From the course: Python vs. R for Data Science
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Using R
- If you're considering working in R, you first need to know what makes R unique and what it has to offer. I'm going to walk you through the key elements of using R for working with data. The first thing to note is that R's syntax and structure can take some time to get the hang of, as it's a bit different from other languages. To name a couple of the differences, while Python uses parentheses for opening and closing function definitions and loops, R uses curly brackets for the same purpose. And while Python's native data structure for ordered mutable sequences is a list, R's corresponding data structure is a vector. Now, R is a powerful language for statistical modeling and analysis. It has several libraries that contain functions for building different types of models, such as linear regression models. An important application of R's tools is performing regression to address causal questions. That's a helpful…
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