GRASP
Exploring R and Python for data analysis
August 5th, 2026, by
When it comes to analysing quantitative data, there are a range of tools available - from statistical software such as SPSS and Stata, to programming languages such as R and Python. Each has its own strengths and limitations, and the best choice will depend on your research questions, data and personal preferences. You may already have a favourite, and that’s great! However, if you’re considering your options and would like to learn more about R and Python, including what they are, why researchers use them and how to get started, then this post is for you.
What are R and Python, and why use them?
R and Python are both open-source programming languages, which means they’re free for anyone to download and use, and anyone can contribute to their development and improvement. The main difference between them is that R was developed specifically for statistical analysis and data visualisation, while Python is a more general-purpose programming language that is widely used in data science, machine learning and software development. Despite these differences, both are widely used by researchers to clean, analyse and visualise data.
In addition to the benefits that come from being open-source, the code-based approach of R and Python offers several advantages – including improved reproducibility, the ability to automate repetitive tasks, and the increased flexibility of being able to customise analyses and workflows to suit specific research needs. They both also have large communities that develop packages to extend their capabilities, and therefore have a wide range of statistical methods, visualisations and data types available.
Where to start learning R and Python
While all of this might sound appealing, the idea of learning R or Python can still feel overwhelming - particularly if you have little or no programming experience. One option for getting started is The Carpentries, a global organisation that develops lessons and supports workshops in coding, data science and research computing. Their lessons are designed for beginners and focus on practical skills that researchers can apply in their own work.
The Carpentries offers two main lesson pathways that you might find helpful as an HDR student – Data Carpentry and Software Carpentry. Which one you start with will depend on your current experience and what you hope to achieve, but in general, Software Carpentry is probably the best choice if you are new to programming and want to develop foundational skills, as it introduces key concepts and tools that support reproducible research. Data Carpentry, on the other hand, offers domain-specific lessons, so is likely a better fit if you are looking to develop your skills in the context of your field.
Finally, note that the Library also provides support for HDR students wanting to explore R and Python through our online guides and workshops. Taking a similar practical approach to The Carpentries, these resources provide an introduction to using R and Python by demonstrating how they can be used for a range of real-world research tasks, from accessing and cleaning data through to analysis and visualisation.
Resources to help you get started
If you are interested in exploring R and Python further, you might find the following resources helpful:
- The Carpentries: explore Data Carpentry and Software Carpentry lessons to build skills in data analysis, programming and reproducible research. Note that the Curtin Institute for Data Science runs selected lessons as workshops twice a year, with information shared on their LinkedIn page and via their mailing list.
- Data Literacy with R: the Library’s introduction to using R for data analysis and visualisation, with a focus on practical research workflows. Includes a link to find out about upcoming workshops.
- Data Literacy with Python: the Library’s introduction to using Python for data analysis and visualisation, with a focus on practical research workflows. Includes a link to find out about upcoming workshops.
Please make any anonymous comments/ feedback, or suggestions for further posts at this link. If you would like to get in touch or write a post for the Ideas Hub blog, please email karen.miller@curtin.edu.au. Contributions from HDR students are welcome!
Image by BoliviaInteligente on Unsplash.