# Item Analysis App – Shiny Code

May 28, 2013
By

(This article was first published on Econometrics by Simulation, and kindly contributed to R-bloggers)

Here is the code for my first Shiny App! It is the one that I posted previously with a few slight revisions. You can see it at:A Shiny R AppIn order to make any sense of this I suggest you working through the tutorial (http://www.rstudio.com/shiny/) it would be also useful to apply for beta access to the rstudio server which provides free hosting for experimental shiny applications.To make the code below work you would need two files server.R and ui.R.  Read the tutorial and you will understand their importance.  After reading the tutorial and playing around a little I was able to create the following app.  Hope you can do the same!
 Visual Reasoning Test v0.2b
Please post as comments links to apps you have developed as well.  Also, feel free to use the data as needed.  It should continue to be updated as more people keep taking the test.
Francis
Code can be found at:
https://github.com/EconometricsBySimulation/2013-05-29-ShinyApp.git
Please tell me if I am doing this github thing wrong.  Should I have a different repo every post?
# server.Rlibrary(shiny) # For this app I will load in data form the visual reasoning test that I posted to this blog last week (Visual Reasoning Test Link).

# We have had a great deal of responses so the data is getting pretty rich!  Thanks so much :)
# I will come back to this generously generated data later!  # Load the item response data into memory with this somewhat odd formation.  con = url("http://concerto4.e-psychometrics.com/media/13/Visual.Reasoning1.RData")  load(file=con)    close(con)    nrow(individual.responses)  # I specify input$obs initially for debugging purposes. Once this loads up on the server it is overwritten by the GUI. input = list(obs=27) # Make a vector of values to identify the session ID for each test taker respondents = unique(individual.responses$sessionID)  # barplot(table(item.disp$user.answer), main="User Responses") ### Item Analysis # Create a vector of item names items = unique(individual.responses$item)    # Calculate some values that will be useful later  responses.mean  = tapply(individual.responses$anscorrect, individual.responses$item, mean)  responses.count = tapply(individual.responses$anscorrect, individual.responses$item, length)    sum.responses = data.frame(items, mean=responses.mean, count=responses.count)    # hist(sum.responses$mean, breaks=12, col=grey(.4), main="Histogram of Item Difficulties", xlab="Probability of Correct Response") # This function takes the min of a vector and that of a scalar or two vectors of equal length. tmin = function(v1,v2) { r = NULL if (length(v2)==1) v2=rep(v2,length(v1)) for (i in 1:length(v1)) r[i] = min(v1[i],v2[i]) return(r) } # A couple of examples tmin(1:10,5) tmin(1:10,10:1) # END SERVER STARTUP # Define server logic required to summarize and view the selected datasetshinyServer(function(input, output) { # Generate a summary of the item output$summary <- renderPrint({      # I want to calculate what percent of the item responses got it right and out of all responses how that compared with other items.    correct.mean = round(mean(responses.mean[input$obs]),2) percentile = round(mean(responses.mean<correct.mean),2) dataset = individual.responses[individual.responses$item==input$obs,] loading = max(table(dataset$answer)/sum(table(dataset$answer))) # I will save a number of text bits to combine together to a single text summary of the item. text0 = paste0("Item ", input$obs, ":\n")      text.5 = paste0("This item was taken by ", responses.count[input$obs], " respondents. ") text1 = "This was a very easy item. As much as " if ((percentile<.80)) text1 = "This was an easy item. As much as " if ((percentile<.60)) text1 = "This was an average item. About " if ((percentile<.40)) text1 = "This was a hard item. Only " if ((percentile<.20)) text1 = "This was a very hard item. Only " text2 = paste0(round(correct.mean,2)*100,"% of people got it correct, putting it in the ", 100-percentile*100, " percentile in terms of difficulty.") text3 = "" if ((loading > .5) & (correct.mean<.5)) text3 = paste0(" Note that there is a large loading on a response ", round(loading,2)*100 ,"% which is not the correct one. This probably indicates that there is something wrong with this problem.") cat(paste0(text0,text.5,text1,text2, text3)) }) # Plot Item Difficulty output$distPlot <- renderPlot({    hist(responses.mean, xlab="Probability of Correct Response", main="Difficulty Distribution")    abline(v=responses.mean[input$obs], lwd=3, col="red") }) # Send item preview to the control bar output$preImage = renderImage({    # When input$n is 3, filename is ./images/image3.jpeg filename = normalizePath(file.path('Images', paste0('Q', input$obs, '.png')))    # Return a list containing the filename and alt text    list(src = filename, alt = paste("Image number", input$obs)) }, deleteFile = FALSE) # Graph bar graph of responses output$respPlot <- renderPlot({    # Grab a subset of the item.response data to display    dataset = individual.responses[individual.responses$item==input$obs,]      # Set up the output for the plots that we would like.    par(mfrow=c(1,2))      # Select the color of the bar which if the right answer to be red.      barcol = c("grey", "grey", "grey", "grey", "grey")      barcol[sort(unique(individual.responses$correct))==dataset$correct[1]]="red"    barplot(table(dataset$answer), col=barcol, main=paste0("Correct Response=",dataset$correct[1]))      # Calculate the average number of correct responses per ten items.    avg.correct = tapply(dataset$anscorrect, ceiling((1:length(dataset$correct))/10), mean)    # Plot those respones over time.        plot(avg.correct, xaxt = "n", type="b", ylab="", xlab="",              main="Performance over time", ylim=c(0,1))    # Change the x axis to have custom tick labels.    navgs = length(avg.correct)      axis(1, at=1:length(avg.correct), paste0(1+(1:length(avg.correct)-1)*10,"/", (tmin((1:length(avg.correct))*10,length(dataset[[1]])))))  })    # Show a table of all of the item response values.  output$view = renderTable({ # Select the subset of data that pertains to the item selected. dataset = individual.responses[individual.responses$item==input$obs,] rownames(dataset) <- 1:nrow(dataset) dataset$ip <- dataset$item <- dataset$id <- NULL    dataset   }, digits=0)})

# ui.Rlibrary(shiny) # Define UI for dataset viewer applicationshinyUI(pageWithSidebar(   # Application title.  headerPanel("Visual Reasoning - Item Response Evaluation"),   # This is the left hand panel.  sidebarPanel(    # This image is just loaded from another image and placed as a thumbnail into the shiny GUI.      imageOutput("preImage", width = "100px", height = "100px"),     # This allows the user to specify what the look of this input device will be.    # In this case a slider that has a min of 1 and max of 92.    sliderInput("obs", "Choose Item:",                 min = 1, max = 92, value = 1, step= 1,                 ticks=c(1,25,50,75,92) , animate=TRUE),      # This is the histogram of item difficulty    plotOutput("distPlot", height = "300px"),     # This displays text below the histogram    helpText("Though histograms are organized into bins we know exactly in the range from 0 to 1 where this particular item falls.")            ),   # Now let's define the main panel.  mainPanel(    # Display the title.    h4("Item Summary"),    # Display the item summary table.    verbatimTextOutput("summary"),     # Display sub heading.    h4("User Responses"),    # Display user response table.    plotOutput("respPlot", height = "300px"),    # Display the note.    helpText("Note: Answer values are masked to mitigate potential cheating."),     # Display sub heading    h4("Observations"),    # Display the table output of item responses.    tableOutput("view"),     helpText("Order is the order that the item was given in in this particular user's experience.")    )))

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