Dplyr is easier and cleaner than Pandas. Do you dare to make a switch? I’ve been a Python fanboy for the last couple of years. The language is excellent for data science, and being a general-purpose language makes it that much easier to work on non-data-science parts of data science. But can R’s dplyr beat Python’s pandas in the data exploration and preparation domain? Yes, it can. Today we’ll see how to use 10 of the most common dplyr functions. That’s not all the package has to offer, so refer to this link for a complete list. We’ll perform the entire analysis with the Gapminder dataset, available directly in R. Here’s how to import […]

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