R Programming in Data Science: High Velocity Data
Offered By: LinkedIn Learning
Course Description
Overview
Learn how to work your mojo on high-velocity data with R. Discover how to acquire, process, and present high-velocity data using this popular programming language.
Syllabus
Introduction
- How can you use R with high-velocity data?
- Perspectives on high-velocity data
- Simulating high-velocity data
- Concepts of batch data
- Handling batch data with R
- Working with near real-time data
- Handling near real-time data with R
- Concepts of real-time data
- Handling real-time data with R
- Setting a default CRAN mirror
- Polling for data in R
- Interrupt-driven data acquisition with R
- Tools
- Profvis
- Rprof
- microbenchmark
- Improving the speed of loops
- Optimizing if... then... else with ifelse
- Avoid copying data
- Combining optimizations
- Use RCPP to speed up functions
- Using microbenchmark to check results
- Static and dynamic reports
- Use R Markdown for static dashboards
- Flexdashboard and other enhancements for static reports
- Use Shiny for interactive dashboards
- Use plumber to create APIs
- Cran task view for WebTechnologies
- Summary
Taught by
Mark Niemann-Ross
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