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R High Performance Programming

By : Aloysius Shao Qin Lim, Tjhi W Chandra

4.4 (14)

R High Performance Programming

4.4 (14)

By: Aloysius Shao Qin Lim, Tjhi W Chandra

Overview of this book

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Understanding R's Performance – Why Are R Programs Sometimes Slow?

1. Understanding R's Performance – Why Are R Programs Sometimes Slow?

Three constraints on computing performance – CPU, RAM, and disk I/O

R is interpreted on the fly

R is single-threaded

R requires all data to be loaded into memory

Algorithm design affects time and space complexity

Summary

2. Profiling – Measuring Code's Performance

2. Profiling – Measuring Code's Performance

Measuring total execution time

Profiling the execution time

Profiling memory utilization

Monitoring memory utilization, CPU utilization, and disk I/O using OS tools

Identifying and resolving bottlenecks

Summary

3. Simple Tweaks to Make R Run Faster

3. Simple Tweaks to Make R Run Faster

Vectorization

Use of built-in functions

Preallocating memory

Use of simpler data structures

Use of hash tables for frequent lookups on large data

Seeking fast alternative packages in CRAN

Summary

4. Using Compiled Code for Greater Speed

4. Using Compiled Code for Greater Speed

Compiling R code before execution

Using compiled languages in R

Summary

5. Using GPUs to Run R Even Faster

5. Using GPUs to Run R Even Faster

General purpose computing on GPUs

R and GPUs

Fast statistical modeling in R with gputools

Summary

6. Simple Tweaks to Use Less RAM

6. Simple Tweaks to Use Less RAM

Reusing objects without taking up more memory

Removing intermediate data when it is no longer needed

Calculating values on the fly instead of storing them persistently

Swapping active and nonactive data

Summary

7. Processing Large Datasets with Limited RAM

7. Processing Large Datasets with Limited RAM

Using memory-efficient data structures

Using memory-mapped files and processing data in chunks

Summary

8. Multiplying Performance with Parallel Computing

8. Multiplying Performance with Parallel Computing

Data parallelism versus task parallelism

Implementing data parallel algorithms

Implementing task parallel algorithms

Executing tasks in parallel on a cluster of computers

Shared memory versus distributed memory parallelism

Optimizing parallel performance

Summary

9. Offloading Data Processing to Database Systems

9. Offloading Data Processing to Database Systems

Extracting data into R versus processing data in a database

Preprocessing data in a relational database using SQL

Converting R expressions to SQL

Running statistical and machine learning algorithms in a database

Using columnar databases for improved performance

Using array databases for maximum scientific-computing performance

Summary

10. R and Big Data

10. R and Big Data

Understanding Hadoop

Setting up Hadoop on Amazon Web Services

Processing large datasets in batches using Hadoop

Summary

Index

Index

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Converting R expressions to SQL

While SQL is a powerful and flexible language used to manipulate data in a database, not everyone is proficient in it. Fortunately, the R community has developed a few packages that translate familiar R syntax into SQL statements that are then executed on the database. We will look at two of them—dplyr and PivotalR.

Using dplyr

The dplyr package is a handy package designed to allow the manipulation of table-like data with a standard set of operations and transformations, no matter where the data is stored—in a data frame, data table, or database. It supports SQLite, PostgreSQL, MySQL, Amazon RedShift, Google BigQuery, and MonetDB databases.

The dplyr package provides a way to specify a set of operations to be performed on the data without actually performing the computations on the database server until we instruct R to do so, by calling the collect()function. By pooling a few operations together (as opposed to executing them one by one), the database server...

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