Why You Should Learn R This Year
Editor’s Note: This post was originally published on Northeastern University’s Level Analytics blog.
Today, our friends at Codementor share their advice on why you should learn R in 2016. Codementor is an on-demand marketplace for software developers. For help with R, Python, SQL, Machine Learning, and more, their experts can assist you.
Every day, a whopping 2.5 exabytes of data is created which is equivalent to 90 years of HD video or 530 million songs worth of data produced everyday! This huge wave of data has driven companies around the world to invest big bucks into tools and technologies to leverage the power of all this data.
As technology improves, the data being collected is also becoming more complex . Companies now have millions of gigabytes of sophisticated data on everything from user preferences to energy consumption. Companies more than ever need people with the skills that can help them extract value from this data.
There’s never been a better time to get into data analytics, but if you’re looking to break into the world of data, then you may be asking yourself: what skills do I need to learn?
In this article, we’re going to look at the benefits of learning one of the most widely-used and talked about technologies for analyzing data — the R programming language.
So, why should you learn R in 2016?
1. It’s one of the most popular tools for data analysis
R is increasingly the lingua franca for cleaning, analyzing and telling a story with data, and there’s plenty of studies and surveys that back this up:
- The monthly TIOBE Index measures the popularity of various programming languages, based on the number of search engine results for queries containing each language. For the past couple of years, R has floated around in TIOBE’s top 20, and came in at number 16 in the most recent May 2016 TIOBE Index.
- For the past couple of years, engineering and applied sciences magazine IEEE has combined data from various sources, including GitHub and CareerBuilder, to create its own list of the top 10 most popular programming languages for that year. In 2015, R came in at number 6, climbing from 9th position in IEEE’s 2014 list. This rise in popularity prompted IEEE to refer to R as 2015’s “big mover.”
- Every year, KDNuggests (a website dedicated to Business Analytics, Big Data, Data Mining, and Data Science) asks its readers what programming or statistics languages they’ve been using for analytics, data mining and/or data science over the previous 12 months. R came out on top in 2011, 2012, 2013, 2014 and 2015.
- Technology book publishers Packt surveyed more than 3,800 data scientists in 2015, as part of their ‘Big Data and BI: Salary and Skills Report.’ When respondents were asked what tools they used on a daily basis, R came in third, with Python scoring the top spot. Even though R didn’t top this particular part of the survey, when Packt asked these Python users what other tools they worked with on a day-to-day basis, R was the most popular answer, suggesting that many data scientists are using Python and R simultaneously.
2. It Commands a High Wage
While getting to grips with a new technology is fun, if your new skill translates into a higher salary, then all the better! So what kind of wages can you look forward to, if you invest some time into learning R?
The 2013–2014 Dice Technology Salary Survey of over 17,000 technology professionals named R as the highest-paying skill, with an average salary expectation of $115,531. By the time the 2016 Dice Technology Salary Survey rolled around, this figure had risen to $126,249. Meanwhile, a recent query of job listings with the R skill by job market analytics specialists Burning Glass Technologies suggests that R programmers can expect to earn in excess of $75,000.
For the past couple of years, technology publishers O’Reilly Media has conducted an annual survey of the various factors that impact thesalaries of data analysts and engineers, with a focus on highly-paid data workers (the median salary of respondents was $91,000, worldwide and $104,000 in the US). In their most recent 2015 Data Science Salary Survey, O’Reilly discovered that R was the 4th most commonly used tool amongst these highly-paid data workers, suggesting that learning R is an excellent way to boost your earning power.
And if you harbor dreams of working for one of the industry giants, then R may help make this dream a reality, as the list of companies who are currently using R includes many household names such as Google, Facebook, Mozilla, Microsoft and Twitter.
3. It has a vibrant community
When you’re debating whether to learn a new technology, it’s always worth taking a look at the people who are using that technology. Generally speaking, the larger and more active the community, the more support you’re likely to get via things like mailing lists, user-contributed documentation, online tutorials, and user groups. These resources are crucial when you’re getting to grips with a new technology, but they’re also important when you’re ready to move onto learning more advanced features.
With a global network of more than 2 million users, R has a vibrant community with lots of online resources that can help you master R. And if you can’t find the information you need via existing resources, you can reach out to the R community directly, for example by posting your problem on Stackoverflow or a dedicated R forum or user group. With millions of R users out there, your cry for help stands a pretty good chance of getting answered!
4. It’s open source
R is free to download and free to use, and as an open source project anyone can access and modify R’s source code. Countless R experts and enthusiasts have already contributed work to the R project, and this “many eyes” approach to R’s development has created a high quality, mature and professional programming language.
Many people have also created their own R extensions and published them online as additional packages, so even if the base R language doesn’t meet your exact needs, chances are there’s an R package out there that will. The best place to find additional packages is the CRAN(“Comprehensive R Archive Network”) repository, which contains over 8,300 packages covering everything from identifying turtles (IDTurtle) to determining solar radiation (solaR).
R’s open source nature also means that it can integrate with many other apps and systems, making it a very versatile addition to your data analysis toolkit.
5. It’s platform independent
With R, you perform data analysis by writing functions and scripts. This may sound daunting, particularly if you don’t have any previous programming experience, but once you’ve mastered R you can often perform data analysis with very few lines of code.
As a programming language, R also promotes experimentation far more than point-and-click software, which may lead you to discoveries you wouldn’t otherwise have made.
If you’re working on a project as part of a team, R’s code-based approach makes it much easier to collaborate, as it’s quick and easy to share your R code with other people. R is also platform independent, so the people you’re sharing your code with don’t even need to be running the same operating system as you.
6. It can generate detailed and varied graphical outputs
Data is easier to understand when it’s represented visually, rather than presented as raw numbers, particularly when you’re dealing with large data sets or you’re communicating your findings to other people.
R has powerful graphical capabilities that allow you to generate high quality graphics such as bar charts, histograms, scatterplots, trees, dynamic graphs, mathematical symbols and even brand new graphics of your own devising, often with very few lines of code.
If you’re working with particularly complex data, or you want to give your graphics some extra pizzazz for things like presentations, then there are plenty of additional packages that extend R’s already-impressive graphical abilities. Some popular R visualization packages you may want to check out include googleVis, ggplot2, and rCharts.
As you can see, R is a powerful and well-established tool in the world of data. Not only is R an in-demand skill that can have a positive impact on your pay packet, but it has a vibrant and active community, which means plenty of resources and additional packages to help you really get the most out of the R programming language.
So, should 2016 be the year you learn R? The most effective way of finding out whether R is right for you, is to try it for yourself! You can download R for free from the CRAN repository.