Whether you're an aspiring developer or an established one looking for a change, learning a programming language is a daunting task.

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You'll need to choose the right one for the job at hand and make sure its use will continue to grow.

With an increase in demand for developers with statistical and data science backgrounds, learning R or Python is a great step to build your career.

The average salary of a UK R developer has increased by almost nine percent since this time last year, and with Python developers earning between £32,500 and £88,750 in the UK, both languages would be an asset for you and your wallet.

But statistical and data analysis skills shouldn't just be limited to developers.

There are lots of non-traditional roles that are branching out, not only into analytics but also database and statistical programming.

If you are in a role which relies on data, whether that be publishing, accounting or marketing, you could benefit from learning with R, Python or other data analysis programming languages.

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Read on to find out if you should learn R or Python.

What is R programming language?

R is an open source programming language and software environment created in 1995 by Ross Ihaka and Robert Gentleman.

R has potential for machine learning, as its data generation and analysis capabilities are outstanding, with command-line scripting built for storing lots of series of complex data-analyses and re-using that analysis on similar sets of data.

Pros of R programming language

R has seen a resurgence in recent years with more businesses opting for data structures built on R.

Evidence of this can be seen at some of the biggest tech giants in the world.

According to research from Revolution Analytics, some Facebook employees are using R to analyse user behaviour, and more than 500 Google employees are using R analytics to enhance its advertising efforts.

R has around two million users and an active online community. This is great for people starting out as lots of information and help is available in just a few clicks.

R is free and open source, so unlike its rivals such as SAS or Matlab, you can customise, cloned and even redistribute it. 

And R's open source nature means that upgrades to its software come much faster and are based on everyday users problems. This is particularly good for statistical languages.

To get started with R, you don't need any additional downloads, but it is a good idea to install RStudio; a free R integrated development environment (IDE).

This studio includes useful features to make the learning process a little easier. 

R is very adaptable, able to import datasets from most major services such as Oracle, MySQL and Microsoft Excel.

It's also available on Windows, Linux and Mac OS X, so should be readily available for most.

Cons of R programming language

With R, the syntax can be quite slow and differs greatly from Python, so if you're used to a quick language, R might take some time to get used to.

R also suffers in the memory management department, although as the community grows larger, it will improve.

While R does lend itself well to machine learning, a lot of people would prefer to use Python. This is because of Python's scikit-learn library, a sleek tool designed to enable developers to reuse code between projects.

What is Python programming language?

Founded in 1991 by developer Guido Van Rossum, Python is an interpreted and general-purpose dynamic programming language.

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Python is used in software development, web frameworks, applications, prototyping and graphic design applications.

Pros of Python programming language

Before you begin any form of data analysis, you'll want to clean up the data and Python does the job brilliantly.

Because Python is a full-service, imperative language it lets programmers add new functions and layers, meaning you can separate your data and clean it. 

Python has an impressive and simply massive amount of libraries. Probably due to its popularity, Python has a huge number of packages and online code from lots of online developer communities such as GitHub.

Python is constantly growing and improving with a strong community offering advice and support.

As Python is a general-purpose language it can handle large data sets and statistical tasks, while also being able to perform everything that a normal programming language can.

Python is also free and open source, so benefits from regular updates, like R.

Like R, Python also runs on all major operating systems such as Microsoft Windows, Linux, and Mac OS X.

Cons of Python programming language

While Python appears in the web and desktop servers, it does fail slightly in mobile development.

As Python is a simple language, it is relatively easy to pick up, which is a blessing and a curse. Once you have got used to its extensive libraries and frameworks, learning a new language could prove tricky.

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Python doesn't really have an equivalent to R's RStudio which could be a drawback for some. Although Python has Eclipse and Visual Studio, RStudio seems much more consistent.


You could easily bypass the decision and opt for both, using Python for the first stage of data aggregation and then feed the data into R, to test it.

But if you are going to choose one over the other, you might want to base your decision on what your colleagues (if you have them) are using. That way you can share resources and help each other out.

Python might be the right choice for you if you want to pick up a programming language quickly, while R - often considered a quirky language - might be best suited for more advanced developers.

Python is a more widely used and might be better if you want a statistical language that is also made for general-purpose programming.