R programming language is the best tool for data reconfiguration and statistical analysis. R is specially built for statistics and is an ideal choice for data scientists looking to do behavioral analysis using the users’ data.
Designed by statisticians, R touts to be the programming choice by statisticians and big data professionals. The syntax makes it easy for the user to create complex models with minimal lines of code. It is open-source which is not limited to any type of operating system. And, since it is open-source, the language is being fully covered under the General Public License Agreement (GNU). One of the many reasons why it has become cost-efficient for projects of small or large size.
With big data analytics becoming a top priority for almost all organizations, it is evident that they would be needing more professionals skilled in the R programming language. It is found that over 60 percent of the people who had participated in a survey mentioning “analytics being the need of the hour” actually depends on data analytics to boost the organization’s marketing strategies, especially the social media marketing.
Why choose R for big data analytics?
Wondering what to choose for data analysis? Don’t worry, we will further talk in brief as to why R is an ideal choice for data professionals.
Data Wrangling
Also, referred to as data munging, data wrangling is the art of transforming data – from one raw format to another format to make the data much more valuable. There are three parts to it – import, tidy, and transform.
Data Visualization
Composes of inbuilt plotting commands, R is used to develop graphs. For someone with zero knowledge about data, it gets challenging to explain the insights derived from the data. Therefore, using data visualization tools, you can easily transform data in the form of graphs, pictorial representations, or charts. This helps explain data insights clearly to stakeholders or business people. Some of the names of data visualization tools include names like ggplot2, Tableau, and FusionCharts, and D3.js.
Data Analysis
R programming is a powerful language used in data analysis, and the termed used here is exploratory data analysis. This process involves multiple techniques like maximizing insights into the dataset, extraction of significant variables, and test assumptions.
RHadoop
The open-source RHadoop provides users the ability to analyze and manage data with Hadoop from the R environment.
As a data scientist or a big data professional, you’ll have to be acquainted with how to use R to utilize the capabilities of enterprise-grade of the MapR Hadoop distribution. The following list is the packages of RHadoop offering multiple functions to the user:
• rhbase – takes care of the connectivity to the HBase distributed database with the help of the Thrift server.
• ravro – an add-on ability that helps the user to read or write Avro files. These files are extracted from the local and HDFS file system. Avro input is also added for the rmr2.
• rhdfs – allows connection to the HDFS (Hadoop Distributed File System).
• plyrmr – R user gets the privilege to perform common data manipulation operations on large datasets that are stored in Hadoop.
• rmr2 – with this package the professional easily gets to perform statistical analysis in R using Hadoop MapReduce functionality available on a Hadoop cluster.
RHIPE
RHIPE is broadly classified as R and Hadoop Integrated Programming Environment. This software package lets the developer develop or design MapReduce tasks that function well in the R environment via R expressions.
The technique used in the package includes Recombine and Divide which makes it possible to perform data analytics. The integration of R to MapReduce is a transformative change and allows the analyst to start specifying Maps and Reduces with flexibility and full power.
If you’re keen on learning these techniques, you will find a few credible big data certification programs online. However, you need to be specific and pick the program that best fits your requirement.
ORCH
ORCH signifies Oracle R Connector for Hadoop – these R packages are ideal for providing predictive analytic techniques that have been written in Java or R programming language. This can be identified as Hadoop MapReduce jobs which applies to the data in the HDFS files.
Besides the techniques, ORCH also provides interfaces that allow users to work with the local R environment, Hive tables, and Apache Hadoop infrastructure, etc. You will also notice that ORCH encompasses multiple algorithms – neural networks for prediction, non-negative matrix factorization, and clustering, etc.
Look no further, R will always be the preferred choice for data analysis.
