Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

High performance computing


Initially, it is important to measure which lines of code take the most computation time. Here, you should try to solve problems with the processing time of individual calculations by improving the computation time. This can often be done in R by vectorization, or often better by writing individual pieces of code in a compilable language, such as C, C++*, or Fortran**.

In addition, some calculations can be parallelized and accelerated through parallel computing.

Profiling to detect computationally slow functions in code

Take an example where you have written code for your data analysis but it runs (too) slow. However, it is most likely that not all your lines of code are slow and only a few lines need improvement in terms of computational time. In this instance it is very important to know exactly what step in the code takes the most computation time.

The easiest way is to find this out is to work with the R function system.time. We will compare two models:

data(Cars93...