This is the fourth blog post in a series of dplyr tutorials. In this tutorial we will summarizing our data: i) counting cases and observations, ii) creating summaries using summarise() and it’s summarise_all(), _if() and _at() variants, and iii) pulling the maximum and minimum row values.

This is the third blog post in a series of dplyr tutorials. In this post, we will cover how to filter your data. Apart from the basics of filtering, it covers some more nifty ways to filter numerical columns with near() and between(), or string columns with regex.

This is a second post in a series of dplyr functions. It covers tools to manipulate your columns to get them the way you want them: this can be the calculation of a new column, changing a column into discrete values or splitting/merging columns.

I went through the entire dplyr documentation for a talk last week about pipes, which resulted in a few “aha!” moments. I discovered and re-discovered a few useful functions, which I wanted to collect in a few blog posts so I can share them with others.

This first post will cover ordering, naming and selecting columns, it covers the basics of selecting columns and more advanced functions like select_all(), select_if() and shortcuts like everything().