ANOVA test

Data

A caption

Assumption Test

ANOVA Test

Box Plot

Multiple Comparison

Code

# 
# if (!require("pacman")) install.packages("pacman")
# pacman::p_load("party", "car", "shinythemes","DT","plotly","RColorBrewer","RColorBrewer","lubridate","leaflet","ggplot2","shiny")
# library(shiny)
# library(ggplot2)
# library(dplyr)
# library(leaflet)
# library(lubridate)
# library(RColorBrewer)
# library(plotly)
# library(DT)
# library(shinythemes)
# library(car)
# library(party)
# #https://www.statmethods.net/stats/anova.html #MANOVA
# library(multcomp) #tukey test 
# runApp(
#   list(
#     ui = fluidPage(
#       headerPanel('Analysis of Variance'),
#       sidebarPanel(
#         fileInput("file1", "CSV file", accept=c("text/csv", "text/comma-separated-values,text/plain", ".csv")),
#         checkboxInput("header", "Header", TRUE),
#         radioButtons('sep', 'Separator',c(Comma=',',Semicolon=';',Tab='\t'),','),
#         selectInput('type', 'Sums of Squares type', 
#                     c(I = 'type1', III = 'type3'), 'type1'),
#         numericInput("CL", "Confidence Level",0.1, min=0, max=1)
#         ,uiOutput('var'),
#         a("Click here!", href = "http://www.listendata.com/") #creating a hyperlink
#       )
#       , mainPanel( 
#         tabsetPanel(type = "tabs", id = "tabs",
#                     tabPanel("Data",DT::dataTableOutput(outputId = "Datatable")),
#                     tabPanel("Assumption Test", h4("Equal Variance Test"), tableOutput(outputId = "variance"),h4("Normality Test"), tableOutput(outputId = "normal"),plotOutput(outputId = "normality")),
#                     tabPanel('ANOVA Table',tableOutput('aovSummary')),
#                     tabPanel("Box Plot", plotOutput(outputId = "boxplot")),
#                     tabPanel("Multiple Comparison",plotOutput(outputId = "multp"),verbatimTextOutput("pair"),plotOutput(outputId = "ci"))
#         )
#       )
#     )
#     , 
#     server = function(input, output, session) {
#       #file
#       csvfile <- reactive({
#         csv <- input$file1
#         if (is.null(csv)){return(NULL)}
#         dt <- read.csv(csv$datapath, header=input$header, sep=input$sep)
#         dt
#       })
#       #variable
#       output$var <- renderUI({
#         if(is.null(input$file1$datapath)){
#           return()
#         }else{
#           
#           list (selectInput("dvar", "Dependent Variable", choices =   names(csvfile()[, sapply(csvfile(), class) == "numeric"])),
#                 selectInput("ivar", "Independent Variable", choices = csvfile()%>% dplyr::select_if(is.factor) %>% names()),
#                 selectInput("cvar", "Color by", choices = names(csvfile())),
#                 actionButton("submit", "Submit")
#           )
#         }
#       })
# 
# 
#       #ANOVA summary
#       output$aovSummary = renderTable({
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           if(input$type == 'type1'){
#             return(isolate(anova(lm(csvfile()[,input$dvar] ~ csvfile()[,input$ivar], data = csvfile()))))
#           }
#           if(input$type == 'type2'){
#             return(isolate(Anova(lm(csvfile()[,input$dvar] ~ csvfile()[,input$ivar], data = csvfile())), Type = "II", test.statistic = "F"))
#           }
#           if(input$type == 'type3'){
#             isolate(
#               fit <- aov(csvfile()[,input$dvar] ~ csvfile()[,input$ivar], data = csvfile())
#             )
#             return(drop1(fit, ~ . , test = 'F'))
#           }
#         }
#       })
#       #multiple plot
#       output$multp <- renderPlot({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           ind <- csvfile()[,input$ivar]
#           fit1 <- aov(csvfile()[,input$dvar] ~ ind, data = csvfile())
#           fit2<- summary(glht(fit1 , linfct = mcp(ind = "Tukey")))
#           fit3 <- cld(fit2,level=input$CL)
#           return(plot(fit3, xlab="Treatment"))
#         }
#       })
#       #Pairwise
#       output$pair <- renderPrint({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           ind <- csvfile()[,input$ivar]
#           fit1 <- aov(csvfile()[,input$dvar] ~ ind, data = csvfile())
#           fit2<- summary(glht(fit1 , linfct = mcp(ind = "Tukey")))
#           return(fit2)
#         }
#       })
#       #confidence interval plot
#       output$ci <- renderPlot({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           ind <- csvfile()[,input$ivar]
#           fit1 <- aov(csvfile()[,input$dvar] ~ ind, data = csvfile())
#           fit2<- confint(glht(fit1 , linfct = mcp(ind = "Tukey")))
#           fit3 <- print(fit2)
#           return(plot(fit3, xlab="Treatment"))
#         }
#       })
#       # Create the boxplot object the plotOutput function is expecting
#       output$boxplot <- renderPlot({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#         return(ggplot(data = csvfile(), aes_string(x = csvfile()[,input$ivar], y = csvfile()[,input$dvar], fill=csvfile()[,input$cvar])) + # x, y, and color layers of the plot depend on user input
#           scale_fill_brewer(palette="Spectral") +
#           geom_boxplot(outlier.colour="red", outlier.shape=8, outlier.size=4))
#         }
#       })
#       # Homogeneity Test
#       output$variance <- renderTable({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           return(leveneTest(csvfile()[,input$dvar] ~ csvfile()[,input$ivar], data = csvfile()))
#         }
#       })
#       # Normality Test
#       output$normal <- renderTable({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           norm <- unlist(shapiro.test(csvfile()[,input$dvar]))
#           return(print(norm))
#         }
#       })
#       # Normality Test
#       output$normality <- renderPlot({ 
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           fit1 <- aov(csvfile()[,input$dvar] ~ csvfile()[,input$ivar], data = csvfile())
#           par(mfrow=c(1,2))
#           return(list(plot(fit1,1),plot(fit1,2)))
#         }
#       })
#       #Data Table
#       output$Datatable <- DT::renderDataTable({
#         if(is.null(input$file1$datapath)){return()}
#         if(input$submit > 0){
#           return(DT::datatable(data = csvfile(),
#                       options = list(pageLength = 20),
#                       rownames = F))
#         }  
#       })
#     })
# )