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R is great for machine learning, data visualization and analysis, and some areas of scientific computing.

R is Becoming the Standard
In a world in which time is limited and that involved in learning a statistical package is nontrivial, learning to program in a system that is unpopular or unsustainable can be futile and frustrating.Spark R training Mumbai  I will not make any predictions as to the life expectancy of any propriety software options out there except to say, there are a lot of expensive options in a market in which the most competitive option (R) is free.  I do not know how long proprietary options will be around, but some version of R is likely to remain popular for the indefinite future.  R is well maintained by an active and highly talented community.Spark R training Mumbai
Thus, as the emerging standard for statistical programming, it is likely to be a highly rewarding process (both fiscally and in terms of opportunities) to learn to use R.

The June update to Apache Spark brought support for R, a significant enhancement that opens the big data platform to a large audience of new potential users. Support for R in Spark 1.4 also gives users an alternative to Python. But which language will emerge as the winner for doing data science in Spark? We spoke to Databricks Ali Ghodsi for answers.

According to Ghodsi, who is Databricks’ vice president of engineering and product management, the company has been bombarded with requests over the past year or so to add support for
Spark R training MumbaiR in Apache Spark. While the software is open source, about three quarters of the framework was written by people who work for Databricks, so it basically controls the direction of Spark.
The outcry for R in Spark was loud and consistent. “I’m shocked by the number” of requests, Ghodsi tells Datanami. “There’s been this explosive growth, especially in the last year, for doing data science in R. I, for one, couldn’t understand where it’s coming from.”
Ghodsi researched the matter, and concluded that much of R’s growth stems from the fact that it’s become the main statistical language taught in colleges.R Spark training Mumbai “When people would go to school and take psychology and biology classes, back in the day they’d be taught SPSS or SAS,” he says. “Now they’re taught R from our R Spark training experts available in Mumbai. These are not necessarily people who have computing background, but the statistics they learn for talking to a computer is R.”
Whereas R is growing in popularity across scientific disciplines, Python’s strength stems from its popularity within computer science as a general-purpose programming language. Interest in Python in booming, and not just among those practicing data science, but across all realms of computer science. According to the latest TIOBE Index, which measures the relative popularity of languages, Python moved up three spots within the last year to claim the number five spot. Meanwhile, R moved up from number 28 on the list to number 17.
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It’s tough to forecast which language—R or Python—will win in the end. “Clearly these are the two popular languages that people want to do when they do data science,” Ghodsi says. “R has been growing faster. I’m not sure about absolute numbers, which one will win….[But whether] you’re a Python person or an R person, it’s making it simpler and lowering the bar for people to join and talk to their big data.”
Spark’s rocket to big data fame ignited about two years ago, and is fueled largely by how much easier and faster it is to use compared to MapReduce, which had been the go-to framework for doing big data science since the Hadoop train started rolling about 10 years ago. Not only does Spark let users program in languages besides Java, but it delivers a much more interactive experience than the batch-oriented MapReduce framework from our R Spark training experts available in Mumbai.

Dataframes today supports Spark’s machine learning and SQL libraries, and will support the graph database and Spark Streaming libraries in the future. Eventually, Dataframes will be the main way that people interact with Spark, Ghodsi says. “One of the main ways you talk to Spark is Dataframes,” he says. “If you’re using R or if you’re using Python or even if you’re using Scala, there’s a Dataframe way you can speak to Spark.”

The Spark framework is evolving at a fast pace, and one of the most important enhancements was the version 1.3 release of Dataframes, which is essentially a “smashup” of different statistical vectors, according to Ghodsi.R Programming training Mumbai It’s interesting that the Dataframes concept was originally developed within R, and the folks behind Spark saw how powerful that approach could be, so they copied it. The Python community also has its version of a Dataframe, which is embodied in the Pandas project. Spark today support both flavors of Dataframes, in R and Python Pandas, as well as Dataframes for Scala.

The ease of doing statistics in R is driving that language’s popularity, Ghodsi says. “That’s one of the big attractions ofSpark R Machine learning training Mumbai R is all these built in statistics libraries, and also all these built-in plotting functions,” he says. “Python is closer to people with computing technology backgrounds. If you’re a programmer, or come from that background, Python might be more natural.”
Apache Spark, the open-source, cluster computing framework originally developed in the AMPLab at UC Berkeley and now championed by Databricks is rapidly moving from the bleeding edge of data science to the mainstream. Interest in Spark, demand for training and overall hype is on a trajectory to match the frenzy surrounding Hadoop in recent years. Next month’s Strata + Hadoop World conference, for example, will offer three serious Spark training sessions: Apache Spark Advanced TrainingSparkCamp and Spark developer certification with additional spark related talks on the schedule. It is only a matter of time before Spark becomes a big deal in the R world as well.
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From here, the next obvious question is: “How do I use Spark with R?” Spark itself is written in Scala and has bindings for Java, Python and R. Searching for a Spark demo online, however, will most likely turn up either a Scala or Python example. sparkR, the open source project to produce an R binding, is not as far along as the other languages. Indeed, a Cloudera web pagerefers to SparkR as “promising work”. The SparkR GitHub page shows it to be a moderately active project with 410 commits to date from 15 contributors.
SparkR Word count Example
Spark R training Mumbai
Note the sparkR lapply() function which is an alias for the Spark map and mapPartitions functions.
These are still early times for Spark and R. We would very much like to hear about your experiences with sparkR or any other effort to run R over Spark.
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