As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. “Machine Learning” is just the most recent case in point. It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Come on folks. Let’s make up our minds.
One of the biggest problems in Business to carry out any analysis is the availability of Data. That is where in many cases, Web Scraping comes very handy in creating that data that’s required. Consider the following case: To perform text analysis on Textual Data collected in a Telecom Company as part of Customer Feedback or Reviews, primarily requires a dictionary of Telecom Keywords. But such a dictionary is hard to find out-of-box. Hence as an Analyst, the most obvious thing to do when such dictionary doesn’t exist is to build one. Hence this article aims to help beginners get started with web scraping with rvest in R and at the same time, building a Telecom Dictionary by the end of this exercise.
If you’d like to learn how you use R to develop AI applications, the Microsoft AI School now features a learning path focused on Microsoft R and SQL Server ML Services.
R Markdown is a well-known tool for reproducible science in R. In this article, I will focus on a few tricks with R inline code. Some time ago, I was writing a vignette for my package WordR. I was using R Markdown. At one point I wanted to show `r expression` in the output, exactly as it is shown here, as an inline code block.