Business Analytics With R Programming Languages

Business Analytics With R Programming Languages

Last updated on 10th Oct 2020, Artciles, Blog

About author

Bibin (Sr Technical Project Manager )

Highly Expertise in Respective Industry Domain with 10+ Years of Experience Also, He is a Technical Blog Writer for Past 4 Years to Renders A Kind Of Informative Knowledge for JOB Seeker

(5.0) | 12358 Ratings 1551

What is R analytics?

R analytics (or R programming language) is a free, open-source software used for all kinds of data science, statistics, and visualization projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

These integrations include everything from statistical functions to predictive models, such as linear regression. R also allows you to build and run statistical models using Sisense data, automatically updating these as new information flows into the model.

The language is built specifically for statistical analysis and data mining. R analytics is not just used to analyze data, but also to create software and applications that can reliably perform statistical analysis. In addition to the standard statistical tools, R includes a graphical interface. As such, it can be used in a wide range of analytical modeling including classical statistical tests, lineal/non-lineal modeling, data clustering, time-series analysis, and more.

Subscribe For Free Demo
[contact-form-7 404 "Not Found"]

Statisticians like using R because it produces plots and graphics that are ready for publication, down to the correct mathematical notation and formulae. It’s quite popular for its visualizations: graphs, charts, pictures, and various plots. BI analysts can use these types of visualizations to help people understand trends, outliers, and patterns in data.

Another reason for its popularity is that its command-line scripting allows users to store complex analytical methods in steps, to be reused later with new data.

Instead of having to reconfigure a test, users can simply recall it. This also makes it useful for validation and confirmation purposes. Researchers can explore statistical models to validate them or check their existing work for possible errors. Even though it’s known as a more complex language, it remains one of the most popular for data analytics.

Get the most out of data analysis using R :

R, and its sister language Python, are powerful tools to help you maximize your data reporting. Instead of using programming languages through a separate development tool like R Studio or Jupyter Notebooks, you can integrate R straight into your analytics stack, allowing you to predict critical business outcomes, create interactive dashboards using practical statistics, and easily build statistical models. Integrating R and Python means advanced analytics can happen faster, with accurate and up-to-date data.

What can I use R analytics for?

There are multiple ways for R to be deployed today across a variety of industries and fields. One common use of R for business analytics is building custom data collection, clustering, and analytical models.

Instead of opting for a pre-made approach, R data analysis allows companies to create statistics engines that can provide better, more relevant insights due to more precise data collection and storage.

More importantly, using R as opposed to boxed software means that companies can build in ways to check for errors in analytical models while easily reusing existing queries and ad-hoc analyses. In order to get the most out of your data, R, and its sister language, Python, should be a part of your analytics stack. They can be integrated in a way that makes them as easy to use as SQL.

In academia and more research-oriented fields, R is an invaluable tool, as these fields of study usually require highly specific and unique modeling.

As such, organizations can quickly custom-build analytical programs that can fit in with existing statistical analyses while providing a much deeper and more accurate outcome in terms of insights.

Even when it comes to social media or web data, R can usually provide models that deliver better or more specific insights than standard measures like page views or bounce rates. 

Business Analytics With R or commonly known as ‘R Programming Language’ is an open-source programming language and a software environment designed by and for statisticians. It is basically used for statistical computations and high-end graphics. Thus, it is a popular language among mathematicians, statisticians, data miners, and also scientists to do data analysis.

R is a GNU project, and is freely available under the GNU (General Public License), and R comes with pre-compiled binary versions for several operating systems ranging from Unix and similar systems (FreeBSD, Linux), Windows and also MacOS.

The inception of ‘R programming Language’!

R programming language was initially written by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand and now it is developed by the R Development Core Team. R is an implementation of ‘S’ programming language which was developed by John Chambers at Bell Labs. The name ‘R’ to a certain extent is derived from the initial names of its creators – Ross Ihaka and Robert Gentleman and to some extent it is based on the ‘S’ programming language. Also, a large group of folks has contributed to ‘R’ by sending code and bug reports to its developers!

What Does the ‘R’ language do?

‘R’ is a totally programmable computer language that:

  • Stores and handles data efficiently using a suite of numeric and textual operators for calculations on array and matrices.
  • Consists of tools for data analysis.
  • Is equipped with graphical services to provide understandable and informative data analysis.
  • Consists of no-nonsense programming techniques with well-developed functions, loops, conditionals, input-output facilities.
  • Consists of a LaTeX-like documentation format, which provides a broad range of documentation, both in the form of hardcopy and softcopy. This makes R easily extensible through functions and extensions, allowing developers to add to its present capabilities.

What doesn’t ‘R’ language do?

