The goal of ralger is to facilitate web scraping in R.
You can install the ralger package from CRAN with:
or you can install the development version from GitHub with:
This is an example which shows how to extract firms denomination from the website of the Algerian Chamber of Commerce and Industry (CACI). For simplicity, we’ll focus on firms operating within the capital (Algiers).
library(ralger)
my_link <- "http://elmouchir.caci.dz/search_results.php?keyword=&category=&location=Alger&submit=Trouver"
my_node <- ".listing_default" # The CSS element, we recommend SelectorGadget
scrap(my_link, my_node)
#> [1] "Adjerid Hanifa"
#> [2] "Dar Chamila"
#> [3] "BNP Paribas / Agences Alger Bordj El Kiffan"
#> [4] "TVA / Touring Voyages Algérie Centre / Zighoud Youcef"
#> [5] "SAMRIA AUTO / Salon Algerian du Materiel Roulant et de L'industrie Automobile"
#> [6] "YOUKAIS"
#> [7] "EDIMETAL"
#> [8] "SYRIAN AIR LINES"
#> [9] "Turkish Airlines / Direction Générale"
#> [10] "Aigle Azur / Agence Didouche Mourad"
#> [11] "British Airways"
#> [12] "DELTA"
#> [13] "Cabinet Ammiche Amer"
#> [14] "VERITEX"
#> [15] "Kermiche Partener"
#> [16] "Marine Soft"
#> [17] "PROGOS"
#> [18] "Ambassade du Royaume d'Arabie Saoudite"
#> [19] "Ambassade de la République d'Argentine"
#> [20] "Ambassade du Burkina Faso"If you want to scrap multiple list pages, just use scrap() in conjunction with paste(). Suppose, we want to extract the above information from the first 3 pages (starts from 0):
my_link <- "http://elmouchir.caci.dz/search_results.php?keyword=&category=&location=Alger&submit=Trouver&page="
my_node <- ".listing_default"
scrap(paste(my_link, 0:2), my_node)
#> [1] "EGT Zeralda / Entreprise de Gestion Touristique de Zeralda"
#> [2] "SPE / Sociéte Algérienne de Production d’Electricité"
#> [3] "GRTG / Société Algérienne de Gestion du Réseau de Transport de Gaz"
#> [4] "GRTE / Société Algérienne de Gestion du Réseau de Transport de Electricité"
#> [5] "Clef du Sud / Miftah El Djanoub"
#> [6] "MPV"
#> [7] "SCAL / La Société des Ciments de l'Algérois"
#> [8] "EVSM / Entreprise de Viabilisation de Sidi Moussa"
#> [9] "Chambre d'Agriculture de la Wilaya d'Alger / CNA"
#> [10] "VERITAL/ Direction Générale"
#> [11] "Wilaya d'Alger"
#> [12] "Officine Abeille"
#> [13] "Twingle"
#> [14] "UGTA / Union Générale des Travailleurs Algériens"
#> [15] "ENEFEP / Etablissement National Des Equipements Techniques Et Pédagogiques de la Formation et de L’enseignement Professionnels"
#> [16] "CCIM / Chambre de Commerce et d'Industrie de Mezghenna"
#> [17] "Conseil Constitutionnel"
#> [18] "Ambassade de la République de Serbie"
#> [19] "Conseil de la Nation"
#> [20] "Radio El Bahdja"
#> [21] "EGT Zeralda / Entreprise de Gestion Touristique de Zeralda"
#> [22] "SPE / Sociéte Algérienne de Production d’Electricité"
#> [23] "GRTG / Société Algérienne de Gestion du Réseau de Transport de Gaz"
#> [24] "GRTE / Société Algérienne de Gestion du Réseau de Transport de Electricité"
#> [25] "Clef du Sud / Miftah El Djanoub"
#> [26] "MPV"
#> [27] "SCAL / La Société des Ciments de l'Algérois"
#> [28] "EVSM / Entreprise de Viabilisation de Sidi Moussa"
#> [29] "Chambre d'Agriculture de la Wilaya d'Alger / CNA"
#> [30] "VERITAL/ Direction Générale"
#> [31] "Wilaya d'Alger"
#> [32] "Officine Abeille"
#> [33] "Twingle"
#> [34] "UGTA / Union Générale des Travailleurs Algériens"
#> [35] "ENEFEP / Etablissement National Des Equipements Techniques Et Pédagogiques de la Formation et de L’enseignement Professionnels"
#> [36] "CCIM / Chambre de Commerce et d'Industrie de Mezghenna"
#> [37] "Conseil Constitutionnel"
#> [38] "Ambassade de la République de Serbie"
#> [39] "Conseil de la Nation"
#> [40] "Radio El Bahdja"
#> [41] "EGT Zeralda / Entreprise de Gestion Touristique de Zeralda"
#> [42] "SPE / Sociéte Algérienne de Production d’Electricité"
#> [43] "GRTG / Société Algérienne de Gestion du Réseau de Transport de Gaz"
#> [44] "GRTE / Société Algérienne de Gestion du Réseau de Transport de Electricité"
#> [45] "Clef du Sud / Miftah El Djanoub"
#> [46] "MPV"
#> [47] "SCAL / La Société des Ciments de l'Algérois"
#> [48] "EVSM / Entreprise de Viabilisation de Sidi Moussa"
#> [49] "Chambre d'Agriculture de la Wilaya d'Alger / CNA"
#> [50] "VERITAL/ Direction Générale"
#> [51] "Wilaya d'Alger"
#> [52] "Officine Abeille"
#> [53] "Twingle"
#> [54] "UGTA / Union Générale des Travailleurs Algériens"
#> [55] "ENEFEP / Etablissement National Des Equipements Techniques Et Pédagogiques de la Formation et de L’enseignement Professionnels"
#> [56] "CCIM / Chambre de Commerce et d'Industrie de Mezghenna"
#> [57] "Conseil Constitutionnel"
#> [58] "Ambassade de la République de Serbie"
#> [59] "Conseil de la Nation"
#> [60] "Radio El Bahdja"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 $2,790,439,000 2009
#> 3 3 Titanic $2,196,094,287 1997
#> 4 4 Star Wars: Episode VII - The Force Awakens $2,068,223,624 2015
#> 5 5 Avengers: Infinity War $2,048,359,754 2018
#> 6 6 Jurassic World $1,670,400,637 2015Sometimes 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:
We’ll work on the famous IMDb website. Let’s say we need a dataframe 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(my_link, 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 The Lord of the Rings: The Return of the King (2003) 8.9
#> 6 Pulp Fiction (1994) 8.9
#> 7 Schindler's List (1993) 8.9
#> 8 12 Angry Men (1957) 8.9
#> 9 Inception (2010) 8.8
#> 10 Fight Club (1999) 8.8
#> # ... with 40 more rowsNote that all columns will be of character class. you’ll have to convert them according to your needs.
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.