No Clocks

AWS has announced support for container images for their serverless computing platform Lambda. AWS doesn’t provide an R runtime for Lambda, and this was the excuse I needed to finally try to make one. An R runtime means that I can take advantage of AWS Lambda to put my R functions in the cloud. I don’t have to worry about provisioning servers or spinning up containers — the function itself is the star.

Tools for building and working with docker containers in R - GitHub - thebioengineer/dockyard: Tools for building and working with docker containers in R

R configurations for Docker. Contribute to rocker-org/rocker development by creating an account on GitHub.

R configurations for Docker. Contribute to rocker-org/rocker development by creating an account on GitHub.

Table of conversion of dates and time formats in R

Tips to helps debug R packages errors on local installation of different OS

crosswalkr

Wings for your R packages: Streamline the process of versioning R packages and updating NEWS - krlmlr/fledge

Wrap R for Sweet R Code. Contribute to WinVector/wrapr development by creating an account on GitHub.

PDF Toolkit. :paperclip: :hammer: :wrench: :scissors: :bookmark_tabs: :file_folder::paperclip: :bookmark: :construction: :construction_worker: - pridiltal/staplr

I'm new to R (and programming in general), so I've been making various functions to warm myself up to it. I've been trying to figure out how to make an R function that will clear my global environ...

renv

A shiny gadget to create ggplot2 charts interactively with drag-and-drop to map your variables. You can quickly visualize your data accordingly to their type, export to PNG or PowerPoint, and retrieve the code to reproduce the chart.

It parses a fitted R model object, and returns a formula in Tidy Eval code that calculates the predictions. It works with several databases back-ends because it leverages dplyr and dbplyr for the final SQL translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.

Provenance tracking for R. Contribute to NCEAS/recordr development by creating an account on GitHub.

Documentation for the caret package.

Recommended Packages Many useful R function come in packages, free libraries of code written by R's active user community. To install an R package, open an R session and type at the command line in...

Most dplyr verbs use "tidy evaluation", a special type of non-standard evaluation. In this vignette, you'll learn the two basic forms, data masking and tidy selection, and how you can program with them using either functions or for loops.

Since the first publication of this post, a couple of packages have emerged to automate this process. The installr package for Windows and the updateR package for OS X are particularly good. However, this continues to be a popular post, so I have decid...

A complete and consistent functional programming toolkit for R.

At RStudio, I have the pleasure of interacting with data science teams around the world. Many of these teams are led by R users stepping into the role of analytic admins. These users are responsible for supporting and growing the R user base in their organization and often lead internal R user groups. One of the most successful strategies to support a corporate R user group is the creation of an internal R package.

Structuring R projects is not fun. However, especially in a collaborative data science setting, it is indispensable, and the mark of a true team player.

The tidyverse is an integrated collection of R packages designed to make data science fast, fluid, and fun.

Advice on workflows for developing R scripts. How to think about whether an action belongs in the script or elsewhere.