Skip to main content

R Language Tutorial for Mainframe Programmers

R+Jobs
IT+Jobs with R Language Skills
Why R? It's free, open source, powerful and highly extensible. "You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants," Google's chief economist told The New York Times back in 2009.

Learn to use R: Your hands-on guide

Part 1: Introduction
Part 2: Getting your data into R
Part 3: Easy ways to do basic data analysis
Part 4: Painless data visualization
Part 5: Syntax quirks you'll want to know
Part 6: Useful resources

Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That lets you re-use your analysis work on similar data more easily than if you were using a point-and-click interface, notes Hadley Wickham, author of several popular R packages and chief scientist with RStudio.

That also makes it easier for others to validate research results and check your work for errors -- an issue that cropped up in the news recently after an Excel coding error was among several flaws found in an influential economics analysis report known as Reinhart/Rogoff.

Why not R? Well, R can appear daunting at first. That's often because R syntax is different from that of many other languages, not necessarily because it's any more difficult than others.

How R is different from Excel?

The error itself wasn't a surprise, blogs Christopher Gandrud, who earned a doctorate in quantitative research methodology from the London School of Economics. "Despite our best efforts we always will" make errors, he notes. "The problem is that we often use tools and practices that make it difficult to find and correct our mistakes."

Sure, you can easily examine complex formulas on a spreadsheet. But it's not nearly as easy to run multiple data sets through spreadsheet formulas to check results as it is to put several data sets through a script, he explains.

Where to download R-Free version?

Click here -You can download here.

Comments

Popular posts from this blog

The best 5 differences of AWS EMR and Hadoop

With Amazon Elastic MapReduce (Amazon EMR) you can analyze and process vast amounts of data. It does this by distributing the computational work across a cluster of virtual servers running in the Amazon cloud. The cluster is managed using an open-source framework called Hadoop.

Amazon EMR has made enhancements to Hadoop and other open-source applications to work seamlessly with AWS. For example, Hadoop clusters running on Amazon EMR use EC2 instances as virtual Linux servers for the master and slave nodes, Amazon S3 for bulk storage of input and output data, and CloudWatch to monitor cluster performance and raise alarms.

You can also move data into and out of DynamoDB using Amazon EMR and Hive. All of this is orchestrated by Amazon EMR control software that launches and manages the Hadoop cluster. This process is called an Amazon EMR cluster.


What does Hadoop do...

Hadoop uses a distributed processing architecture called MapReduce in which a task is mapped to a set of servers for proce…

5 Things About AWS EC2 You Need to Focus!

Amazon Elastic Compute Cloud (Amazon EC2) - is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.
Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction.

The basic functions of EC2... 
It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment.Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change.Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate themselves from common failure scenarios. 
Key Points for Interviews:

EC2 is the basic fundamental block around which the AWS are structured.EC2 provides remote ope…

6 Most Popular IoT Protocols Currently Being Used

The below is complete list of Protocols being used in Internet of things projects.

CoAP: Constrained Application Protocol. MQTT: Message Queue Telemetry Transport. XMPP: Extensible Messaging and Presence Protocol. RESTFUL Services: Representational State Transfer. AMQP: Advanced Message Queuing Protocol Websockets. 
Related:
5 Challenges in Internet-of-things mostly people look inHot IT Skills by Udemy and Dice