Crawler un site web et extraire des données avec R

Possible de crawler sans budget ? Oui avec RCrawler !

Crawler et scraper des données est devenu une pratique incontournable pour les SEO depuis plusieurs années. Des solutions payantes existent comme par exemple Screaming Frog, Oncrawl, Botify ou encore Seolyzer. Pour ceux et celles qui n’ont les budgets pour passer sur de telles plateformes, il existe des solutions qui s’appuient sur des languages de programmation comme python ou encore R.

Dans cet article, je vais vous expliquer comment crawler gratuitement en exploiter le package RCrawler. Nous verrons comment configurer les informations à scraper et comment organiser la donnée de sorte qu’elle soit exploitable par la suite. RCrawler est un package très intéressant car nativement il embarque de nombreuses fonctionnalités comme le stockage des fichiers HTML (vous n’aurez pas à re-crawler si vous avez oublié de récupérer des informations) ou encore le crawl en mode headless browser, particulièrement apprécié pour des sites conçus sur des framework Angular ou React.

RCrawler nous y voilà !

# define you setwd()
setwd("/path/")

# install to be run once
install.packages("Rcrawler")

# and loading
library(Rcrawler)


# what we want to extract
CustomLabels <- c("title",
                  "Meta_description",
                  "h1",
                  "h2",
                  "h3",
                  "Hreflang",
                  "canonical_tag",
                  "meta_robots"
                  )

# How to grab it, do not hesitate to add other stuff you want to grab by adding xpath
CustomXPaths <- c("///title",
                  "//meta[@name='description']/@content",
                  "///h1",
                  "///h2",
                  "///h3",
                  "//link[@rel='alternate']/@hreflang",
                  "//link[@rel='canonical']/@href",
                  "//meta[@rel='robots']/@content")

# create proxy configuration if you need it. In this exemple we do not need it
# proxy <- httr::use_proxy("190.90.100.205",41000)
# use proxy configuration

# Crawler settings : I add many options but there are not all compulsory
Rcrawler(Website = "https://www.v6protect.fr", 
         #Obeyrobots=TRUE, 
         #RequestsDelay = 10, 
         #dataUrlfilter ="/path", 
         #crawlUrlfilter="/path/",
         #MaxDepth = 1, 
         ExtractXpathPat = CustomXPaths, 
         PatternsNames = CustomLabels,
         #Useragent="Mozilla 3.11",
         NetworkData = TRUE, #inlinks
         NetwExtLinks =TRUE, #outlinks
         statslinks = TRUE,
         #use_proxy = proxy,
         #ignoreAllUrlParams = TRUE
         )

# I combine data
crawl <-data.frame(do.call("rbind", DATA))
crawl_complete <- cbind(INDEX,crawl)
Idurl = as.numeric(crawl_complete$Id)
crawl_complete = cbind(Idurl,crawl_complete)


# I count inlinks
count_to = NetwEdges[,1:2] %>%
  distinct() %>%
  group_by(To) %>%
  summarise(n = n())

# I rename columns
count_to = count_to %>% 
rename(Idurl = To, Inlinks = n)

# I join inlinks data with my crawl data
df_final = left_join(count_to, crawl_complete,by="Idurl")

# I remove columns that I do not need
df_final = select(df_final, -Idurl, -Id, -IN)

# I rename columns
df_final = df_final %>% 
rename(Outlinks = OUT, Depth = Level)


## PAGERANK calculation
links <- NetwEdges[,1:2] %>%
  #grabing the first two columns
  distinct() 
# loading igraph package
library(igraph)
# Loading website internal links inside a graph object
g <- graph.data.frame(links)
# this is the main function, don't ask how it works
pr <- page.rank(g, algo = "prpack", vids = V(g), directed = TRUE, damping = 0.85)

# I grab results inside a dedicated data frame
values <- data.frame(pr$vector)
values$names <- rownames(values)

# delating row names
row.names(values) <- NULL

# reordering column
values <- values[c(2,1)]

# renaming columns
names(values)[1] <- "PageID"
names(values)[2] <- "pagerank"

#replacing id with url
values$url <- NetwIndex
names(values)[3] <- "Url"

# out of 10
values$Pagerank<- round(values$pagerank / max(values$pagerank) * 10)

# I join my crawl with Pagerank information
crawl = left_join(values,df_final,by="Url")

# I clean my dataframe by removing columns
crawl = select(crawl, -PageID.x,pagerank,-PageID.y)

#HERE WE ARE ! You can export you crawl data in an csv file
write.csv(crawl, "my_crawl_data.csv", sep=";")


###  BONUS ###

# FIND MY LAST CRAWL : HTML
ListProjects()
LastHTMLDATA <- LoadHTMLFiles("xxxxxxx", type = "vector")
# or to simply grab the last one:
LastHTMLDATA <- LoadHTMLFiles(ListProjects()[1], type = "vector")

for(i in 1:nrow(LastHTMLDATA)) {
  LastHTMLDATA$title[i] <- ContentScraper(HTmlText = LastHTMLDATA$html[i] ,XpathPatterns = "//title")
  LastHTMLDATA$h1[i] <- ContentScraper(HTmlText = LastHTMLDATA$html[i] ,XpathPatterns = "//h1")
  LastHTMLDATA$h2[i] <- ContentScraper(HTmlText = LastHTMLDATA$html[i] ,XpathPatterns = "//h2")
  LastHTMLDATA$h3[i] <- ContentScraper(HTmlText = LastHTMLDATA$html[i] ,XpathPatterns = "//h3")
}

## REACT OR ANGULAR CRAWLING SETTINGS ##
# RCrawler handly includes Phantom JS, the classic headless browser.
# Download and install phantomjs headless browser
install_browser()
 
# start browser process 
br <-run_browser()
Rcrawler(Website = "https://www.example.com/", Browser = br)
 
# don't forget to stop browser afterwards
stop_browser(br)