R, the open source scripting language was released in 1995 and since then it has grown efficiently and has become a go-to language for the data scientists around the globe. R includes a large number of data packages, shelf graph functions, etc. which proves as a proficient language for big data analytics as it has effective data handling capability. Tech giants like Microsoft, Google are using R for large data analysis. In this article, we list down 6 ways R, the statistical language can be utilised for big data analytics.

1| Data Analysis

Exploratory data analysis is a term minted in data analysis using R. This is an approach for data analysis which includes a variety of techniques such as extraction of important variables, test underlying assumptions, maximising insights into the dataset, etc.

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2| Data Visualisation

R has certain inbuilt plotting commands which makes it easier to create simple graphs. While ggplot2 can be said as one of the most versatile data visualisation package. ggplot2 implements the grammar of graphics which is a coherent system for describing and building graphs. This package allows the user to add, remove or alter components in a plot at a high level of abstraction.

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3| Data Wrangling

Data Wrangling is the art of getting your data into R in a useful form for visualisation and modelling. It encompasses data transformation and plays a crucial part during a project. It includes basically three main parts, import, tidy and transform.

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4| RHIPE

RHIPE stands for R and Hadoop Integrated Programming Environment. It is a software package which allows the R user to create MapReduce jobs that work entirely within the R environment using R expressions. The package uses the Divide and Recombine technique to perform data analytics over Big Data. This integration with R is a transformative change to MapReduce as it allows an analyst to quickly specify Maps and Reduces using the full power, flexibility, and expressiveness of the R interpreted language.

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5| ORCH

ORCH stands for Oracle R Connector for Hadoop is a collection of R packages which provides predictive analytic techniques, written in R or Java as Hadoop MapReduce jobs, that can be applied to data in HDFS files. It also provides interfaces to work with Hive tables, the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables. There are several analytic algorithms in ORCH such as linear regression, neural networks for prediction, clustering, matrix completion using low-rank matrix factorization, and non-negative matrix factorization.

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6| RHadoop

RHadoop is an open source collection of five R packages which allows users to manage as well as analyse data with Hadoop from an R environment. It allows data scientists familiar with R to quickly utilize the enterprise-grade capabilities of the MapR Hadoop distribution directly with the analytic capabilities of R. The three packages of RHadoop are as follows

rhdfs – This package provides basic connectivity to the Hadoop Distributed File System.

rmr2 – This package allows R developer to perform statistical analysis in R via Hadoop MapReduce functionality on a Hadoop cluster.

rhbase – This package provides basic connectivity to the HBASE distributed database, using the Thrift server.

plyrmr – This package enables the R user to perform common data manipulation operations, as found in popular packages such as plyr and reshape2, on very large data sets stored on Hadoop.

ravro – This package adds the ability to read and write avro files from local and HDFS file system and adds an avro input format for rmr2.

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