R is a software programming language developed in 1993. In New Zealand, two professors of Auckland University Ross Ihaka and Robert Gentleman first conceived R. The most stable beta version of R was made in 2000. Here ‘R’ holds an extensive catalog produced of statistics and graphics methods. These methods include a machine learning algorithm, time series, linear regression, statistical inferences and many more.

R programming is a tool used for statistical analysis and creates publication-quality data visualization. Today, R is one of the popular languages used by many industry giants. The following series of step-like do data analysis using R; program, transform, discover, model and communicate the results.

Those who are certified in R programming are R programmers. R language is a free platform can be used on any operating system. You can install R for free and use it without ever purchasing its license.

**Why are R Programmers in a Great Demand for Employers?**

Let’s now see why R programming is so important to learn. As technology is getting flooded with data eruption, it has taken the industries by storm. The future generation technologies paved their way for this highly digital world to make devices smarter with information.

It is not possible without the data science technologies. R programming is ranked top in the most preferred languages in the past few years. The reasons why programmers prefer this language over others are:

**More job opportunities: With the constant hype in data science, more job opportunities come every day to provide a chance to data analysts to climb up their career to a next level. As R is a popular programming language to learn will help you to rise in your job.**

- Attractive salary: According to the survey conducted, R is ranked as the top paid skill.
- Important for Data Science: There are three primary reasons; let’s understand:

**Run your code without using the compiler –**R is an understood language. Therefore run Code without a compiler. R will interpret and develop the code quickly.**Calculation with vectors –**A vector language is R so that any function can be added to the vector.**Statistical Language –**R is a complete turning language where any task can be performed.

**Trend: As per a famous ranking system, R is climbing the chart of popularity steadily since 2008. Hence R programmers are in high demand by the companies.**

**Why is R essential for Industries?**

- As said earlier, it is a free and open source that offer great visualization. Researchers say that there are far capabilities than other tools.
- For a data-driven industry, R programming can be used as their platform and recruit trained R programmers.

**Roles and Responsibilities**

**Full support and documentation: The online resources of R include well supported and documented message boards. Many R developers participate in this online discussion of the packages and tools designed. These packages help in creating Random-effects regression type of models.**

- More appealing to employers: R is an inherently valuable and useful skill. It is helpful for any industry that relies on data analysis. The price of learning statistical packages is very high at the enterprise level. Due to that employer hires people who know R and save thousands without purchasing statistic packages.

- Analyze, acquire and clean data in one place: Using the R, you can do data acquisition, analysis and cleaning at one home.

- Use data visualization tool: You can take advantage of the excellent data visualization tool of R.

- Speed up: You can learn R easily and lots of communities, even companies that offer high-quality courses online.

**The Latest Trend!**

R has become an extensive favorite tool in the past few decades. This language is used for data analysis in the top companies in the world. Certain organizations are there who monitor regularly and publish reports of ongoing trends in this data science world. According to the survey of 2015, there is a 40% rise in the requirement of R analytic professionals.

R is a language that booms along with other languages like Python, Hadoop. R is used by about 2 million people now. In this programming career, there are artificial intelligence and machine learning. In business intelligence also involves advantages of the R. A proper amount of mathematical knowledge, programming experience and the ability of financial analysis are needed to pursue this path.

If you think of a career in data science, 2018 offers a plethora of profitable opportunities. A career in R Programming can lead you to become the following:

- R programmer
- Data Scientist
- Data Analyst
- Data Visualization Analyst
- Data Analyst
- Database Administrator

**Who can become an R Programmer?**

R programming is more suitable for those who have an interest in machine learning, statistical analysis, and data mining. It is not recommended for any general programmer who aspires to become a data scientist.

**Skills Require**

**Programming Languages**

A data scientist has to be excellent in any one of the languages like R, SAS, Python, Hadoop, etc. It is not only about writing code instead needed to be comfortable with using various programming environments for analyzing data. In the data science field, unprecedented value and interest in the business is given around the world.

**Understanding the statistics**

Probability, Hypothesis Testing, Inferential and Descriptive statistics are the necessary things to learn in data science. An intuitive understanding is needed to interpret the statistical output of a business.

**Machine learning**

Machines do the best job when it comes to categorizing and computing large unstructured data. This cannot be done alone by them, but they can identify the trends or patterns that are not clear to a data scientist. They have to be supervised, so you must have these skills to help computers learning from data to derive insights and bring a practical solution.

**Visualization Skills**

Knowledge in data visualization tools like QlikView, Tableau, Sisense or Plotly ensures that you are confident to present insight into technical as well as non-technical audiences to convince them for business values insight can be drawn.

**Communication**

R programmer must be the best communicator. They must work with lots of professionals and stakeholders to solve real-life problems. Also, they must understand the data and domain for which their works.

**Recommended Courses for R Programming!**

R programming is primarily a course that explains the concepts and brings the learner into facing real-life tasks and problems. Some of the R programming courses offered by top rated online sources like Simplilearn, Udemy, Coursera, etc. are:

**Data Science with R programming by Simplilearn:**

**R programming by Johns Hopkins University**

**
**

**The R Programming Environment - How to Setup Environment to write and test your R-Programming Code**

**
**

**R Programming A-Z: R For Data Science With Real Exercise**

**
**

**Data Science and Machine Learning Boot camp with R**

**
**

To become an R programmer, good command over the programming languages and your ability to adopt the changes in technology is the key to success. An R programmer is beneficial for today’s world due to its growing importance in different industries.

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central