You’re neither a science guru nor a math wizard, but you want to board the data science rocket, which is hot. You’ve heard that R is the programming language to learn if one wants to master the statistical computing required to be a data scientist. Since you’re both scientifically and mathematically challenged, should you dare learn it?
R is easy to learn, despite the impression that it’s too complex for a non-specialist. Its syntax is simple and intuitive, and it is a flexible, interpreted language – not a compiled one. R directly executes all typed commands without needing to build a complete program, like in most commonly used programming languages.
If your conviction to be a data scientist outweighs your fear, keep on reading. You’ll find that once you get past the “But I can’t program!” nonsense, you may find that you do enjoy programming—and that R is the way to go.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is R?
R is a language, software, and environment used for statistical computing and graphics production in data science. Its creators are Ross Ihaka and Robert Gentleman. It is a dialect of the S language developed by John Chambers et al. at the AT&T Bell Laboratories (now Lucent Technologies).
S comes in the form of S-PLUS, a software program by TIBCO Software Inc. R is a different implementation of S, but a lot of codes written for S remain unchanged under R.
Thanks to the terms of the Free Software Foundation’s GNU General Public License, R is free for everyone. Statisticians, known as the R Development Core Team, develop and distribute R.
It is available in several forms: the sources (written mainly in C and some routines in Fortran), those intended for Unix and Linux machines, and some pre-compiled binaries for Macintosh, Linux, and Windows.
How to Get a Copy of R?
Download your free copy of R from CRAN’s (Comprehensive R Archive Network) website. The folks at CRAN work extensively on the development of the R language and environment.
Their site provides the files needed to install R. Select the build for your operating system. For Windows users, choose the BASE version. For Mac users, load this file: R-3.2.4.pkg from the macOS list.
Instructors from Edureka, an online course provider, suggest also downloading the complimentary R Studio, which makes it easier for novices to program with R and work with it.
It has beneficial elements typical of an application used for coding. Its dashboard has four sections. This makes it more convenient to monitor and manage several windows for caching scripts, issuing commands, summoning past ones, and displaying visualizations, among others.
Attributes of R
- It’s the foremost language for statistical computing and data science.
- It’s a real programming language with a command-line interface for executing codes.
- It has a user base of millions.
- It’s 100% free and open source.
- Its data visualizations are the best in its class.
- There are thousands of R packages (extensions to R).
Components of R
R has many functions for graphics and statistical analyses. Graphics have their own window. Depending on the operating system, users can save them in various formats. The screen displays the statistical analysis results.
Users can save, write in a file, or use in subsequent analyses some intermediate results, such as residuals, P-values, or regression coefficients. For example, R users can program loops to analyze several data sets in succession. They can also combine different statistical functions in one program to perform more complex analyses.
Advantages of R
Listed below are the primary advantages of R programming language in data science:
- R is the leading programming language in statistics and data science. Free Code Camp reported in their introductory R tutorial that it ranks first in a survey of data mining experts. Of these experts, 50% use it more than Python. Data Camp stated in their course offering for R Programming that – in 2012, Oracle estimated R users to be over 2 million worldwide and growing. Every year, this number grows by about 40%.
- One strength of R is that it produces well-designed publication-quality plots, including mathematical symbols and formulas, when needed. Despite the defaults for the minor design choices in graphics already set, the user has full control.
- R compiles and runs on macOS, Windows, various UNIX platforms, and similar systems, including FreeBSD and Linux.
- It’s optimized for vector operations, which means you can go through an entire row or an entire table of data without having to write four loops. People familiar with this process know it is inconvenient and labor-intensive.
- R is expandable with its extensive range of statistical and graphical techniques. These include classical statistical tests, linear and nonlinear modeling, clustering, classification, and time-series analysis.
- More than 9,000 contributed or third-party packages are available free for users to add to R. This makes it possible for them to do virtually anything when working with data.
- Experts prefer the S language for research in statistical methodology, but they favor R as an open-source route to participate in that activity.
- R has its own LaTeX-like documentation format for providing comprehensive documentation. A free software system, LaTeX is the current standard for the production and communication of technical and scientific documentation.
