if (!require("pacman")) install.packages("pacman")
pacman::p_load(shiny, rmcorr, rlang, tidyverse)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
fileInput("file1", "Choose CSV File", accept = ".csv"),
checkboxInput("header", "Header", TRUE),
selectInput("subvar", label = "Subject Variable", choices = NULL),
selectInput("xvar", label = "x Variable", choices = NULL),
selectInput("yvar", label = "y Variable", choices = NULL),
#only enable this if three columns are selected?
actionButton("compute", label = "Compute repeated measures correlation"),
actionButton("plot", label = "Plot")
#also allow them to paste in data?
),
mainPanel(
h1("Dataset"),
DT::dataTableOutput("df"),
h1("Repeated Measures Correlation"),
uiOutput(outputId = "rmc"),
h1("Plot"),
plotOutput("rmcplot")
)
)
)
server <- function(input, output, session) {
df <- reactive({
file <- input$file1
ext <- tools::file_ext(file$datapath)
req(file)
validate(need(ext == "csv", "Please upload a csv file"))
read.csv(file$datapath, header = input$header)
})
output$df <- DT::renderDataTable(df())
observe({
choices1 = colnames(df())
updateSelectInput(session,"subvar", choices = choices1)
updateSelectInput(session,"xvar", choices = choices1)
updateSelectInput(session,"yvar", choices = choices1)
})
data <- eventReactive(input$compute, {
rmcorr(input$subvar, input$xvar, input$yvar, dataset = df())
})
output$rmc <- renderUI({
str_rrm <- paste("Repeated measures correlation: ", round(data()$r, digits = 3))
str_df <- paste("Degrees of freedom: ", data()$df)
str_p <- paste("p-value:", data()$p)
str_CI <- paste("95% Confidence Interval: ", paste0(round(data()$CI[1], digits = 3), sep = ", ", round(data()$CI[2], digits = 3)))
HTML(paste(str_rrm, str_df, str_p, str_CI, sep = '</br>'))
#Add diag info: Sample size (N) and mean repeated measures (k) with range?
#Need warning for missing data, empty cells?
#Jon: Worthwhile for me to add formatted output with paper-ready stats??? r_rm(df) = 0.ZZZ 95% CI [ , ], p = 0.XYZ
})
plotdata <- eventReactive(input$plot, {
ggplot2::ggplot(df(), ggplot2::aes(x = !!(sym(input$xvar)), y = !!(sym(input$yvar)), group = factor(!!(sym(input$subvar))),
color = factor(!!(sym(input$subvar))))) +
ggplot2::geom_point(ggplot2::aes(colour = factor(!!(sym(input$subvar))))) +
ggplot2::geom_line(ggplot2::aes(y = data()$model$fitted.values), linetype = 1)
})
output$rmcplot <- renderPlot({
plotdata()
})
}
shinyApp(ui = ui, server = server)
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