nflreadr 1.3.1

Fixes CRAN bug and provides some function improvements, most notably improved logic for load_participation()’s pbp join.

New Features

Function Updates

Bugfixes

Thank you to @atungate,@grayhawk40, @guga31bb,@jestarr, @john-b-edwards,@marvin3FF,@mrcaseb, @SCasanova, @shirondru, @tanho63, and @TheMathNinja for their contributions and feedback towards this release!


nflreadr 1.3.0

This release introduces several new data functions, some new utilities, and an array of data/function updates.

New Data!

New Functions!

Function Updates!

Other bugfixes

Thank you to @albtree, @john-b-edwards, @mrcaseb, @pranavrajaram, @tanho63, and @tpenney89 for their contributions and feedback on this release!


nflreadr 1.2.0

This release updates all nflverse URLs to use the new nflverse-data repository releases, as well as provides improved pretty-printing methods that tell you when the data was last updated.


nflreadr 1.1.3

This release adds functions and arguments to access new data, along with some backend changes.

New data and functions

Backend

Thank you to @armstjc, @Grayhawk34, @john-b-edwards, @mrcaseb, @pranavrajaram, @rogers1000, and @tanho63 for their contributions and feedback on this release!


nflreadr 1.1.2

New Functions


nflreadr 1.1.1

New Data and Functions

Bug Fixes

Thank you to @ajreinhard, @brunomioto, @jthomasmock, @mrcaseb, @SCasanova, and @tanho63 for their feedback and contributions to this package!


nflreadr 1.1.0

This release makes some backend changes for speed, reduced dependency footprint, and ease of maintenance.

New Data

Backend Changes


nflreadr 1.0.0

The goal of {nflreadr} is to efficiently load data from nflverse GitHub repositories, and features caching, optional progress updates, and data dictionaries.

At this time, it includes data from the following repositories:

This will hopefully provide a unified and reliable package for downloading nflverse data that can be extended to the rest of the nflverse and ffverse package families.

Special thanks to Seb, Ben, John, Lee, and Thomas for their contributions to the package and to the data pipelines that this package relies on 🎉