Monthly Archive: March 2017
Lifecycle badges
Package lifecycles & APIs This page describes the typical lifecycle on an R package. Knowing where a package is in its lifecycle is particularly important for understanding how the API...continue reading.
Learn the tidyverse
.hideOnMobile .sectionTitle { display: none; } R for data science The best place to start learning the tidyverse is R for Data Science (R4DS for short), an O’Reilly book written...continue reading.
Learn the tidyverse
See how the tidyverse makes data science faster, easier and more fun with “R for Data Science”. Read it online, buy the book or try another resource from the community.continue reading.
Get help!
In space, no one can hear you scream. – Alien (1979) Luckily the tidyverse is a friendlier place. Ease of adoption and ease of use are fundamental design principles for...continue reading.
Contribute to the tidyverse
The tidyverse would not be possible without the contributions of the R community. No matter your current skills, it’s possible to contribute back to the tidyverse. Answer questions The easiest...continue reading.
Posts on Tidyverse 2017-03-12 22:00:00
There will often be a paragraph of text right here. HTML Lists This is a bullet list This bullet contains some code library(tidyverse) tibble(x = runif(100)) You need to perform...continue reading.
tibble 1.4.1
We’re excited to announce version 1.4.1 of the tibble package. Tibbles are a modern reimagining of the data frame, keeping what time has shown to be effective, and throwing out...continue reading.
dbplyr 1.2.0
We are very excited to announce that dbplyr 1.2.0 is now available on CRAN! dbplyr is the database backend for dplyr. It interacts with databases directly by translating dplyr code...continue reading.
rstudio::conf 2018
We had a great time at the 2019 rstudio::conf! Many attendees arrived early for 2 days of in-depth workshops, followed by two days of conference sessions. This year’s conference featured...continue reading.
fs 1.0.0
fs 1.0.0 is now available on CRAN! fs provides a cross-platform, uniform interface to file system operations. fs uses libuv under the hood, which gives a rock solid cross-platform interface...continue reading.
tibble 1.4.2
We’re excited to announce version 1.4.2 of the tibble package, a minor update focusing mostly on display and performance. This is a small release designed to address the biggest issues...continue reading.
haven 2.0.0
We’re pleased to announce that haven 2.0.0 is now on CRAN. haven enables R to read and write various data formats used by other statistical packages by wrapping the ReadStat...continue reading.
vdiffr 0.3.0
We’re thrilled to announce that vdiffr 0.3.0 is now on CRAN! vdiffr is a testthat extension that makes it easy to add visual unit tests for R plots. Testing visualisations...continue reading.
dbplyr 1.3.0
We’re stoked to announce that dbplyr 1.3.0 is now available on CRAN. dbplyr is the database backend for dplyr, translating dplyr syntax into SQL. This is a minor release primarily...continue reading.
rlang 0.3.1
The patch release 0.3.1 of rlang is now on CRAN! This release polishes the rlang backtraces introduced in 0.3.0. See the NEWS for the complete set of changes. The main...continue reading.
tibble 2.0.1
PRE.fansi SPAN {padding-top: .25em; padding-bottom: .25em}; I’m pleased to announce that version 2.0.1 of the tibble package is on CRAN now, just in time for rstudio::conf(). Tibbles are a modern...continue reading.
rstudio::conf 2019
This article is originally published at https://www.tidyverse.org/blog/ Thanks for visiting r-craft.org This article is originally published at https://www.tidyverse.org/blog/ Please visit source website for post related comments.continue reading.
rstudio::conf 2020
This article is originally published at https://www.tidyverse.org/blog/ Thanks for visiting r-craft.org This article is originally published at https://www.tidyverse.org/blog/ Please visit source website for post related comments.continue reading.
Modeling Generalized Poisson Regression in SAS
The Generalized Poisson (GP) regression is a very flexible statistical model for count outcomes in that it can accommodate both over-dispersion and under-dispersion, which makes it a very practical modeling...continue reading.