Top 10 dplyr Functions — Data Analysis Made Easy

[This article was first published on R – Better Data Science, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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 […]

The post Top 10 dplyr Functions — Data Analysis Made Easy appeared first on Better Data Science.

To leave a comment for the author, please follow the link and comment on their blog: R – Better Data Science.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)