{"id":66568,"date":"2018-12-11T11:29:30","date_gmt":"2018-12-11T10:29:30","guid":{"rendered":"https:\/\/internetrepublica.com\/?p=66568"},"modified":"2019-01-11T11:54:47","modified_gmt":"2019-01-11T10:54:47","slug":"web-scraping-con-r","status":"publish","type":"post","link":"https:\/\/internetrepublica.com\/blog\/web-scraping-con-r\/","title":{"rendered":"Web Scraping con R"},"content":{"rendered":"

\u00bfQu\u00e9 es web scraping?<\/h2>\n

El Web Scraping<\/strong> (<\/em>rascado en espa\u00f1ol) es una t\u00e9cnica que se usa para la extracci\u00f3n de datos<\/strong> no estructurados de una p\u00e1gina web<\/strong>, los cu\u00e1les podemos analizar o manipular posteriormente.
\nEn ocasiones interesa descargar cierta informaci\u00f3n directamente de una o varias p\u00e1ginas de un sitio web, aunque esta no se encuentre f\u00e1cilmente accesible.<\/p>\n

Las posibles usos del web scraping son muy variados y, aunque el ejemplo que vamos a incluir m\u00e1s abajo, vamos a realizar una extracci\u00f3n de informaci\u00f3n puramente SEO, para saber qu\u00e9 listados de informaci\u00f3n publica la competencia, existen otros muchos usos: clasificaci\u00f3n de productos\/servicios para creaci\u00f3n de motores de recomendaci\u00f3n, obtenci\u00f3n de datos para creaci\u00f3n de sistemas basados en procesamiento de lenguajes naturales, obtenci\u00f3n de las etiquetas de im\u00e1genes para la clasificaci\u00f3n de contenido gr\u00e1fico, obtenci\u00f3n de datos de redes sociales para an\u00e1lisis de sentimientos\u2026<\/p>\n

\u00bfPor qu\u00e9 usar R?<\/h2>\n

Aunque para el web scraping existen complementos o software especializado, nosotros nos decantamos por usar el lenguaje de programaci\u00f3n R<\/strong> (aunque no es el \u00fanico lenguaje) y sus librer\u00edas. Esto nos permite programar un algoritmo a medida<\/strong>, lo que nos da mucha m\u00e1s flexibilidad: programar extracciones con cierta frecuencia, gestionar errores o adaptarnos a los cambios de la web de origen con mayor flexibilidad. Este es el mejor m\u00e9todo para realizar web scraping complejos, con un gran volumen de datos y frecuentes.<\/p>\n

Ejemplo pr\u00e1ctico: \u00bfC\u00f3mo realizar la extracci\u00f3n de los tratamientos m\u00e9dicos que incluye cun.es?<\/h2>\n

Este caso, el acceso a las diferentes p\u00e1ginas del listado se genera mediante AJAX<\/strong>. En estos casos, necesitar\u00edamos una soluci\u00f3n que incorpore un motor de Javascript<\/strong>.
\nPara hacer scraping con R, necesitamos dos cosas b\u00e1sicamente:<\/p>\n

    \n
  1. Tener instalado R y seguir los pasos que se incluyen a continuaci\u00f3n.<\/strong><\/li>\n
  2. Tener conocimientos b\u00e1sicos de HTML y CSS.<\/strong><\/li>\n<\/ol>\n

    Para hacer web scraping con R<\/strong> debemos hacer uso del paquete \u201cRvest<\/strong>\u201d. Esta librer\u00eda b\u00e1sicamente permite extraer datos de una p\u00e1gina usando HTML<\/strong> y XML<\/strong>. Podemos instalarla escribiendo en la consola lo siguiente:<\/p>\n

    install.packages(\u201crvest\u201d)<\/p><\/blockquote>\n

    A continuaci\u00f3n tecleamos:<\/strong><\/p>\n

    library(\u201crvest\u201d)<\/p><\/blockquote>\n

    Para saber c\u00f3mo funciona con m\u00e1s detalle podemos acceder a la siguiente direcci\u00f3n: https:\/\/cran.r-project.org\/web\/packages\/rvest\/rvest.pdf<\/a>
    \nPara nuestro ejemplo, basta con saber que unos de lo m\u00e9todos que se incluyen en la librer\u00eda es read_html(\u201curl\u201d). Esta funci\u00f3n permite crear un objeto que contiene todas las etiquetas HTML<\/strong>.
    \nPara crear el objeto escribimos en consola:<\/p>\n

    html.cun <- read_html(“https:\/\/www.cun.es\/enfermedades-tratamientos\/tratamientos”)<\/p><\/blockquote>\n

    Cuando tenemos todas las etiquetas HTML<\/strong>, podemos acceder a cualquiera de sus nodos, que no son otra cosa que p\u00e1rrafos, im\u00e1genes, enlaces, tablas\u2026
    \nAunque las necesidades de obtener datos dependen de cada proyecto, en este caso, por simplificar, se extraer\u00e1n todos los tratamientos que incluye la web de     CUN<\/a>.
    \nPara ello, debemos identificar el nodo es cuesti\u00f3n haciendo uso de XPath o CSS Selector. Con     Chrome<\/a> es muy f\u00e1cil hacer esto ya que podemos inspeccionar un elemento en la consola y copiar el XPath:
    \n\"\"
    \nUna vez que hemos copiado el XPath<\/strong>, lo que demos es llamar a las funciones html_nodes() y html_text() de la siguiente forma para extraer los datos:<\/p>\n

    tratamientos <- html.definicion.temp %>% html_nodes(xpath = ‘\/\/div[contains(@id,”main”)]’) %>% html_text()<\/p><\/blockquote>\n

    Con html_nodes() obtendr\u00edamos un listado de los div cuyo id es \u201cmain\u201d y con html_text() extraer\u00edamos \u00fanicamente el texto, que es lo que almacenar\u00edamos en la variable \u201ctratamientos<\/strong>\u201d.
    \nDe esta forma, estar\u00edamos obteniendo todos los tratamientos que se incluyen en la p\u00e1gina en cuesti\u00f3n.
    \n\"\"
    \nUna vez obtenida informaci\u00f3n, ya podemos usarla para fines SEO y para realizar cualquier tipo de an\u00e1lisis.<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u00bfQu\u00e9 es web scraping? El Web Scraping (rascado en espa\u00f1ol) es una t\u00e9cnica que se usa para la extracci\u00f3n de datos no estructurados de una…<\/p>\n","protected":false},"author":20,"featured_media":66580,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[119],"tags":[1150,1145,1143,1149,1146,1148,1147,1144],"class_list":["post-66568","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seo","tag-etiquetas-html","tag-extraccion-de-datos","tag-html","tag-motores-de-recomendacion","tag-pagina-web","tag-servicios","tag-tecnicas-seo","tag-web-scraping"],"yoast_head":"\nWeb Scraping con R - Internet Rep\u00fablica<\/title>\n<meta name=\"description\" content=\"\u00bfQu\u00e9 es web scraping? 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