  • R is not a database, though R programming language easily connects to DBMSs (Database Management Systems).
  • R’s Language interpreter tends to be sluggish at times, though it has the facility to call its own C/C++ code.
  • R doesn’t consist of any graphical user interface, though it is friendly with Java, Tcl/TK.
  • R language doesn’t provide any spreadsheet view of data, though it connects to Excel/MS office easily.
Course Curriculum

Advance your Career with R Programming Training By World Class Faculty

  • Instructor-led Sessions
  • Real-life Case Studies
  • Assignments
Explore Curriculum

Some key characteristics of Business Analytics With R:

Characteristics-R-Data-Analysis

R is a very powerful enterprise-driven programming language which has the following striking features:

1. R is an open-source software!

Yes, R is free! It is licensed under GPL (just as Linux) and you have all the freedom to do whatever you want to do with R! You can be as creative as possible and make interesting modifications in it. R is open for integration into other systems too. While working on R programming language, you can access data whether it is on SAS, SPSS, SQL Server, Oracle or Excel and also integrate R in various applications and web-servers.

2. R programming is designed for Data Analysis!

R is primarily a data analysis software that consists of a vast collection of algorithms for data retrieval, processing, analysis and high-end statistical graphics. R has the built-in universal statistical methods such as mean, median, distributions, covariance, regression, non-linear mixed effects, GLM, GAM and the list just goes on… The functions of R programming language can access all the areas of the analysis results and combine analytical methods to reach certain conclusions which are crucial for the organizations.

For instance, precise information on the number of people (and their backgrounds) using a particular mobile handset can be very useful to a mobile company in leveraging its business.

3. R programming is Object-oriented!

Yes, it’s true! As compared to other statistical languages, R programming language has strong object-oriented programming facilities. This is because R has derived from S programming language. Though R is proficient in developing fully object-oriented programs, it’s approach to OOP is based on generic functions instead of class hierarchies. R consists of three OOP systems S3, S4 and R5. These features are based on the concepts of classes and methods. It will be unfair to compare R with typical object-oriented languages like Perl, Python, Ruby and so on.

4. R is an Interpreted Computer Language!

R is typically an interpreted computer language allowing some incredible branching, looping and modular programming using functions. The R distribution consists of functionality for a broad spectrum of statistical procedures such as time series analysis, classical parametric and nonparametric tests, linear and non-linear regression models, clustering, smoothing and so on. Also, advanced developers of R programming can write ‘C’ code to work on R objects directly.

5. R language produces high-end graphics!

R programming as a flexible graphical environment to offer a wide variety of graphical functions for data presentations such as bar plots, pie charts, histograms, time series, dot charts, image plots, 3D surfaces, scatter plots, maps, etc. Using R, you can customize your graphics endlessly, and develop fresh graphics by combining different graph types and have great FUN!

6. Business Analytics with R provides Advanced Analytics!

You can find various amazing domain-specific suites for R such as Rmetrics Project for computational finance and BioConductor for the analysis and comprehension of high-throughput genomic data. Apart from these suites, there are several add-on packages available for R such as CRAN (a set-up of ftp and web servers across the globe to store identical and latest versions of the code and documentation for R) and Task Views (Guides for the R functions and packages which are handy for certain methodologies and disciplines).

7. Business Analytics with R has a well-knit community!

R programming language has a vast community of 2 million people, which is growing exponentially! R is no longer just a programming language but a culture among various programmers world across. Surf the internet and in a fraction of a second you will find several websites, forums, blog posts, articles on R programming language. For instance, we have Crantastic, a community website for R packages where you can search for, review and tag CRAN packages. If you are looking for R tweets on twitter, this is how to go about it – #rstats hashtag on Twitter.

Thus, one thing we learnt about R programming language is that R is limitless in terms of data analysis. It has some outstanding features which can always be explored further for a powerful and flexible data computation. The way in which the functionality and popularity of R is growing, R programming language is going to stay for long and continue helping organizations in the complicated process of data analysis.

R Sample Resumes! Download & Edit, Get Noticed by Top Employers! Download

Programming Language like R just not helps in the technical fields, it will also be a great help in the business.

  • Here, the main reason is that R is open-source, therefore it may be modified and redistributed as per the user’s need. it’s great for visualization and has way more capabilities as compared to other tools.
  • For data-driven businesses, an absence of information Scientists may be a huge concern. Companies are using R programming as their core platform and also are recruiting trained R programmers.
  • It is prominently called the language of statistics. this is often the most reason, why R is dominant among other programming languages for developing statistical tools.
  • R Programming is employed within the financial domain to make econometric models, analyze fraudulent transactions.
  • It is employed by telecom sectors for Subscriber Profiling, Churn Management, and Personalised advertising
  • It is employed in computational biology to perform genomic analysis.

Are you looking training with Right Jobs?

Contact Us

Popular Courses