Data Analytics
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Python or R? Five Things to Ask When Choosing a Data Analysis Language

If you’re entering the world of data analysis, or moving beyond spreadsheets into bigger data and more complex statistical work, you’ve probably heard a lot about the various software packages available to choose from. You’re probably also wondering which one is right for you and your business.

Two names that you’ve almost certainly heard of are Python and R. It’s possible to use both – many analysts do – as there are plenty of transferrable approaches between the two. However, if you’re starting afresh, focusing on one is usually preferable as you’ll undoubtedly encounter less confusion along the way by specialising. If you’re choosing an approach for a whole team or organisation, it’s even better to find one platform that everybody can work with. Many analysts who work with multiple programs do so because legacy projects require them to switch or multiple clients use different software.

Unlike competitors (SPSS or SAS), both Python and R are free and open source. You can download full versions and get started today, with copious learning materials to help you on your journey. Despite this, both R and Python are powerful and flexible. 

So, how to choose between these two popular and widely-used options? A few factors to consider:

Cost: While both are free and open-source, meaning you shouldn’t have to pay to get started on your analysis, some IDEs (the development or analysis environment you work in) are paid for, with an especially large range of premium options for Python. When beginning analysis projects, there are excellent free options available for both languages, with professional-level IDEs only necessary for more technical, large scale work. Therefore, the cost shouldn’t be much of a differentiator in your decision.

Data analysis: Historically, R was a statistical analysis language while Python was multi-purpose. As a result, R had the best suite of analysis options. A few years ago, if you wanted to be confident and do any type of mathematical work, you would use R. However, with the evolution of Python analysis packages like Pandas, NumPy and SciPy, the gap has decreased. Although R remains stronger for all-round statistical work, for Machine Learning (especially Deep Learning) and neural nets, Python is currently best. On the other hand, some types of visualisation available in R are not yet matched by Python. 

Ease of use: This is the main area where some argue that there is a meaningful difference for those without the specific focuses discussed above. For accessing data and performing automated analysis, or anything routine and repeated frequently, Python is considered the easier platform to use. For deeper, ad hoc analysis, many find R superior. Working with R will feel familiar to those with a background in statistical of spreadsheet-based software. Those with a programming background will likely feel more comfortable with Python, and its popularity for web development makes it the best choice for those who want to develop and deploy models in the same language. Python has a gentler learning curve than R, so beginners and those looking to get up to speed quickly may favour it. 

Community: Both languages have active communities but more people use Python overall. This is reflected in the dominance of Python-related discussions on Q&A sites like Stack Overflow. Until recently, more data analysis specialists used R. However, this distinction has now largely broken down. As Python is a general purpose programming language, if you’re interested in joining a community where you can learn other skills, such as web development, Python is your best bet.


Across industries, much data analysis is done in R because until recently, it was the best free option for serious data work. If you need to work with legacy projects using R, then R remains a great option. That’s also the case if you’re familiar with other statistical analysis or spreadsheet packages and plan to focus solely on data analysis. However, there are now few analyses that can be done in R but not Python. If you need to pull data regularly, are working with APIs, running a lot of frequently repeated tasks, need to automate reporting, or want to access a wider programming community, then Python is the best option. Ultimately, either remains an excellent choice.