R and package masking – a real life example
Very often, in our coding life, we forget one very simple yet important point to take care to: the importance of conflicts between packages. When you load a library in best case the command line is executed with no problem. However, the console is sometimes stuffed with red messages that we often ignore, moreover when the function we need reach the desired goal. Sooner or later during your R-user life you could stack in an error, and you could get crazy because you are sure of the accuracy of data, parameters and everything else. The problem is exactly the upload of two or more packages that conflicts because they share the same name for different functions, that cannot be used by R simultaneously.
Design and Redesign in Data Visualization
Fernanda Viégas and Martin Wattenberg have written a wonderful piece titled Design and Redesign in Data Visualization about criticism in data visualization. They thoughtfully analyze the practice and point out some of the issues when people create redesigns, including intellectual honesty and perfect hindsight. They then go on to define some ‘rules of engagement’ for a more reasonable approach to redesign. They argue for a kinder, more respectful, and more balanced process. Their ideas are informed by the critique in design and certainly make a lot of sense for visualization.
Python and R: Basic Sampling Problem
In this post, I would like to share a simple problem about sampling analysis. And I will demonstrate how to solve this using Python and R. The first two problems are originally from Sampling: Design and Analysis book by Sharon Lohr.
Scraping Web Pages With R
One of the things I tend to avoid doing in R, partly because there are better tools elsewhere, is screenscraping. With the release of the new rvest package, I thought I’d have a go at what amounts to one of the simplest webscraping activites – grabbing HTML tables out of webpages.
scikit-learn video #2: Setting up Python for machine learning
Last Wednesday, I introduced my new weekly video series, ‘Introduction to machine learning with scikit-learn’. Over the next few months, you’ll learn how to perform effective machine learning using Python’s scikit-learn library in order to advance your data science skills. I’ll be covering machine learning fundamentals and best practices, as well as how to implement those practices using scikit-learn.
Recommending Recommender Systems When Preferences Are Not Driven By Simple Features
Using a Talent Analytics Dashboard to Forecast Team Behavior
Analytics can be used in a variety of ways to demonstrate real business value for organizations. Talent analytics, defined as the measurement of an organization’s talent, is no exception. Imagine being able to use analytics to forecast at a glance visually how four different teams within a large organization may interact with each other? With today’s talent analytics technology you’re able to identify trends and forecast employees behavior at the team (or organizational) level. CEOs and Human Capital executives are then able to leverage this data to align talent assets strategically. See below for a screen shot.