The R programming language, usually referred to as “R”, is free open-sourced language utilized as an environment for statistical graphics aid and computing, data analytics, and scientific research.
There are various statistical methods and graphical techniques such as clustering, statistical hypothesis, data mining, regression modeling, and data warehousing. These are utilized by the statisticians, data analysts, researchers and marketers, and data miners to retrieve, clean, analyze, and visualize and present data. In the event of managing large-scaled polls, examining databases of literature and languages, and conducting user surveys, the demand for R language is rapidly rising for the past few years, and shows no sign of stopping anytime soon!
R programming language is simple to learn and readily available and utilized in various programs. RStudio integrated development environment (IDE) is a powerful tool for programming with R with all the code, results, and visualizations together in one place. With RStudio Cloud, programming can even be done using web browsers. This expressive syntax and easy-to-use interface greatly contributes to its growing popularity.
There are all kinds of data scientists who work on a wide variety of problems and projects. Interested in analyzing language? Predicting the stock market? Digging deep into Super Bowl statistics? There's always a place for you in the data world, and your journey starts by learning R!
Pick an area you’re interested in. Be it :
· Data Science
· Data Analysis
· Data Visualization
· Statistics
· Dashboard Reports
· Predictive Modeling using Machine Learning
Here are some interesting project ideas for some of the aforementioned topic areas :
Predictive Modeling using Machine Learning
· An algorithm that forecasts weather where you live.
· A tool that predicts stock market trends.
· An algorithm that automatically summarizes news articles.
Statistics
· A model that predicts the cost of Grab and Uber trips near your area.
Dashboard reports
· A map of the live locations of buses near your area.
· A stock market summary.
· A Covid-19 tracker, much like this one.
· A summary of your personal spending habits.
Applications of R Programming in the Real World
1. Data Science Harvard Business Review named data scientist as the “sexiest job of the 21st century”. With the advent of IoT devices generating a multitude of data that can be utilised to inform the decision making process, data science is a field that can go no other way but up. Programming languages like R give a data scientist superpowers! Collect real-time data, perform statistical and predictive analysis, create visualizations, and communicate actionable results to stakeholders is much like seeing into what the future holds.
2. Statistical Computing R is one of the most popular programming language among statisticians. In fact, it was initially built by statisticians for statisticians! With a rich package repository of over 9100 packages that encapsulates almost every statistical function you can imagine, R’s expressive syntax allows researchers - even those hailing from non-technical backgrounds - to quickly and effectively import, clean, and analyze their data from various data sources. The best part is its charting capabilities which allow data plotting of interesting visualisations.
3. Machine Learning R is plenty useful in predictive analytics and machine learning. It's various packages for common ML tasks include linear and non-linear regression, decision trees, linear and non-linear classifications, and many others, all ready to be used in fields from finance and business, to biological and genetics research, to retail and marketing.
Why use R for statistical computing and graphics?
1. R is free and open-source! R and most R packages are licensed under the terms of the GNU General Public License. Therefore, they are free to download even in commercial applications without having to call your lawyer.
2. R is popular – and still increasing in popularity Every year, IEEE publishes a list of the most popular programming languages, and R was ranked 6th in 2021. This is a big deal for a domain-specific language to be almost if not more popular than general-purpose languages like C# and JavaScript. Clearly, there is a growing interest in R programming language and its related fields of Data Science and Machine Learning.
3. R runs on all platforms Distributions of R are available for all popular platforms – Windows, Linux and Mac. The R code can easily be ported between platforms without major issues. This cross-platform interoperability is an important feature in today’s rapidly expanding computing world to reap all the benefits of technologies that are able to run on all systems.
4. Learning R will open doors to much more career and job opportunities According to the salary surveys, data scientists are paid a median salary of close to USD$100k worldwide. Being a good R programmer who has experience in juggling various working tools will definitely allow you to stand out from the crowd, even if you do not plan to be a full-fledged data scientist!