- R’s system is a lot more flexible than traditional software.
- R users benefit from many online programs written for S, most of which can be directly used with R.
- R has a progressive community backing it, so users continually get innovations.
Characteristics of the R Environment
“Environment” means it is a fully organized and logical system and not a gradual accumulation of unadaptable tools like some other data analysis software.
Although many regard R as a statistics system, others view it as an environment where users can implement statistical techniques and easily extend R using packages. Around eight packages are supplied with the R distribution. Many more are available via the CRAN suite of websites that deal with a wide array of modern statistics.
R as a software suite is quite comprehensive, as it is engineered for the calculation and manipulation of data, as well as for displaying graphics. It includes:
- a consolidated compilation of intermediate tools for analyzing data
- a convenient facility for handling and storing data
- a collection of operators for calculating arrays, including matrices
- graphical facilities for data analysis and display (on-screen or hard copy)
- a simple, yet well-developed programming language with user-defined recursive functions, loops, conditionals, and input/output facilities
R, like S, is designed around a true computer language. Users add extra functionality by designating new roles. Since most of its system is written in the R dialect of S, users find it easy to follow the algorithmic choices made.
For tasks that need in-depth computation, users have the option to link Fortran, C, and C++ codes and call these at run time. Advanced users can directly manipulate R objects by writing C code.
Quirks of R
Emmanuel Paradis, the author of the book R for Beginners, assures wannabe R programmers that learning R is doable. He calls attention to one of R’s most prominent features: flexibility.
While a traditional software program displays the analysis results right away, R stores these results in an “object.” R users carry out their analyses without displaying the results. This feature may sound strange, but there is a useful reason for it.
Users extract only the portion of the results of interest to them. If they run a series of 20 regressions, for instance, and they want to compare the different regression coefficients, R displays only the estimated coefficients.
Therefore, the results may take a single line, while traditional software could open 20 windows with results. How will this benefit you? It simplifies tasks and saves time.
Major Companies That Use R
According to David Smith of Revolution Analytics, these are just some of R’s distinguished clients:
- Uber—uses R to anonymously gather data to determine which elements of their service clients utilize most often.
- Facebook—uses R to scrutinize customer habits, such as profile creation and updates on status.
- Twitter—uses R to visualize data and cluster semantics.
- Orbitz—uses R to recommend the best accommodation to its customers.
- Airbnb—uses R to predict client behavior.
- Google—uses R to generate targeted results, like fabricating more influential marketing methods.
Why a Non-Technical Person Can Learn R?
To non-programmers or those not technically inclined, R could seem intimidating at first because its terminologies and rules are different from that of other programming languages.
Consultant John D. Cook, who has professionally written software in several programming languages, did not help by saying R was the most difficult language he learned. But he said this in the context of those wanting to learn R who mastered other languages beforehand. The R language is simple but unorthodox. This doesn’t mean it’s any more difficult than other languages.
It also helps assuage some people’s trepidation if they know that R is similar to some concepts or languages they already know.
How R Is Similar to Windows or DOS
If you’re old enough to know what DOS is, R is similar in that you type in commands at the prompt. These commands tell the system what to do. With DOS, you type a string of tasks for the computer to execute until it delivers your desired results.
For the uninitiated, DOS is an abbreviation for the ‘disk operating system,’ which was originally developed for IBM personal computers.
After you install R on your computer, you start it by launching the corresponding executable. The prompt, by default, ‘>,’ signifies that R is waiting for your commands. As in a Windows environment, you can execute some commands, such as opening files and invoking online help through the pull-down menus by using the program rgui.exe.
Why Learn Data Analytics With R?
A combination training of data analytics and R certifies you in mastering the most popular analytics tool preferred by data scientists. Experts favor R over similar programming languages for its statistical capacity, graphical capability, affordability, and an extensive collection of packages.
Completing data analytics with R will imbue you with the essential skills that can propel you to a stellar career in data science.
Resources for Learning R
Numerous resources, both online and offline, abound for studying R. These are free, discounted, or paid. The paid, expensive ones offer certification upon graduation. Some diploma and degree programs integrate R into their curricula, and some institutions offer employment assistance after graduation. Many offer financial assistance, including scholarship grants.
