Anorexia experiment data

server.r

library(shiny)

# Data preparation ----------------------------------------------------------
library(MASS) # For the anorexia data set
library(reshape2)

# Load the anorexia data set
data(anorexia)

# Give each person a unique ID
anorexia$ID <- as.factor(1:nrow(anorexia))

# Rename columns
names(anorexia)[names(anorexia) == "Prewt"]  <- "Pre"
names(anorexia)[names(anorexia) == "Postwt"] <- "Post"

names(anorexia)[names(anorexia) == "Treat"] <- "Treatment"

# Change order of Treatment factor levels
anorexia$Treatment <- factor(anorexia$Treatment, levels = c("Cont", "CBT", "FT"))

# Rename Treatment values
levels(anorexia$Treatment)[levels(anorexia$Treatment) == "Cont"] <- "Control"
levels(anorexia$Treatment)[levels(anorexia$Treatment) == "CBT"] <- "Cognitive behavioral"
levels(anorexia$Treatment)[levels(anorexia$Treatment) == "FT"] <- "Family treatment"


# Convert to long format
anorexia <- melt(anorexia,
                 id.vars       = c("ID", "Treatment"),
                 value.vars    = c("Pre", "Post"),
                 variable.name = "Time",
                 value.name    = "Weight")

# Make sure factor levels Pre and Post are in correct order for plotting
anorexia$Time <- factor(anorexia$Time, levels = c("Pre", "Post"))


# Calculate mean, standard deviation, and count
library(plyr)
anorexia_summary <- ddply(anorexia, c("Treatment", "Time"), summarise,
                          mean = mean(Weight),
                          sd   = sd(Weight),
                          n    = length(Treatment))

names(anorexia_summary)[names(anorexia_summary) == "mean"] <- "Weight"



# Define server logic -------------------------------------------------------
shinyServer(function(input, output) {

  # Generate a plot of the requested variable against mpg and only 
  # include outliers if requested
  output$main_plot <- reactivePlot(function() {

    # Options:
    # x Color by group
    # x Facet by group
    # Overall means and error bars

    if (input$color_treatment) {
      aes_mapping <- aes(x=Time, y=Weight, colour=Treatment, shape=Treatment)
    } else {
      aes_mapping <- aes(x=Time, y=Weight)
    }


    # Save this dodging specification
    pd <- position_dodge(0.1)

    p <- ggplot(anorexia, mapping = aes_mapping) +
      geom_point(alpha = 0.3) +
      geom_line(aes(group = ID), alpha = 0.2) +
      scale_shape_manual(values = c(21, 22, 24)) +
      scale_colour_hue(l = 40) +
      theme_bw()

    if (input$facet_treatment) {
      p <- p + facet_grid(. ~ Treatment)
    }


    if (input$show_means) {
      # Add mean points
      p <- p +
           geom_line(data = anorexia_summary, aes(group = Treatment),
                     size = 1.5, position = pd) +
           geom_point(data = anorexia_summary, aes(group = Treatment),
                      size = 5, fill = "white", position = pd)
    }

    print(p)
  })


  # Text output options
  # t-test overall (paired)
  # t-test for each group
  # ANOVA between groups

  output$t_test_output <- reactivePrint(function() {

    # If we're distinguishing groups, do separate t-tests for each group, and
    # put them in a list
    if (input$color_treatment || input$facet_treatment) {
      list(
        "Control" =
          t.test(Weight ~ Time,
                 data = subset(anorexia, Treatment == "Control"),
                 paired = input$t_tests_paired),
        "Cognitive behavioral" =
          t.test(Weight ~ Time,
                 data = subset(anorexia, Treatment == "Cognitive behavioral"),
                 paired = input$t_tests_paired),
        "Family treatment" =
          t.test(Weight ~ Time,
                 data = subset(anorexia, Treatment == "Family treatment"),
                 paired = input$t_tests_paired))

    } else {
      t.test(Weight ~ Time, data = anorexia, paired = input$t_tests_paired)
    }

  })


  output$anova_output <- reactivePrint(function() {
    res <- aov(Weight ~ Treatment * Time + Error(ID/Time), data=anorexia)
    summary(res)
  })

})

ui.r

library(shiny)
library(ggplot2)

# Define UI for miles per gallon application
shinyUI(pageWithSidebar(
  
  # Application title
  headerPanel("Anorexia treatment experiment"),
  
  # Sidebar with controls to select the variable to plot against mpg
  # and to specify whether outliers should be included
  sidebarPanel(
    wellPanel(
      p(strong("Show different treatment groups")),

      checkboxInput("color_treatment",
                    "With colors and shapes",
                    value = FALSE),

      checkboxInput("facet_treatment",
                    "With facets",
                    value = FALSE),

      conditionalPanel(
        condition = "input.color_treatment || input.facet_treatment",
        checkboxInput("show_means",
                      "Show means",
                      value = FALSE)
      )
    ),

    wellPanel(
      p(strong("Statistical tests")),

      checkboxInput("t_tests",
                    "t-test for pre/post weight",
                    value = FALSE),

      # TODO: Indent this somehow?
      conditionalPanel(
        condition = "input.t_tests",
        checkboxInput("t_tests_paired",
                      "Use paired t-test",
                      value = TRUE)

      ),
      checkboxInput("anova",
                    "ANOVA between treatment conditions",
                    value = FALSE)

    )
  ),
  
  # Show the caption and plot of the requested variable against mpg
  mainPanel(
    plotOutput("main_plot"),

    conditionalPanel(
      condition = "input.t_tests",
      h3("t-test for weight before and after treatment"),
      verbatimTextOutput("t_test_output")
    ),

    conditionalPanel(
      condition = "input.anova",
      h3("ANOVA for weight before and after treatment"),
      p("3x2 mixed design: Treatment (between) and Time (within)."),
      verbatimTextOutput("anova_output")
    )
  )
))