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Table of contents
1.
Introduction
2.
Approaches for Analysis of Big Data
3.
Custom applications for big data analysis
3.1.
R- environment
3.2.
Google Prediction API
3.3.
Semi-custom applications for big data analysis
4.
Frequently asked questions
4.1.
What is the role of the R- environment?
4.2.
Is R an open-source language?
4.3.
How many data structures are present in R?
4.4.
What are operators used in R?
4.5.
Why R- environment is considered the best fit for data analytics?
5.
Conclusion
Last Updated: Mar 27, 2024
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R- environment

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Introduction

R Programming Language is a top programming language of data science, consisting of effective functions to tackle all problems related to Big Data processing. The open-source scripting language was released in 1995. It has grown efficiently and has evolved as a go-to language for data scientists around the globe. R contains many data packages, shelf graph functions, etc. Tech giants like Microsoft and Google use R for Big data analysis.

Source: https://miro.medium.com/max/1024/1*TX77o_zJ4zbpJ3vN4BkLsg.jpeg

 

Now we will understand the approaches for analysis of Big Data using R.

Approaches for Analysis of Big Data

  • In many cases, extensive data analysis will be represented to the end-user through reports and visualizations. Since the raw data can be incomprehensively varied, you will have to use analysis techniques to help present the data in meaningful ways.
  • Data visualization techniques will help, but they will need to be improved by more complicated tools to examine big data.
  • As traditional reporting and visualization are familiar, they are insufficient, making it necessary to create new applications and approaches for big data analysis. Or else, you will be in a holding pattern until vendors start to catch up with the demand.
  • Early adoption of big data needs the creation of new applications designed to address analysis requirements and time frames.
  • Why is this so important? It is essential because a well-used representation from traditional data analysis will be insufficient.

 

These new applications can be divided into two categories:

  1. Custom: these are the ones that are coded from scratch.
  2. Semi-custom: these are based on some existing frameworks or components.

 

Now we will discuss both of them in detail.

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Custom applications for big data analysis

A custom application is generally created for a specific purpose or a related set of objectives. Particular areas of a business or organization will always require a custom set of technologies to support unique activities or provide a competitive advantage. For extensive data analysis, custom application development aims to speed the time to decide or the time to act.  

We will now examine some additional options available for those who may need custom analysis applications for big data.

R- environment

It is based on the "S" statistics and analysis language developed in the 1990s by Bell Laboratories. It is examined by the GNU project and is available under the GNU license. Among other advanced capabilities, it supports:

  • Programmers design programming language for programmers with more familiar constructs, including loops, user-defined recursive functions, conditionals, and a broad range of input and output facilities.
  • Most of the system-supplied parts are written in the S language.
  • Advanced visualization capabilities
  • Effective data handling and manipulation components.

Google Prediction API

It is an example of an emerging big data analysis application tool class. It is available on the Google developer's website and is well documented and provided with several mechanisms for access using different programming languages. It looks for similar patterns and matches them to proscriptive, prescriptive, or other existing designs.

Semi-custom applications for big data analysis

It is not always required to code a new application completely. Semi-custom applications can be an efficient approach because there are many examples of application building blocks to incorporate into your semi-custom application. Some of these include:

  • GeoToolsAn open-source geospatial toolkit for manipulating GIS data in many forms, analyzing spatial and non-spatial attributes or GIS data, and creating graphs and networks of the data.
  • TA-Lib: The Technical Analysis library is used extensively by software developers who need to perform technical analysis of financial market data.
  • GeoTools: An open-source geospatial toolkit for manipulating GIS data in many forms, analyzing spatial and non-spatial attributes or GIS data, and creating graphs and networks of the data.

Frequently asked questions

What is the role of the R- environment?

It is a Procedural Programming Language that breaks down a task into a sequence of Stages, Processes, and Subroutines. It allows R to transform data into meaningful Statistics, Graphs quickly and develop Statistical Learning Models for predictions and inferences.

Is R an open-source language?

Yes, R is an open-source language. R has an alternative to traditional statistical packages such as SPSS, SAS, and Stata. It is an extensible and computing environment for Windows, Macintosh, UNIX, and Linux platforms.

How many data structures are present in R?

There are primarily six types of Data Structures in R.  We can organize them based on their dimensions, i.e., 1D, 2D, and 3D. We can also classify them as h heterogeneous or homogeneous based on their content.

What are operators used in R?

Operators are the symbols directing the compiler to perform various operations between the operands. Operators simulate the different mathematical, logical, and decision processes performed on a set of Complex Numbers, Integers, and Numericals as input operands.

Why R- environment is considered the best fit for data analytics?

R has specific inbuilt plotting commands, making it easier to create simple graphs. At the same time, ggplot2 can be said as one of the most versatile data visualization packages. ggplot2 implements graphics grammar, a coherent system for describing and building graphs.

Conclusion

This article extensively discusses the R- environment. We understood the various approaches for Analysis of Big data analysis. We also discussed the custom application and semi-custom application for big data analysis.

Refer to the blog Big Data: A guide for beginners to learn more about Big Data in detail.

We hope that this blog helped you enhance your knowledge regarding the R- environment in Big data. You can refer to our guided paths on the Coding Ninjas Studio platform to learn more about DSA, DBMS, Competitive Programming, Python, Java, JavaScript, etc. To practice and improve yourself in the interview, you can check out Top 100 SQL problemsInterview experienceCoding interview questions, and the Ultimate guide path for interviews.

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