R Programming Language

R is a programming language and an analytics tool that was developed in 1993 by Robert Gentleman and Ross Ihaka at the University of Auckland, Auckland, New Zealand. It is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. R is one of the most popular analytics tool used in Data Analytics and Business Analytics. It has numerous applications in domains like healthcare, academics, consulting, finance, media, and many more. Its vast applicability in Statistics, Data Visualization, and Machine Learning have given rise to the demand for certified trained professionals in R.

Some important features of R are as follows:

It is a free and open-source programming language issued under GNU (General Public License).
It has multi-platform interoperability which means that it has distributions running on Windows, Linux, and Mac. R code can easily be ported from one platform to another.
It uses an interpreter instead of a compiler, which makes the development of code easier.
It can easily and efficiently associate different databases, and it does well in bringing in information from Microsoft Excel, as well as, Microsoft Access, MySQL, SQLite, Oracle, etc.
It is a pliable and capable language that bridges the gap between Software Development and Data Analysis.
It provides a wide variety of packages with the diversity of codes, functions, and features tailored for data analysis, statistical modeling, visualization, Machine Learning, and importing and manipulating data.
It is a combination of various power tools to communicate reports in different forms like CSV, XML, HTML, and pdf, through interactive websites, with the help of R packages.

Steps to perform Data Analysis in R:

Import: The first step is to import data into the R environment. It means that you take the data stored in files, databases, HTML tables, etc., and load it into an R data frame to perform data analysis on it.
Transform: In this step, first, we make our data tidy by making each column a variable, and each row an observation. Once we have tidy data, we narrow down on it to find observations of our interest, create new variables that are functions of existing variables, and find summary statistics of the observations.
Visualization: It is used to make our data more understandable by representing data in graphical form. Visualization makes it easy to recognize patterns, find trends, and exceptions in our data. It enables us to convey information and results quickly and visually.
Model: Models are complementary tools for visualization. These are fundamentally mathematical or computational tools used to answer questions related to our observations.
Communication: In this last step of data analysis, we focus on communicating the results from visualization, and modeling with others provides the ease to produce well-designed print- quality plots for sharing worldwide.

Companies using R

Google: R is a language that is widely used by Google. Google has a project called Google Flu Trends, which uses R to estimate current flu activity. Google also uses R Programming to make their advertising more effective.
Facebook: If you have a Facebook account, then you’d probably be updating your status and putting up profile pictures. Facebook is able to do this with the help of R programming
Airbnb: Airbnb has created its own R package called ‘Rbnb’ for the purpose of visualization of data scaling.

Salary Trends for R Programming

According to Survey by O’Reilly Data Scientist Survey, the median salary for R skills is around $115,000. With R skills, you have an opportunity to apply for different job roles such as:

• Statistical Analyst
• Data Analyst
• Data Scientist

Conclusion

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