The volume of data that enterprises acquire every day is increasing exponentially. It is now possible to store these vast amounts of information on low cost platforms such as Hadoop.
The conundrum these organizations now face is what to do with all this data and how to glean key insights from this data. Thus R comes into picture. R is a very amazing tool that makes it a snap to run advanced statistical models on data, translate the derived models into colorful graphs and visualizations, and do a lot more functions related to data science.
One key drawback of R, though, is that it is not very scalable. The core R engine can process and work on very limited amount of data. As Hadoop is very popular for Big Data processing, corresponding R with Hadoop for scalability is the next logical step.
This book is dedicated to R and Hadoop and the intricacies of how data analytics operations of R can be made scalable by using a platform as Hadoop.
With this agenda in mind, this book will cater to a wide audience including data scientists, statisticians, data architects, and engineers who are looking for solutions to process and analyze vast amounts of information using R and Hadoop.
Using R with Hadoop will provide an elastic data analytics platform that will scale depending on the size of the dataset to be analyzed. Experienced programmers can then write Map/Reduce modules in R and run it using Hadoop's parallel processing Map/Reduce mechanism to identify patterns in the dataset.
R is an open source software package to perform statistical analysis on data. R is a programming language used by data scientist statisticians and others who need to make statistical analysis of data and glean key insights from data using mechanisms, such as regression, clustering, classification, and text analysis. R is registered under GNU (General Public License). It was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, which is currently handled by the R Development Core Team. It can be considered as a different implementation of S, developed by Johan Chambers at Bell Labs. There are some important differences, but a lot of the code written in S can be unaltered using the R interpreter engine.
R provides a wide variety of statistical, machine learning (linear and nonlinear modeling, classic statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. R has various built-in as well as extended functions for statistical, machine learning, and visualization tasks such as:
- Data extraction
- Data cleaning
- Data loading
- Data transformation
- Statistical analysis
- Predictive modeling
- Data visualization
It is one of the most popular open source statistical analysis packages available on the market today. It is crossplatform, has a very wide community support, and a large and ever-growing user community who are adding new packages every day. With its growing list of packages, R can now connect with other data stores, such as MySQL, SQLite, MongoDB, and Hadoop for data storage activities.