Rstudio Dplyr Cheatsheet

R Cheat Sheets
R Dplyr Cheat Sheet

2014-08-01Garrett Grolemund

R Markdown is a framework for writing versatile, reproducible reports from R. With R Markdown, you write a simple plain text report and then render it to create polished output. You can: Transform your file into a pdf, html, or Microsoft Word document—even a slideshow—at the click of a button. Embed R code into your report. When you render the file, R will run the code and insert its results into your report. Read more →

The Shiny cheat sheet is a quick reference guide for building Shiny apps. Check out all of our cheat sheets here. Please post on RStudio Community. Symbol - a name that represents a value or object stored in R. Issymbol(expr(pi)) Environment - a list-like object that binds symbols (names) to objects stored in memory.

httr 0.4 is now available on CRAN. The httr packages makes it easy to talk to web APIs from R.The most important new features are two new vignettes to help you get started and to help you make wrappers for web APIs. Other important improvements include: New headers() and cookies() functions to extract headers and cookies from responses. status_code() returns HTTP status codes. POST() (and PUT(), and PATCH()) now have an encode argument that determine how the body is encoded. Read more →

2014-07-24Roger Oberg

RStudio is very pleased to announce the general availability of Shiny Server Pro 1.2.Download a free 45 day evaluation of Shiny Server Pro 1.2Shiny Server Pro 1.2 adds support for R Markdown Interactive Documents in addition to Shiny applications. Learn more about Interactive Documents by registering for the Reproducible Reporting webinar August 13 and Interactive Reporting webinar September 3.We are excited about the new ways in which you can now share your data analysis in Shiny Server Pro along with the security, management and performance tuning capabilities you and your IT teams need to scale. Read more →

2014-07-23Hadley Wickham

I’ve released four new data packages to CRAN: babynames, fueleconomy, nasaweather and nycflights13. The goal of these packages is to provide some interesting, and relatively large, datasets to demonstrate various data analysis challenges in R. The package source code (on github, linked above) is fully reproducible so that you can see some data tidying in action, or make your own modifications to the data.Below, I’ve listed the primary dataset found in each package. Read more →

2014-07-22Kevin Ushey

We’re excited to announce a new release of Packrat, a tool for making R projects more isolated and reproducible by managing their package dependencies.This release brings a number of exciting features to Packrat that significantly improve the user experience: Automatic snapshots ensure that new packages installed in your project library are automatically tracked by Packrat. Bundle and share your projects with packrat::bundle() and packrat::unbundle() – whether you want to freeze an analysis, or exchange it for collaboration with colleagues. Read more →

tidyr is new package that makes it easy to “tidy” your data. Tidy data is data that’s easy to work with: it’s easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R’s hundreds of modelling packages). The two most important properties of tidy data are: Each column is a variable. Each row is an observation. Arranging your data in this way makes it easier to work with because you have a consistent way of referring to variables (as column names) and observations (as row indices). Read more →

2014-07-21Garrett Grolemund

We’ve added a new section of articles to the Shiny Development Center. These articles explain how to create interactive documents with Shiny and R Markdown.You’ll learn how to Use R Markdown to create reproducible, dynamic reports. R Markdown offers one of the most efficient workflows for writing up your R results. Create interactive documents and slideshows by embedding Shiny elements into an R Markdown report. The Shiny + R Markdown combo does more than just enhance your reports; R Markdown provides one of the quickest ways to make light weight Shiny apps. Read more →

The RStudio team recently rolled out new capabilities in RStudio, shiny, ggvis, dplyr, knitr, R Markdown, and packrat. The “Essential Tools for Data Science with R” free webinar series is the perfect place to learn more about the power of these R packages from the authors themselves.Click to learn more and register for one or more webinar sessions. You must register for each separately. If you miss a live webinar or want to review them, recorded versions will be available to registrants within 30 days. Read more →

2014-07-08Garrett Grolemund

RStudio will teach the new essentials for doing data science in R at this year’s Strata NYC conference, Oct 15 2014.R Day at Strata is a full day of tutorials that will cover some of the most useful topics in R. You’ll learn how to manipulate and visualize data with R, as well as how to write reproducible, interactive reports that foster collaboration. Topics include:9:00am – 10:30am A Grammar of Data Manipulation with dplyr Speaker: Hadley Wickham Read more →

2014-06-30Garrett Grolemund

Shiny v0.10 comes with a quick, handy guide. Use the Shiny cheat sheet as a quick reference for building Shiny apps. The cheat sheet will guide you from structuring your app, to writing a reactive foundation with server.R, to laying out and deploying your app.You can find the Shiny cheat sheet along with many more resources for using Shiny at the Shiny Dev Center, shiny.rstudio.com.(p.s. Visit the RStudio booth at useR! Read more →