R Programming for Data Science

R Programming for Data Science ? programming language for statistical analytics, data mining and visualization.

R Programming for Data Science

R Programming language offers various tools for Data Science

R Programming language is open source programming language with a runtime for running the R Programs. This programming language is more than just a programming language, it comes with many library for statistical analytics. Visual chart and graphs is very important in data visualization and R Programming provides best option for generation of such visual graphics for applications.
In this tutorial we are going to explore R Programming language and see how it can be used in Data Science projects.

R programming language is used to solve the business problem by analyzing data and providing suggestions to business. Developer main responsibility in the industry is to understand the business problem and come up with the analytical solution.

A good data scientist should learn Linear algebra, statistics & Probability skills, such as distributions, statistical testing, regression, etc… as there are important in analyzing business problem and come up with the analytical solution using R or SAS programming language. Data Scientists and Data miners prefer R Programming as its open source and provide all necessary tools.

Strength of R Programming for Data Science

R programming language is designed and developed keeping statistics, analytics, charts and graphs generation in mind. R programming language is first class programming language for Data mining related work.

R Programming for Data Science

R as data analytics software: R comes with 4000+ packages which are used for statistical analysis, data visualization and predictive modeling. Several Data professionals like scientist, statisticians, and data analysts and data miners are using R Programming happily to accomplish their job.

R as programming language: R is open source object oriented programming language and it provides pre developed libraries which are used mostly for statistical analytics.

Best environment for statistical analysis: R Programming language is developed by statisticians and many researches are done in R programming language first. So, any new technology of statistics computing first comes to the R programming language.

R an open source project with community support: R Programming language is open source project and it come with source code. Since this is open source project it is support by open community and you will be able to find answers to your query online easily.

R is community project: Leading 20 statisticians all the world is contributing towards this project. During past 23 years thousands of contributors created various packages and given it to community. All these contributions make R Programming language a big success with many tools for analyze data.

Top Data Science projects using R Programming

Following are the projects using R Programming language for data analytics:

Bank of America: Data analytics in financial modeling and visualization.

Chicago: Used R for real-time textual analysis for indentifying the source of food poisoning.

Facebook: Facebook is using R programming for exploring new data and visualizing it.

New Yourk Time: Used R programming for Election Forecast, data journalism etc...

Twitter: Twitter use R programming for monitoring user experience on its network.

Top packages of R Programming used for Data Science

As mentioned earlier R Programming language comes with rich set of pre-developed packages for various functionality. R language comes with 4000+ packages developed by R Group and various contributors around the world. But following the most used R Programming packages for Data Science projects:

sqldf: This package is used to perform SQL queries on R data frames.

forecast: This is used for time series analysis on the data models.

plyr: This package is used for splitting a data structure into groups and the apply a function individually to these groups. Finally result data is combined and returned in a data structure.

stringr: This package offers various string operators.

Database drivers: Database drivers for various database are available with the R Programming packages and it supports RMongo, SQLite, RMySQL etc… You can use this library to read data from these data sources in R Program.

lubridate: This package is used for working with dates and times in R Program.

ggplot2: This package provide library for producing spiffy plots.

qcc: qcc is a library for statistical quality control in R Programming.

reshape2: This package is used for data restructuring.

randomForest: This is a machine learning package for R Programming language.

R Programming tutorials

On our website we have written many tutorials to help beginners in learning R Programming. All the R Programming tutorials are available at:

R Programming Training

We are offering online, classroom and weekend training classes in R Programming language. Check following training courses:

Join our online training course in Data analytics with R Programming and learn it from our expert trainers. You will also develop sample project as part of R Programming training course. 0