Articles
These can be offline, such as those published in newspapers, magazines, and trade journals, or online. An example of the latter is Computer World’s Beginner’s Guide to R—An Introduction. There are numerous websites on the internet that can grant you access to hundreds of articles on programming using R.
Books
Aspiring data scientists and potential R students should have a basic understanding of statistics. If you have zero knowledge of stats, read a book on it. We recommend Statistics for Dummies by Deborah J. Rumsey.
Manuals
Teach yourself R by consulting the manuals distributed with R:
- R Language Definition: R-lang.pdf
- Data Import/Export: R-data.pdf
- Writing R Extensions: R-exts.pdf
- Installation and Administration: R-admin.pdf
- An Introduction to R: R-intro.pdf
The files are in different formats, depending on your type of installation.
Blogs
A simple Google search uncovers many blogs dedicated to R. To help you understand the significance of R in data science, see blog entries on websites such as Towards Data Science.
CRAN Site
This hosts documents, bibliographic resources, and links to other sites. There’s also a list of books and articles about R or statistical methods. Some documents and tutorials were written by R users.
R News
This electronic journal fills the gap between electronic discussion lists and traditional scientific publications. The first issue was published in January 2001, and the last issue was published in October 2008. R News is the predecessor of the R Journal.
FAQ Directories
R is distributed with an FAQ (Frequently Asked Questions) localized in this directory. If you haven’t downloaded R yet, see an updated version on CRAN’s website. Just click ‘Download R’ on the website.
Discussion Lists
Subscribe to R’s four discussion lists by sending a message or reading the archives. R’s Development Core Team animates the general discussion list ‘rhelp’. Access this once you’ve downloaded R because it’s a valuable information source for R users.
The three other lists are dedicated to developers. They post announcements of new versions. Many users have sent ‘rhelp’ functions or programs, which can be found in the archives.
Online Tutorials
- Computer World has a PDF on their site that teaches R programming basics. Its title is ‘Learn R for Beginners.’
- Another tutorial is Data Camp’s Quick R offered in a YouTube playlist called ‘Learn R Programming.’
- Are you interested in doing interactive coding exercises and earning a certificate? Data Camp’s free tutorial is a fun way to get started. This interactive course teaches you how to analyze data by controlling elements like factors, frames, lists, matrices, and vectors.
- Free Code Camp’s R tutorial, ‘Learn the Basics of Statistical Computing,’ is a hands-on overview of the language, emphasizing statistical programming.
Certification Courses
Edureka offers certification with their Data Analytics with R course on their website. They also offer YouTube video tutorials, webinars, and masterclasses.
More data science courses are available at websites such as Coursera, DataCamp, etc..
What to Expect From an R Tutorial?
If you’re just testing the waters, you don’t have to pay a ton of money to learn R. Numerous online and offline resources (print publications and e-books) offer free tutorials.
Typical Curriculum
To give you a peek into an online course, these are the modules where you can expect:
- To understand what a variable is. (Just to give you a head start, a variable is a reserved memory location to store values. This means that when you create a variable, you reserve some space in memory.)
- To utilize the control panel as a calculator.
- To discover the data types R supports.
- To generate, label, and choose components from vectors, compare their different types, and use them to analyze results.
- To generate, understand, compute, and use matrices to analyze results.
- To generate, categorize, and differentiate factors because R stores categorical data in them.
- To generate, choose sections from, and order data frames depending on determinants.
- To generate, label, and categorize lists. Unlike vectors, lists store elements of various kinds, like how-to lists consisting of different task classifications.
- To learn other topics, including operators, conditional statements, loops, strings, and functions.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Most people who want to learn R aspire to be data scientists. Some want to delve into the realm of data science in whatever capacity, just to be part of something big because they feel R will pave the way for the future of current developments in technology. They are right. Whichever path you choose to take, we hope this introduction will somehow lead you to it, directly or otherwise.
We wish you a stimulating and exhilarating journey into a world very few brave souls dare to go. May it reward you with much bounty.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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