rstudio / shinydeveloperconference

Materials collected from the First Shiny Developer Conference Palo Alto, CA January 30-31 2016

RStudio

RStudio is an integrated development environment (IDE) for the R programming language. Some of its features include:

• Customizable workbench with all of the tools required to work with R in one place (console, source, plots, workspace, help, history, etc.).
• Syntax highlighting editor with code completion.
• Execute code directly from the source editor (line, selection, or file).
• Full support for authoring Sweave and TeX documents.
• Runs on Windows, Mac, and Linux, and has a community-maintained FreeBSD port.
• Can also be run as a server, enabling multiple users to access the RStudio IDE using a web browser.

For more information on RStudio please visit the project website.

Getting the Code

RStudio is licensed under the AGPLv3, the terms of which are included in the file COPYING. You can find our source code repository on GitHub at https://github.com/rstudio/rstudio.

Documentation

For information on how to use RStudio check out our online documentation.

For documentation on running your own RStudio Server see the server getting started guide.

See also the following files included with the distribution:

• COPYING - RStudio license (AGPLv3)
• NOTICE - Additional open source software included with RStudio
• SOURCE - How to obtain the source code for RStudio
• INSTALL - How to build and install RStudio from source

If you have problems or want to share feedback with us please visit our community forum. For other inquiries you can also email us at [email protected].

shinydeveloperconference's Issues

RxODE simulation of repeated IV infusion

deleted

Help for R -- Error in FUN(X[[i]], ...) : object 'descr' not found

Hello,

I am working on my project in RStudio using Rmarkdown and I got the following error:
Error in FUN(X[[i]], ...) : object 'descr' not found
A due date will be today. Please help!
Here is the code:

# First save the results
dataset_stats_by_Grade <- by(data = dataset, INDICES = dataset$Grade, 
                    FUN = descr, stats = c("mean", "sd", "min", "med", "max"),transpose = TRUE)

# Then use view(), like so:
View(dataset_stats_by_Grade, method = "pander", style = "rmarkdown")
When I run, I found the following error:
Error in FUN(X[[i]], ...) : object 'descr' not found 

Thanks!!

Unable to clone repository on Windows due to invalid folder name - "/solutions*"

When trying to clone on Windows I receive the following error:

$ git clone https://github.com/rstudio/ShinyDeveloperConference.git
Cloning into 'ShinyDeveloperConference'...
remote: Counting objects: 411, done.
remote: Total 411 (delta 0), reused 0 (delta 0), pack-reused 411
Receiving objects: 100% (411/411), 16.25 MiB | 973.00 KiB/s, done.
Resolving deltas: 100% (43/43), done.
Checking connectivity... done.
fatal: cannot create directory at 'Modules/Exercise-1/solution': Invalid argument*
warning: Clone succeeded, but checkout failed.
You can inspect what was checked out with 'git status'
and retry the checkout with 'git checkout -f HEAD'

We can't have folder names with * on it.

Slide 17 in Reactivity missing

Slide 17 (showing the graph of how reactivity works) that is present in the recording1 is missing in ShinyDeveloperConference/Reactivity/slides.Rmd

(On a related note, slides in github differ from slides in the recording.)

[1] https://www.rstudio.com/resources/webinars/shiny-developer-conference/

Reactivity/solution_05b uses rbind more than necessary

Is there a reason that the following doesn't demonstrate a more efficient solution?

  rv <- reactiveValues(points = cars)
  
  # Same as Solution_05a.R, but instead of keeping
  # track of the single most recent point, we accumulate
  # all previous points using rbind().
  observeEvent(input$click, {
    if (!is.null(input$click)) {
      thisPoint <- data.frame(
        speed = input$click$x,
        dist = input$click$y
      )
      rv$points <- rbind(rv$points, thisPoint)
    }
  })
  
  output$plot <- renderPlot({
    df <- rv$points
    plot(df, pch = 19)
    
    model <- lm(dist ~ speed, df)
    abline(model)
  })
}

As Joe Cheng does, I'd like to recognize that while rbind works fairly quickly usually, it seems like this demonstrates how things could be done slightly more efficiently in general