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Action buttons can be tricky to use in Shiny because they work differently than other widgets. Widgets like sliders and select boxes maintain a value that is easy to use in your code. But the value of an action button is arbitrary. What should you do with it? Did you know that you should almost always call the value of an action button from
The newest article at the Shiny Development Center explains how action buttons work, and it provides five useful patterns for working with action buttons. These patterns also work well with action links.
Read the article here.
We’ve added a new cheatsheet to our collection. Data Visualization with ggplot2 describes how to build a plot with ggplot2 and the grammar of graphics. You will find helpful reminders of how to use:
- coordinate systems
- position adjustments
- legends, and
The cheatsheet also documents tips on zooming.
Download the cheatsheet here.
Bonus – Frans van Dunné of Innovate Online has provided Spanish translations of the Data Wrangling, R Markdown, Shiny, and Package Development cheatsheets. Download them at the bottom of the cheatsheet gallery.
We’ve added a new cheatsheet to our collection! Package Development with devtools will help you find the most useful functions for building packages in R. The cheatsheet will walk you through the steps of building a package from:
- Setting up the package structure
- Adding a DESCRIPTION file
- Writing code
- Writing tests
- Writing documentation with roxygen
- Adding data sets
- Building a NAMESPACE, and
- Including vignettes
The sheet focuses on Hadley Wickham’s devtools package, and it is a useful supplement to Hadley’s book R Packages, which you can read online at r-pkgs.had.co.nz.
Download the sheet here.
Bonus – Vivian Zhang of SupStat Analytics has kindly translated the existing Data Wrangling, R Markdown, and Shiny cheatsheets into Chinese. You can download the translations at the bottom of the cheatsheet gallery.
We’ve teamed up with DataCamp to make a self-paced online course that teaches ggvis, the newest data visualization package by Hadley Wickham and Winston Chang. The ggvis course pairs challenging exercises, interactive feedback, and “to the point” videos to let you learn ggvis in a guided way.
In the course, you will learn how to make and customize graphics with ggvis. You’ll learn the commands and syntax that ggvis uses to build graphics, and you’ll learn the theory that underlies ggvis. ggvis implements the grammar of graphics, a logical method for building graphs that is easy to use and to extend. Finally, since this is ggvis, you’ll learn to make interactive graphics with sliders and other user controls.
The first part of the tutorial is available for free, so you can start learning immediately.
RStudio has teamed up with O’Reilly media to create a new way to learn R!
The Introduction to Data Science with R video course is a comprehensive introduction to the R language. It’s ideal for non-programmers with no data science experience or for data scientists switching to R from Excel, SAS or other software.
Join RStudio Master Instructor Garrett Grolemund as he covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. You’ll learn R’s syntax and grammar as well as how to load, save, and transform data, generate beautiful graphs, and fit statistical models to the data.
All of the techniques introduced in this video are motivated by real problems that involve real datasets. You’ll get plenty of hands-on experience with R (and not just hear about it!), and lots of help if you get stuck.
You’ll also learn how to use the ggplot2, reshape2, and dplyr packages.
The course contains over eight hours of instruction. You can access the first hour free from O’Reilly’s website. The course covers the same content as our two day Introduction to Data Science with R workshop, right down to the same exercises. But unlike our workshops, the videos are self-paced, which can help you learn R in a more relaxed way.
To learn more, visit Introduction to Data Science with R.
RStudio has teamed up with Datacamp to create a new, interactive way to learn dplyr. Dplyr is an R package that provides a fast, intuitive way to transform data sets with R. It introduces five functions, optimized in C++, that can handle ~90% of data manipulation tasks. These functions are lightning fast, which lets you accomplish more things—with more data—than you could otherwise. They are also designed to be intuitive and easy to learn, which makes R more user friendly. But this is just the beginning. Dplyr also automates groupwise operations in R, provides a standard syntax for accessing and manipulating database data with R, and much more.
In the course, you will learn how to use dplyr to
filter()observations from your data in a targeted way
arrange()observations within your data set by value
- derive new variables from your data with
- create summary statistics with
- perform groupwise operations with
- use the dplyr syntax to access data stored in a database outside of R.
You will also practice using the
tbl data structure and the new pipe operator in R, %>%.
The course is taught by Garrett Grolemund, RStudio’s Master Instructor, and is organized around Datacamp’s interactive interface. You will receive expert instruction in short, clear videos as you work through a series of progressive exercises. As you work, the Datacamp interface will provide immediate feedback and hints, alerting you when you do something wrong and rewarding you when you do something right. The course is designed to take about 4 hours and requires only a basic familiarity with R.
Some of the most innovative Shiny apps share data across user sessions. Some apps share the results of one session to use in future sessions, others track user characteristics over time and make them available as part of the app.
This level of sophistication creates tricky design choices when you host your app on a server. A nimble server will open new instances of your app to speed up performance, or relaunch your app on a bigger server when it becomes popular. How should you ensure that your app can find and use its data trail along the way?
Shiny Server developer Jeff Allen explains the best ways to share data between sessions in Share data across sessions with ShinyApps.io, a new article at the Shiny Dev Center.
Registration is now open for the next Master R Development workshop led by Hadley Wickham, author of over 30 R packages and the Advanced R book. The workshop will be held on January 19 and 20th in the San Francisco bay area.
The workshop is a two day course on advanced R practices and package development. You’ll learn the three main paradigms of R programming: functional programming, object oriented programming and metaprogramming, as well as how to make R packages, the key to well-documented, well-tested and easily-distributed R code.
To learn more, or register visit rstudio-sfbay.eventbrite.com.
Want to see who is using your Shiny apps and what they are doing while they are there?
Google Analytics is a popular way to track traffic to your website. With Google Analytics, you can see what sort of person comes to your website, where they arrive from, and what they do while they are there.
Since Shiny apps are web pages, you can also use Google Analytics to keep an eye on who visits your app and how they use it.
RStudio is planning a new Master R Developer Workshop to be taught by Hadley Wickham in the San Francisco Bay Area on January 19-20. This will be the same workshop that Hadley is teaching in September in New York City to a sold out audience.
If you did not get a chance to register for the NYC workshop but wished to, consider attending the January Bay Area workshop. We will open registration once we have planned out all of the event details. If you would like to be notified when registration opens, leave a contact address here.