Basic text string functions in R

An Overview of Data Analysis Tools to Start the Journey of Exploration
The Digital Revolution spawned by the PC and digital communications is driving a new ecosystem of businesses forward. They are capitalizing on this rich new resource we call ‘data’ to provide services via analysis and reporting. The deluge of information is disrupting all business verticals.

Survival Analysis with Plotly: R vs. Python
In this notebook we introduce Survival Analysis using both R and Python. We will compare programming languages and leverage Plotly’s Python and R APIs to convert graphics to interactive Plotly objects.

Introductory Time-Series analysis of US Environmental Protection Agency (EPA) pollution data
This example shows how to download and access the open pollution data for the US available from the EPA directly from R. Moreover we have seen here how to map the locations of the stations and subset the dataset. We also looked at ways to perform some introductory time-series analysis on pollution data.

Most Viewed Data Mining Videos on YouTube
The top Data Mining YouTube videos by those like Google and Revolution Analytics covers topics ranging from statistics in data mining to using R for data mining to data mining in sports.

Posterior predictive output with Stan
I continue my Stan experiments with another insurance example. Here I am particular interested in the posterior predictive distribution from only three data points. Or, to put it differently I have a customer of three years and I’d like to predict the expected claims cost for the next year to set or adjust the premium.

Interactive charts in R
I’m giving a talk tomorrow at the Edinburgh R usergroup (EdinbR) on how to get started building interactive charts in R. I’ll talk about rCharts as a great general entry point to quickly generating interactive charts, and also the newer htmlwidgets movement, allowing interactive charts to be more easily integrated with RMarkdown and Shiny. I also tried to throw in a decent amount of Edinburgh-related examples along the way.

How Predictable is the English Premier League?
I wanted to look at uncertainty over the long run in English football. To do this used the data provided by and analyzed these with R. These data consist of 34,740 matches played in the top 5 divisions of English football between 2000 and 2015, containing information about both the result and the odds offered by bookies on this result.

Query Multiple Google Analytics View IDs with R
Extracting Google Analytics data from one website is pretty easy, and there are several options to do it quickly. But what if you need to extract data from multiple websites or, to be more precise, from multiple Views? And perhaps you also need to summarize it within a single data frame?

Scraping jQuery DataTable Programmatic JSON with R
School of Data had a recent post how to copy ‘every item’ from a multi-page list. While their post did provide a neat hack, their ‘words of warning’ are definitely missing some items and the overall methodology can be improved upon with some basic R scripting.