5. R is being used by tech giants Data science is a fast-growing field, and its adoption by tech giants is always a sign of a programming language’s potential, as today’s companies will make sure to back up major decisions by concrete analysis of data. Especially top tech firms like Facebook, Google, and Microsoft, who are all in the mix of R language supporters. Since R has the right mix of simplicity and power, businesses and companies across the globe in virtually every industry that requires analytics are widely using it to make calculated decisions.
Now, let me clear your doubts that are making you hesitant on learning R.
FAQ :
Is it challenging to learn R?
Learning R can certainly be challenging with its fair share of frustrating moments. It is so very important to stay motivated and keep learning!
Taking baby steps and working on projects that are you are genuinely interested in can help you power through a whole lot of frustrating moments. As an added encouragement, many researchers learnt R as their first language to help in solving their need for research data analysis. That’s the power of the R programming - you can simply learn as you go! All you really need is data and a clear intent to draw conclusions by analysing that data.
Fun fact - R is built on top of S programming language that was originally intended to help students learn programming while playing around with data.
On the other hand, programmers that have a background in Python, PHP, or Java language may find R and its syntax quirky and confusing at first. But after some time, they too are attracted by the usefulness of R! Even with all the capabilities of a programming language, you probably won't find yourself writing if conditions or loops in your code, but rather taking advantage of other programming constructs such as vectors, lists, frames, data tables, and matrices that will achieve the same purpose and allow bulk transformations to be performed on your data.
Where to learn R for free?
Psst! There are lots of free R learning resources out there on the Internet! I'll be doing a review of popular free learning platforms soon, so do subscribe to Compidia and stay tuned!
However, chances are that you will need to patch together different free resources, not to mention spending extra time researching and building your own curriculum.
In contrast, platforms that require monetary investments may offer better structured teaching methods, especially interactive, in-browser coding that is common for programming modules!
Is it possible to learn R from scratch (with no coding experience whatsoever)?
Definitely! Many R learners start off with little to no coding experience, and actually go on to secure jobs as data analysts, data scientists, and data engineers! So, R is surely a beginner friendly language.
Nowadays, thanks to the powerful tools from tidyverse collection of packages, accessing, cleaning, manipulating, analyzing, and visualizing data with R is easier than ever.
How long does it take to learn R?
Learning a programming language is much like learning a human natural language - you're never really done! Programming languages constantly evolve and there's always more to learn. However, you can get to a point of being able to write simple - but functional - R code pretty quickly.
To reach the job-ready stage, it really depends on your goals, the job you're aiming for, and the amount of time you can dedicate to serious studying and learning. Just to give some rough context, learners surveyed in 2020 reported reaching their learning goals in less than a year - a good portion even less than six months - and with less than ten hours of weekly studies on average.
Is R a good language to learn in 2021?
Again, definitely! R is popular and flexible, and it has professional use in a wide variety of contexts, from academia to finance and business. Even if you do not aspire to work as a full-fledged data scientist or analyst, R data skills can still be really useful. Take spreadsheets for instance - if you've work with them before, you'll know what I'm going on about! There are many things you could be doing better and faster with even a little smudge of R knowledge.
Which to learn first - base R or tidyverse?
This is actually a popular debate topic in the R community. There are a multitude of tidyverse big fans as it is powerful, intuitive, and fun to use. But in order to gain a complete understanding of its tools, you'll definitely need to understand base R syntax, mostly to understand R data types.
The most effective recommendation is to learn a mix of base R and tidyverse methods, preferably during your introductory learning stage. Build a strong foundation, and you'll be set!
Getting help in R
The lively online community - RStudio Community - is all about getting help, giving help, and receiving help! It is also knows as one of the friendliest and most inclusive out of all the programming communities out there.
You can also take advantage of the help() function to know more on specific topics. Use the ? operator for this.
> help(Syntax)
> ?SyntaxThe help.search() function allows a search engine type of search. Use the ?? operator for this.
> help.search("histograms")
> ??"histograms"So, what are you waiting for? Learn R right here and right now!
Check out my how-to articles on Installing R and RStudio application or Getting Them in Anaconda in 5 Simple Steps!
Comments