29Jan 2019 by Andrew Treadway
Creating a word cloud on R-bloggers posts

Creating a word cloud on R-bloggers posts

R, Web Scraping
This post will go through how to create a word cloud of article titles scraped from the awesome R-bloggers. Our goal will be to use R's rvest package to search through 50 successive pages on the site for article titles. The stringr and tm packages will be used for string cleaning and for creating a term document frequency matrix (with tm). We will then create a word cloud based off the words comprising these titles. First, we'll load the packages we need. [code lang="R"] # load packages library(rvest) library(stringr) library(tm) library(wordcloud) [/code] Let's write a function that will take a webpage as input and return all the scraped article titles. [code lang="R"] scrape_post_titles <- function(site) { # scrape HTML from input site source_html <- read_html(site) # grab the title attributes…
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19Jan 2019 by Andrew Treadway
Scraping data from a JavaScript webpage with Python

Scraping data from a JavaScript webpage with Python

Python, Web Scraping
This post will walk through how to use the requests_html package to scrape options data from a JavaScript-rendered webpage. requests_html serves as an alternative to Selenium and PhantomJS, and provides a clear syntax similar to the awesome requests package. The code we'll walk through is packaged into functions in the options module in the yahoo_fin package, but this article will show how to write the code from scratch using requests_html so that you can use the same idea to scrape other JavaScript-rendered webpages. Note: requests_html requires Python 3.6+. If you don't have requests_html installed, you can download it using pip: [cc] pip install requests_html [/cc] Motivation Let's say we want to scrape options data for a particular stock. As an example, let's look at Netflix (since it's well known). If…
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