R Programming in Data Science: High Volume Data

Description

In this course, you will learn:

• Accessing memory and processing power
• Visualizing high-volume data
• Profiling and optimizing R code
• Compiling R functions
• Parallel processing with R
• Using R with other big data solutions

Syllabus:

• 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
• hcards
• Improving your reading comprehension
• Reading efficiently and critically
• Sentence completion technique
• Understanding passage-based reading questions
• Passage-based reading technique
• Tips for recognizing wrong answers

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