R Programming in Data Science: High Volume Data

Introduction

• Wrangling high-volume data with R
• Sample data set

1. Problems and Opportunities with High-Volume Data

• Perspectives on high-volume data
• Big data and available memory
• Code: Finding available memory
• Big data and CPU cycles
• Code: How fast is your computer?

2. Visualizing High-Volume Data

• High-volume data and visualizations
• Code: Graphs for high-volume data
• Code: rug() and jitter()
• Code: Applying statistics to plots
• Code: Subsampled graphs for high-volume data
• Code: Trellising data across multiple charts

3. Working within the R Programming Language

• R programming tools for high-volume data
• Downsampling
• Profile R code to find inefficiencies
• Code: Profile R code to find inefficiencies
• Avoid the copy-on-modify problem with R
• Code: Avoid copy-on-modify with data.table
• Optimization versus readability

4. Advanced High-Volume Techniques

• Compile R functions
• Parallel processing with R
• Code: Parallel R functions
• bigmemory, LaF, and ff packages

5. Use R with External Big Data Solutions

• Store high-volume data in a database
• Code: R with databases
• Cloud computing with R
• Sparklyr with R
• Code: R with Sparklyr

Conclusion

• Summary of high-volume data with R