In sccastro/DRTr: Reading, Analyzing, and Visualizing Detection Response Task Output Files

Here's a test of an output report for the kind of information you might want immediately available from DRT output.

knitr::opts_chunk$set(echo = TRUE)
require(kableExtra)
require(tidyverse)
load("SATdata.Rdata")
dat <- drt.clean 

Observations per Cell of the Design per Person

kable(table(dat$condition,dat$subid)) %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

Minimum and Maximum

kable(data.frame(rbind(t(apply(dat, 2, min)),
      t(apply(dat, 2, max)))))  %>%
  kable_styling(bootstrap_options = "striped", full_width = T)

Hits and Misses by Condition

kable(table(dat$response,dat$condition))%>%
  kable_styling(bootstrap_options = "striped", full_width = T)

Distributions

shinyApp(

  ui = fluidPage(
    selectInput("RT", "Subject:",
                choices = as.character(levels(dat$subid))),
    plotOutput("rtPlot")
  ),

  server = function(input, output) {
    data <- reactive({dat %>%
  filter(subid == input) %>%
  na.omit()})
    output$rtPlot = renderPlot({
       ggplot(data(), aes(x = rt)) + geom_histogram(data = data(), aes(x = dat$rt, y=..density.., fill = dat$condition), alpha = .5)+ 
        ylab("Frequency")+ xlab("Reaction Time") + theme_classic()
    })
  },

  options = list(height = 500)
)

Testing

    id <- sample(0:1, 100, replace=T)
val <- sample(seq(1:25), 100, replace=T)
val2 <- sample(seq(1:10), 100, replace=T)

data <- data.frame(id, val, val2)

shinyApp(


ui = fluidPage( titlePanel("why u no color?"),
                sidebarLayout(sidebarPanel(
                  selectInput("feature", "Feature:",
                    choices=colnames(data),
                    selected='val'),
        hr(),
        helpText("Select a feature to compare"),

        sliderInput("binSize",
                    "Size of Bins",
                    min = 1,
                    max = 10,
                    value = 2)
    ),

    mainPanel(
      plotOutput("featurePlot")
    )
  )
),

# Define a server for the Shiny app
server = function(input, output) {

  # Fill in the spot we created for a plot
  output$featurePlot <- renderPlot({

    # Render histogram

    # Note to readers, the outcome is the same whether the fill command
    # is as written, or written as: fill=as.factor(id)

    p <- ggplot(data, aes_string(x=input$feature, fill=as.factor(data$id))) +
        geom_histogram(alpha=0.5, aes(y=..density..), position='identity', binwidth = input$binSize);
    print(p)
  })
}
)

Clusters

shinyApp(

  ui <- fluidPage(
  headerPanel('Iris k-means clustering'), #This is the title
  sidebarPanel( #This is the sidebar with buttons:
    selectInput('xcol', 'X Variable', names(iris)), #xcol is the value, X Variable the label
    selectInput('ycol', 'Y Variable', names(iris), #ycol is the value, Y Variable the label
      selected = names(iris)[[2]]),
    numericInput('clusters', 'Cluster count', 3,
      min = 1, max = 9)
  ),
  mainPanel(
    plotOutput('plot1')
  )
  ),

server <- function(input, output) {

  selectedData <- reactive({ #We'll talk about reactives in a bit
    iris[, c(input$xcol, input$ycol)]
  })

  clusters <- reactive({
    kmeans(selectedData(), input$clusters)
  })

  output$plot1 <- renderPlot({ #This creates the plot that you want to see
    #with dynamic changes
    par(mar = c(5.1, 4.1, 0, 1)) #this creates the window position
    plot(selectedData(), #The we plot the clusters from above with selectedData()
         col = clusters()$cluster, #How many clusters
         pch = 20, cex = 3) #Color and size default
    points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
  })

})

sccastro/DRTr documentation built on May 17, 2019, 1:15 a.m.