- Problems and opportunities with high-velocity data
- Characteristics of high-velocity data
- Real-time processing of high-velocity data with R
- Using R to acquire high-velocity data
- Polling for data with an R program
- Using Profvis, Rprof, and microbenchmark
- Optimizing R code for use with high-velocity data
- Using R to present high-velocity data
- Using R Markdown for static dashboards
Skill Level Intermediate
- [Mark] Hi, I'm Mark Niemann-Ross. High velocity data is like drinking from a fire hose with a dixie cup. It arrives at high speed demanding high performance from hardware and software alike. R, the programming language, isn't designed for this highly demanding task. It's an interpreted language with little or no access to the guts of a computer. However, with some careful implementation and a few tips and tricks, R can be revved up to handle high-speed data and draw useful real-time conclusions.
And I'm going to share some methods for using R with high-velocity data. I'll provide you with a framework for understanding different types of high-velocity data, how to acquire, process, and present high-velocity data. I'll cover interrupts, polling, Shiny, profiling, optimizing, dashboards, and websites with R. This course will show you skills you can use in real life to solve problems you're facing right now. So what are we waiting for? Let's learn how to handle high-velocity data with R.