R was created in 1991 by Ross Ihaka and Robert Gentleman as an extension of the S programming language. It was made public in 1993 then available as free and open-source in 1995. Its’ strengths are in statistical computing as well as graphing.
Techniques such as linear and nonlinear modeling, graphical plots, statistical tests, clustering, and classification are all possible with the language and environment set up by R. R works by allowing data to be input and managed. This makes the language the most popular in the field of data science. Distributed computing is a platform in R that allows it to work with separate processing areas to make processing the inputs and commands more efficient. Packages are organized groups of code that are a collection of functions. They allow the features of R to be extended. While some packages are present when the language is installed, other packages can be found through groups and other resources in the community. These packages allow time to be saved when coding because of functions that are already written. The extensive amount of packages for machine learning also make R more desirable for programmers. In machine learning, it is often necessary to create algorithms when making predictions. This programming language lends itself to creating algorithms that are automated for machine learning. While academia traditionally used R, with the increased use of statistics in multiple industries using technology and web applications, the language has spread into other sectors.
So why learn R? Aside from being free and open-source and the number one language for statistics and data science, it has other benefits. Learning R can prepare a coder for lucrative careers and the ability to use cutting-edge technology. Almost every industry now has a need for R as analyzing statistics has become integral in every field. There is a large community of users and many user groups that provide support and a forum for collaboration.
TIOBE index measures the popularity of programming languages by considering the number of lines of code written in each language. TIOBE ranked R the 13th most popular programming language and it continues to gain popularity. While it is the most popular coding language in statistics and data science, it is transcending into other industries as well. All industries can use R’s capabilities as they collect information, analyze findings, identify trends, and work to make predictions for their individual companies.
With libraries including ggplot2 and plotly, R is able to create data visualizations that other programming languages can’t emulate. Representations are high quality Scatterplots, histograms, bar charts, correlograms, and even heat maps can be created with this language. These models for presenting data are essential for data scientists and anyone looking to compare data sets of any size in a visual way.
Social Media, IT, finance, government, healthcare, ecommerce, manufacturing, and academica all find uses for the capabilities of this programming language.
It is easy to understand how this programming language has many real-life applications and is used in a variety of companies. Facebook uses it to update statuses, while Google uses it to make predictions about the economy and the return on investment of its advertisements. Trulia utilizes R to help predict future home prices and well as other analysis points like crime rates. The Food and Drug Administration is able to predict drug and food interactions in clinical trials using their data and this programming language. The healthcare industry as a whole can use R’s capabilities to do everything from predicting the spread of a disease to analyzing drug trials and genetic sequencing. Microsoft, The National Weather Service, and Twitter even have uses for this language as a statistical aid.
One package, rmarkdown provides the ability for R programmers to produce both Word and PowerPoint documents that can easily be reproduced by only changing one line.
The dbplyr package allows for data to be transferred from a directory to R language for visualization. Once linked, queries can easily be performed without actually downloading all of the information.
In order to learn this language on your own, first download a compiler that is compatible with your operating system. Next, search for tutorials, introductory online courses, and online programs to help you learn. Some are more structured than others so you can choose the level of assistance that is right for you. There are also reputable books that you can look to for step-by-step guides to learning the language.
If you would like a more structured and in-depth program, a bootcamp may be right for you. Data science and data analytics bootcamps are the most likely to teach R as part of their curriculums but be sure to double check what is covered before signing up. Bootcamps are offered in a variety of settings including in-person, online, and in hybrid combinations. They offer comprehensive curriculums and supports to prepare you to enter the coding world.