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Statistics

R or Python?

One of the first major decisions to make when getting into data analysis is about which language to choose. Python and R are the widely used programming languages in the field of statistics and data science and both are great choices. Below you will find some condensed information about their application for statistics. However, you also might want to have a look at this infographic by Datacamp, as it provides some helpful general guidance about both languages and might be a good starting point to inform yourself.

R for statistics

R was initially used mostly in academia and is nowadays popular among social science scholars, statisticians, engineers, and scientists without strong computer programming skills. It is great for exploratory data analysis and all kinds of statistical tests and models can easily be implemented.

Usability

Advantages

Disadvantages

IDE

Writing code can be a messy task and to get some support on this, programmers rely on some shortcuts and helpful tools to get the work done. An IDE (integrated development environment) can combine several functions and tools like a code editor, syntax highlighting, autocomplete, and debugging to make your life easier. Especially when you are a beginner (and all this stuff just mentioned sounds kinda confusing for you)a must-have one. For R, RStudio is by far the most popular IDE and it’s great, actually!

Ecosystems

Communities

Python + pandas

Python is used by programmers who dealt with data analysis, statistical techniques, or by developers. Python can be used as a single tool that can be integrated with every part of your workflow. Python is really flexible for beginners to build anything that was never built before.

Usability

Advantages

Disadvantages

IDE

Similar to RStudio for R, also python has some IDEs which can support your workflow and are especially useful for beginners. Examples of more popular IDEs for python are jupyter notebooks, spyder, vs code

Ecosystems

Communities