R Programming in Data Science: High Velocity Data

Introduction

• How can you use R with high-velocity data?

1. Problems and Opportunities 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

2. Using R to Acquire High-Velocity Data

• Polling for data in R
• Interrupt-driven data acquisition with R

3. Profiling Tools for R

• Tools
• Profvis
• Rprof
• microbenchmark

4. Optimizing R to Process High-Velocity Data

• 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

5. Using R to Present High-Velocity Data

• 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

Conclusion

• Summary