About mxlabs
A platform for practising analytical coding in R.
What is mxlabs?
mxlabs is a platform to practice coding for data processing and analysis. It has 101 problems that combine coding and statistical theory.
The problems range from sorting, merging, and cleaning data to modern causal inference techniques.
mxlabs is a work in progress — the range of topics and difficulty of problems will expand over time.
How it works
mxlabs uses R, one of the most widely used languages for data analysis. To focus on core principles, use of pre-packaged functions is limited.
For example, to calculate a mean you will have to know the dimensions of a dataset and how to sum its columns.
Problems are tagged by difficulty and topic. You can filter for both, and run your code before submitting to check it works. You'll also see how your solution compared with others who've taken the same problem.
AI feedback
You can request AI feedback. If you get an error, the review can explain what went wrong and why. If your answer is incorrect, it can identify where your logic broke down. If your answer is correct, it can suggest how your code could be improved.
AI review is available as three free calls per day. You will need to provide your email to use it so we can track usage. See the Privacy & AI page for details.
Why mxlabs?
mxlabs plugs three gaps in the world of data analytics.
There is little opportunity to practice analytical coding in a way that builds methodological understanding. These days there are pre-packaged functions for almost everything. That's great, but when you're learning or building a new skill, it can prevent you from deepening your understanding — even in universities.
This is being made worse by rapid advances in AI coding. The productivity gains are real, but when you combine data analysis and statistical methods to make decisions or drive the direction of a business, applying the wrong method — or the right method incorrectly — can have serious consequences.
Assessing analytical skills is hard without seeing them in practice. For analysts, knowing where your gaps are or how you compare with others is useful. For teachers and hiring managers, that information is equally valuable.