakes Jekyll Theme 4.4.…

To see the page titles, look at the titles column.

z %>% select(title, url2)
## # A tibble: 4 × 2
##   title                                                              url2       
##   <chr>                                                              <chr>      
## 1 Visualising Tour De France Data In R -                             https://al…
## 2 A Gentle Introduction to PyTorch 1.2 | by elvis | DAIR.AI | Medium https://me…
## 3 Convolutional Neural Network (CNN)  |  TensorFlow Core             https://ww…
## 4 Pytorch | Getting Started With Pytorch                             https://ww…

Where there are tables embedded on a page in the <table> tag, these will be gathered into the list column tables. html_df will attempt to coerce each table to tibble - where that isn’t possible, the raw html is returned instead.

z$tables
## [[1]]
## [1] NA
## 
## [[2]]
## [1] NA
## 
## [[3]]
## [[3]]$`no-caption`
## # A tibble: 0 × 0
## 
## 
## [[4]]
## [[4]]$`no-caption`
## # A tibble: 11 × 2
##    X1    X2         
##    <chr> <chr>      
##  1 Label Description
##  2 0     T-shirt/top
##  3 1     Trouser    
##  4 2     Pullover   
##  5 3     Dress      
##  6 4     Coat       
##  7 5     Sandal     
##  8 6     Shirt      
##  9 7     Sneaker    
## 10 8     Bag        
## 11 9     Ankle boot

html_df() does its best to find RSS feeds embedded in the page:

z$rss
## [1] "https://alastairrushworth.github.io/feed.xml"
## [2] NA                                            
## [3] NA                                            
## [4] NA

html_df() will try to parse out any social profiles embedded or mentioned on the page. Currently, this includes profiles for the sites

bitbucket, dev.to, discord, facebook, github, gitlab, instagram, kakao, keybase, linkedin, mastodon, medium, orcid, patreon, researchgate, stackoverflow, reddit, telegram, twitter, youtube

z$social
## [[1]]
## # A tibble: 3 × 3
##   site     handle                           profile                             
##   <chr>    <chr>                            <chr>                               
## 1 twitter  @rushworth_a                     https://twitter.com/rushworth_a     
## 2 github   @alastairrushworth               https://github.com/alastairrushworth
## 3 linkedin @in/alastair-rushworth-253137143 https://linkedin.com/in/alastair-ru…
## 
## [[2]]
## # A tibble: 3 × 3
##   site    handle    profile                     
##   <chr>   <chr>     <chr>                       
## 1 twitter @dair_ai  https://twitter.com/dair_ai 
## 2 twitter @omarsar0 https://twitter.com/omarsar0
## 3 github  @omarsar  https://github.com/omarsar  
## 
## [[3]]
## # A tibble: 2 × 3
##   site    handle      profile                       
##   <chr>   <chr>       <chr>                         
## 1 twitter @tensorflow https://twitter.com/tensorflow
## 2 github  @tensorflow https://github.com/tensorflow 
## 
## [[4]]
## # A tibble: 4 × 3
##   site     handle                    profile                                    
##   <chr>    <chr>                     <chr>                                      
## 1 twitter  @analyticsvidhya          https://twitter.com/analyticsvidhya        
## 2 facebook @analyticsvidhya          https://facebook.com/analyticsvidhya       
## 3 linkedin @company/analytics-vidhya https://linkedin.com/company/analytics-vid…
## 4 youtube  UCH6gDteHtH4hg3o2343iObA  https://youtube.com/channel/UCH6gDteHtH4hg…

Code language is inferred from <code> chunks using a preditive model. The code_lang column contains a numeric score where values near 1 indicate mostly R code, values near -1 indicate mostly Python code:

z %>% select(code_lang, url2)
## # A tibble: 4 × 2
##   code_lang url2                                                                
##       <dbl> <chr>                                                               
## 1     1     https://alastairrushworth.github.io/Visualising-Tour-de-France-data…
## 2    -0.860 https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb75…
## 3    -0.983 https://www.tensorflow.org/tutorials/images/cnn                     
## 4    -1     https://www.analyticsvidhya.com/blog/2019/09/introduction-to-pytorc…

Publication dates

z %>% select(published, url2)
## # A tibble: 4 × 2
##   published           url2                                                      
##   <dttm>              <chr>                                                     
## 1 2019-11-24 00:00:00 https://alastairrushworth.github.io/Visualising-Tour-de-F…
## 2 2019-09-01 18:03:22 https://medium.com/dair-ai/pytorch-1-2-introduction-guide…
## 3 2022-01-26 00:00:00 https://www.tensorflow.org/tutorials/images/cnn           
## 4 2019-09-17 03:09:28 https://www.analyticsvidhya.com/blog/2019/09/introduction…

Comments? Suggestions? Issues?

Any feedback is welcome! Feel free to write a github issue or send me a message on twitter.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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