Have you heard of R programming?
If you work in the data science field, you may be familiar with the term. If you’re thinking about becoming a data scientist, R programming may be new to you.
However, if it is, don’t feel bad. Outside of academia and the data science field, R is not well known.
Even within the field, many programmers prefer to learn alternatives to R programming language like Python.
So, why use R in data science?
What Is R Programming?
Knowing the history of the R programming language makes it easier to understand how R is both a programming language AND an environment.
In the early 1990s, two professors from the University of Auckland, New Zealand needed a robust statistical computing solution that would operate in a MAC environment.
The R programming language was invented to fill that need.
By 1995, Robert Gentleman and Ross Ihaka had developed the R programming language, which was designed for statistical computing.
R is most closely related to the S language, which was developed by a team from Bell Labs.
Headed by John Chambers of Bell Labs (AT&T), the team created the S language for statistical computing in a proprietary environment.
R is considered a modern implementation of the S programming language.
When R was released as part of the GNU project, an open-source community developed around R, which resulted in a set of software tools for data manipulation, calculation and graphical display.
The environment includes data handling, operators for calculations, and a set of tools for data analysis.
The ability to display data analysis in graphical form is built into the environment. It even allows programmers to create functions in R, C or C++ to add to the environment.
Why Use R in Data Science?
R is often tied to statistics and statistical computing, but it has a myriad of applications in the field of data science.
Because R is both an environment and a language, it has the flexibility to add functionality or to deliver results quickly using pre-existing tools.
The following are just a few of the areas in which R can be used in the field of data science:
- Descriptive statistics
- Exploratory data analysis
- Analysis of probability distributions
- Predictive modeling
Using various tools, use R for validity testing, web applications, linear regression, and organizing and pre-processing of data.
R has an extensive library of visualization tools that make it easy to display data in graphical form. The plotting packages make it easy to display data without extensive coding.
Another advantage is the ongoing contributions of the R community. Any new statistical methods are added to the R libraries, giving it an edge when it comes to statistical analysis.
All of these factors make R a valuable tool for data scientists. It is why R programming is vital in Big Data.
How Do I Learn R Programming?
As with any programming language, learn R by using it. Introductory courses present the basics of R programming, such as syntax requirements, data structures, and variables, but they rarely provide coding opportunities.
It isn’t until the basic information is put into use that learning begins. In the beginning, R did not have a user interface, which made it more challenging to learn.
Today, tools such as R Studio exist to provide a graphical user interface (GUI) for programming. These tools make it much easier to learn R; however, the command-prompt version is still available.
Depending on whom you ask, R is easy or hard to learn. If you are familiar with C, C++, or Fortran, R uses some of the same concepts, making it easier to understand.
If you are not familiar with C, C++, or Fortran languages, you may want to consider an online data science program such as the one powered by Woz-U to help you navigate programming conventions.
Although R is an interpreted language, coding errors will still prevent it from executing. It is essential that R code be error-free. Indeed, compile it to run faster in real-time.
What Careers Use R Programming?
R programming can open the door to unlimited opportunities. What industry doesn’t need extensive data analysis?
- Finance. Use time-series analysis averages and autoregression to assess credit risk, predict stock-market movement, and manage investment portfolios.
- Healthcare. For more advanced processing, such as drug discovery, use R for preclinical trials and drug-safety analysis. R provides a set of exploratory data analysis and visualization tools to help explain results and trends to decision-makers.
- Manufacturing. Companies use time series modeling and statistical analysis to help make data-driven decisions.
- Technology. Tech firms use R to create predictive models for advertising placement, consumer recommendations, and social media trends.
As machine learning becomes more prominent across all industries, R programming skills will only become more valuable.
According to Indeed, as of April 2020, R programmers potential salary earnings are between $124,000 and $71,000 per year. The range reflects differences in location, experience, and industry.
R programmers not only provide in-demand skills, but they also offer employers cost savings. Because R is open-source, companies do not have to pay licensing fees for proprietary statistical packages, which can be costly at an enterprise level.
Finding Purpose and Meaning
If you want the freedom to find meaningful work while using your computing skills, learning the R programming language may be the perfect career choice.
R is fast becoming the most talked-about language in the data science field. It currently ranks tenth in the TIOBE Index of programming languages.
Why not take the first step towards a purposeful and meaningful career and sign up for the data science program powered by Woz U.