The Power of R – And Why it’s an Essential Skill for Data Analysts

Abhirami Sankar

June 19th, 2014

power of R

The New York Times had described R as: “a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age, whether being used to set ad prices, find new drugs more quickly or fine-tune financial models.”

So, what is so enthralling about R’s powerful language?

R is an open source software and it is the language of statisticians and Data analysts. Its syntax and structure have been explicitly designed to formulate expressions about statistical objects. Particularly useful for Data analysis as it contains a number of built-in easy to use commands for organizing data and creating both numerical and graphical summaries of data. It is also a platform for predictive analytics and data science and runs on Windows, UNIX and MacOS. Not just that even in the field of big data analytics, R is used for handling complex and large data and can be used on high performance clusters.

You must definitely admit that R is known for its beautiful and unique data visualizations. Heavily influenced by thought leaders in data visualization like Bill Cleveland and Edward Tufte, R provides us with not just the typical bar charts and line plots which you find in excel and other softwares, but also various ways to represent multidimensional data using multi-panel charts and 3-D surfaces

Whether you are taking a look at the summary of data or building decision trees there is nothing that you cannot achieve with R! This is because CRAN provides you a well-defined set of packages (around 4000 so far) to perform any kind of data function that you wish! Some of the day to day R packages that you can use are plyr, sqldf, lattice, zoo, Rcurl and XML.

Here is a list of other interesting R Packages:

RGoogleTrends allows you to download Google Trends data directly from R.

FlashMXML can record R graphics in MXML (a kind of XML language) and you can compile the XML file to flash output.

SVGAnnotation enables us to save R graphics in SVG format, which also supports animation.

Shiny is an application package that helps you to convert all your statistical analysis in R to interactive web applications and No HTML or JavaScript knowledge is even necessary.

Coming to the GUI’s for R, RStudio is my favourite code editor that interfaces with R for Windows, MacOS X, and Linux platforms. A more stable version is the R-Commander. Another classy GUI is the Rattle (the R Analytical Tool to Learn Easily).It presents statistical and visual summaries of data and transforms data into forms that can be readily modelled. It also provides a milestone to more sophisticated processing and modelling in R itself, for unconstrained data mining as well as illustrates the R code that is used to achieve this. No one can deny that this is indeed a big boon for advanced data miners!

R is mainly used in interactive sessions,  that is, a user types instructions into the command line of the programming language from his computer. These instructions are then executed and the result is displayed on-screen, and then the programming language waits for the next command. But you can use R for Batch processing as well. Batch processing typically means that the user can pass all his commands in advance as a script file (e.g. input.R) in one shot without waiting for human

Related Searches :

Examples of How R is Used

Handling Big Data Using R

 The Winning Combo: SAS and R


Interested in learning about other Analytics and Big Data tools and techniques? Click on our course links and explore more.
Jigsaw’s Data Science with SAS Course – click here.
Jigsaw’s Data Science with R Course – click here.
Jigsaw’s Big Data Course – click here.

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