In order to make your code go faster, you need to know how long it takes to run. This chapter introduces the idea of benchmarking your code.

Welcome!

R version

Benchmarking

Comparing read times of CSV and RDS files

Operational differences: "<-" and "="

Elapsed time

Relative time

How good is your machine?

DataCamp hardware

Benchmark DataCamp's machine

The Art of Benchmarking

R is flexible because you can often solve a single problem in many different ways. Some ways can be several orders of magnitude faster than the others. This chapter teaches you how to write fast base R code.

Memory allocation

Why is this piece of code slow?

Timings - growing a vector

Timings - pre-allocation

Importance of vectorizing your code

Vectorized code: multiplication

Vectorized code: calculating a log-sum

Data frames and matrices

Data frames vs. matrices

Data frames and matrices - column selection

Selecting a row in a data frame

Row timings

Fine Tuning: Efficient Base R

Profiling helps you locate the bottlenecks in your code. This chapter teaches you how to visualize the bottlenecks using the profvis package.

What is code profiling

Profiling a function

Where's the hold-up?

Profvis in action

Profvis: Larger example

Change the data frame to a matrix

Calculating row sums

Use && instead of &

Monopoly overview

Diagnosing Problems: Code Profiling

Some problems can be solved faster using multiple cores on your machine. This chapter shows you how to write R code that runs in parallel.

CPUs - why do we have more than one

How many cores does this machine have?

What sort of problems benefit from parallel computing?

Can this loop run in parallel (1)?

Can this loop run in parallel (2)?

The parallel package - parApply

Moving to parallel programming

Moving to parApply

The parallel package - parSapply

Using parSapply()

Timings parSapply()

You can write efficient R code!

Turbo Charged Code: Parallel Programming

Information on 45,000 movies

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Intermediate R

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

Big Data en R

R Développeur

Principes de base de la programmation R

Jumping Rivers & Newcastle University

The importance of vectorizing your code

General rule

Vectorized functions

Generating random numbers

Why is the loop slow?

R club

Let's practice!

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Fine Tuning: Efficient Base R - Importance of vectorizing your code

Importance of vectorizing your code

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