I have played a bit with shiny. The RStudio folks provides the possibility of deploying R “Shiny” applications on their server. The final result which will look a lot like this: https://<YourAccount>.shinyapps.io/<YourApp>. There are several plans for this type of deployment, including one that allows you to deploy up to 5 applications for free.
You can sign up using on www.shinyapps.io creating a new account or logging in using your github.com or gmail account. The most recent version of Shiny does no longer require two separate files as I was reporting in the last article on the subject. Now you can condense all the functionality in a single file: app.R. In synthesis, you need to build a app.R file that contains the following:
- The inclusion of the shiny library: library(shiny)
- A ui function: ui <-fluidPage(….
- This will define your page layout and your controls
- A server function: server <- function(input, output) {
- This will be your server logic
- A call to shinyApp(ui=ui, server=server )
- This is the glue that puts everything together
When building your application in RStudio, it recognizes it as a “Shiny” application and it lets run it in your browser, in a separate window or in the plot pane. It also shows a “Publish” button that allows you to upload the application to the www.shinyapps,io website. This you can do provided that you have set up an account.
To learn the basics, I have used the tutorials and the shiny app gallery that you can find at http://shiny.rstudio.com/gallery/. Almost all examples include a link to or a pane with the source code.
As far as the application, I have borrowed some code from this article on r-bloggers.com. This is in relation with a swirl() problem on two of the classes of the Statistical Inference course on Coursera.
The shiny app I propose can be found at this link: Shaded Normal Distribution. It does much less than the original shadenorm() function by Tony Cookson did, I tried to leave all the nice use cases that he proposed as comments in the code. This is a screenshot, You will find all the source code below it:
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library(shiny) library(ggplot2) # DEFINE UI ui <-fluidPage( # Application title titlePanel("Shaded Normal Distribution"), # Sidebar with a slider inputs sidebarLayout( sidebarPanel( sliderInput("mu", "Mean:", min = -20, max = 20, value = 0), sliderInput("sig", "Sigma:", min = 1, max = 20, value = 1), checkboxInput("lefttail", label = "Hide Left Tail", value = FALSE), checkboxInput("righttail", label = "Hide Right Tail", value = FALSE), sliderInput("dens", "Density:", min = 1, max = 100, value = 40), sliderInput("below", "Below:", min = -3.0, max = 0.0, value = -1.96, step= 0.005), sliderInput("above", "Above:", min = 0, max = 3.0, value = 1.96, step= 0.005) ), # Show a plot of the generated distribution mainPanel( plotOutput("normPlot") ) ) ) # DEFINE SERVER LOGIC server <- function(input, output) { output$normPlot <- renderPlot({ shadenorm(mu=input$mu, sig=input$sig, justabove = input$lefttail, justbelow = input$righttail, dens=input$dens, below=input$below, above=input$above) }) } # From r-bloggers.com # # How to Shade Under a Normal Density in R # April 3, 2011 # By Tony Cookson # shadenorm = function(below=NULL, above=NULL, pcts = c(0.025,0.975), mu=0, sig=1, numpts = 500, color = "blue", dens = 40, justabove= FALSE, justbelow = FALSE, lines=FALSE,between=NULL,outside=NULL){ if(is.null(between)){ below = ifelse(is.null(below), qnorm(pcts[1],mu,sig), below) above = ifelse(is.null(above), qnorm(pcts[2],mu,sig), above) } if(is.null(outside)==FALSE){ below = min(outside) above = max(outside) } lowlim = mu - 4*sig uplim = mu + 4*sig x.grid = seq(lowlim,uplim, length= numpts) dens.all = dnorm(x.grid,mean=mu, sd = sig) if(lines==FALSE){ plot(x.grid, dens.all, type="l", xlab="X", ylab="Density") } if(lines==TRUE){ lines(x.grid,dens.all) } if(justabove==FALSE){ x.below = x.grid[x.grid<below] dens.below = dens.all[x.grid<below] polygon(c(x.below,rev(x.below)),c(rep(0,length(x.below)),rev(dens.below)),col=color,density=dens) } if(justbelow==FALSE){ x.above = x.grid[x.grid>above] dens.above = dens.all[x.grid>above] polygon(c(x.above,rev(x.above)),c(rep(0,length(x.above)),rev(dens.above)),col=color,density=dens) } if(is.null(between)==FALSE){ from = min(between) to = max(between) x.between = x.grid[x.grid>from&x.grid<to] dens.between = dens.all[x.grid>from&x.grid<to] polygon(c(x.between,rev(x.between)),c(rep(0,length(x.between)),rev(dens.between)),col=color,density=dens) } } <p style="text-align: left;"># THIS IS THE GLUE shinyApp(ui=ui, server=server )</p> # ## ---------------------- ## # # ## Shading under a Normal ## # # ## in R ## # # ## ---------------------- ## # # shadenorm() ## Defaults mu = 0, sig = 1, pcts = c(0.025, 0.975) # # ## Below, Above, Outside and Between # # shadenorm(below=-1.5) ## Sets lower endpoint at -1.5 # # shadenorm(below=-1.5,justbelow=TRUE) ## Just plots the lower tail # # shadenorm(above=1.5) ## Sets upper endpoint at 1.5 # # shadenorm(above=1.96, justabove=TRUE) # # shadenorm(above=1.5, justabove=TRUE) ## Similar to below # # shadenorm(below=-1.5,above=1.5) ## Plots "outside" of -1.5 and 1.5 # # shadenorm(outside = c(2.5, -2.5)) ## Alternative "outside" plotter # # shadenorm(outside = c(1, 2, -1, -0.2, 2.4)) ## works by taking min and max # # shadenorm(between = c(2.5, -2.5)) ## Plots between specified points # # shadenorm(between = c(-1.75, 0, 2, 0.5, -1)) ## Plots between specified points # # ## Can specify different means and standard deviations with mu and sig arguments # # shadenorm(mu=2, sig=10) # # shadenorm(mu=2, sig=10, between=c(-2, 14)) ## works with between and outside # # shadenorm(mu=2, sig=10, outside=c(-5, 14)) # # ## Can Apply different colors and densities... color and dens # # shadenorm(mu=2, sig=10, outside=c(-3, 12), col="blue") # # shadenorm(mu=2, sig=10, outside=c(-3, 12), col="blue",dens=15) ## Dens default is 40 # # shadenorm(mu=2, sig=10, outside=c(-3, 12), col="blue",dens=150) # # ## Can plot one and then another ontop of it using lines = TRUE # # shadenorm(mu=2, sig=10, outside=c(-3, 12), dens=15) # # shadenorm(mu=2, sig=15, between=c(-3, 12),lines=TRUE, col="blue",dens=15) # # ## Example: Plotting a Hypothesis Test for the mean # # ## Truth: mu.true = 8 # # ## Hypothesis: mu.ho = 6 # # ## Generate Data Under Truth # # mu.true = 5 ## Alternative Mean # # mu.ho = 6 # # sig = 8 # # N = 250 ## Sample Size # # # # std.err = sig/sqrt(N) # # crits = qnorm(c(0.025,0.975),mean=mu.ho, sd = std.err) # # shadenorm(outside = crits, mu = mu.ho, sig = std.err,dens=15) # # shadenorm(between = crits, mu = mu.true, sig = std.err, lines=TRUE, color="green",dens=15) <p style="text-align: left;"> |
