Non-parametric Tests

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Mann-Whitney U-test

Comparing two independent conditions

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

Your data needs to have the header (variable names) in the first row.


                
                

Basic statistics


                

Ranks


                

Box plots with individual data points

Test result


                

R session info

              

Wilcoxon signed-rank test

Comparing two related conditions

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

Your data needs to have the header (variable names) in the first row.


                
                

Basic statistics


                

Ranks


                

Box plots with individual data points

Test result


                

R session info

              

Kruskal-Wallis test

Differences between several independent groups

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

Your data needs to have the header (variable names) in the first row.


                
                

Basic statistics


                

Ranks


                

Box plots with individual data points

Test result


                

R session info

              

Friedman test

Differences between several related groups

Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.

Your data needs to have the header (variable names) in the first row.


                
                

Basic statistics


                

Ranks


                

Box plots with individual data points

Test result


                

R session info

              
Note

This web application is developed with Shiny.


List of Packages Used
library(shiny)
library(shinyAce)
library(psych)
library(beeswarm)

Code

Source code for this application is based on "The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012).

The code for this web application is available at GitHub.

If you want to run this code on your computer (in a local R session), run the code below:
library(shiny)
runGitHub("npt","mizumot")


Citation in Publications

Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp


Article

Mizumoto, A., & Plonsky, L. (2015). R as a lingua franca: Advantages of using R for quantitative research in applied linguistics. Applied Linguistics, Advance online publication. doi:10.1093/applin/amv025


Recommended

To learn more about R, I suggest this excellent and free e-book (pdf), A Guide to Doing Statistics in Second Language Research Using R, written by Dr. Jenifer Larson-Hall.

Also, if you are a cool Mac user and want to use R with GUI, MacR is defenitely the way to go!


Author

Atsushi MIZUMOTO, Ph.D.
Associate Professor of Applied Linguistics
Faculty of Foreign Language Studies /
Graduate School of Foreign Language Education and Research,
Kansai University, Osaka, Japan



Code for "Non-parametric Tests"
by Atsushi Mizumoto

show with app
library(shiny)
library(shinyAce)
library(psych)
library(beeswarm)



shinyServer(function(input, output) {


#-----------------------------------------------------------------
# Mann-Whitney U-test (Comparing two independent conditions)
#-----------------------------------------------------------------

    # Basic statistics

        MWU.bs <- reactive({

            dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))
            describeBy(dat[,2], dat[,1])

        })

        output$MWU.bs.out <- renderPrint({
            MWU.bs()
        })



    # Rank

        MWU.ranking <- reactive({

            dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))

            ranked <- rank(dat[,2])
            data <- data.frame(dat[,1], ranked)

            n <- round(tapply(data[,2], data[,1], length),2)
            m <- round(tapply(data[,2], data[,1], mean),2)
            t <- round(tapply(data[,2], data[,1], sum),2)
            ranks <- data.frame(n, m, t)
            colnames(ranks) <- c("n","Rank Mean","Rank Sum")

            print(ranks)

        })

        output$MWU.ranking.out <- renderPrint({
            MWU.ranking()
        })



    # Box plot

        MWU.boxPlot <- function(){
            dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))

            boxplot(dat[,2] ~ dat[,1], las=1)
            beeswarm(dat[,2] ~ dat[,1], col = 4, pch = 16, vert = TRUE, add = TRUE)

        }

        output$MWU.boxPlot <- renderPlot({
        print(MWU.boxPlot())
        })



    # Mann-Whitney U-test

        MWU.test <- reactive({

            dat <- read.csv(text=input$text1, sep="", na.strings=c("","NA","."))

                dat2 <- split(dat, dat[,1])
                x <- dat2[[1]][,2]
                y <- dat2[[2]][,2]
                max.len = max(length(x), length(y))
                x <- c(x, rep(NA, max.len - length(x)))
                y <- c(y, rep(NA, max.len - length(y)))

                U.test <- function(x, y, correct = TRUE)
                {
                    x <- x[!is.na(x)]
                    y <- y[!is.na(y)]
                    n1 <- length(x)
                    n2 <- length(y)
                    n <- n1 + n2
                    xy <- c(x, y)
                    r <- rank(xy)
                    U1 <- n1*n2+n1*(n1+1)/2-sum(r[1:n1])
                    tie <- table(r)
                    U <- min(U1, n1*n2-U1) # U
                    V <- n1*n2*(n^3-n-sum(tie^3-tie))/12/(n^2-n) # variance ties considered
                    E <- n1*n2/2 # Expected
                    z <- ((U-E)-ifelse(correct, 0.5, 0))/sqrt(V) # z-value
                    EffectSize.r <- abs(z)/sqrt(n)
                    ESrCI <- r.con(EffectSize.r, n, p =.95, twotailed=TRUE)
                    P <- pnorm(abs(z), lower.tail=FALSE)*2
                    cat(" Mann-Whitney U-test", "\n",
                    "\n",
                    "U =", U, ",", "E(U) =", E, ",", "V(U) =", V, "\n",
                    "z-value =", round(z, 3), "\n",
                    "p-value =", P, "\n", "\n",
                    "Effect size r [95% CI]=", round(EffectSize.r, 3), "[", round(ESrCI, 3), "]", "\n"
                    )
                }
                U.test(x, y, correct = FALSE)
        })

        output$MWU.test.out <- renderPrint({
            MWU.test()
        })



    # Info
        MWU.info <- reactive({
            info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
            info2 <- paste("It was executed on ", date(), ".", sep = "")
            cat(sprintf(info1), "\n")
            cat(sprintf(info2), "\n")
        })

        output$MWU.info.out <- renderPrint({
            MWU.info()
        })





#-----------------------------------------------------------------
# Wilcoxon signed-rank test (Comparing two related conditions)
#-----------------------------------------------------------------

    # Basic statistics

        WSR.bs <- reactive({

            dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))
            describe(dat)

        })

        output$WSR.bs.out <- renderPrint({
            WSR.bs()
        })



    # Rank

        WSR.ranking <- reactive({

            dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))

            dat$diff <- dat[,2] - dat[,1]
            dat$sign <- ifelse(dat$diff < 0, "Negative", ifelse((dat$diff > 0), "Positive", "Tie"))
            newdata <- subset(dat, dat$sign != "Tie") # Except Tie
            newdata$rank <- rank(abs(newdata$diff))
            n <- tapply(dat[,1], dat$sign, length)
            m <- tapply(newdata$rank, newdata$sign, mean)
            t <- tapply(newdata$rank, newdata$sign, sum)

            list(n = n, "Rank Mean" = round(m, 2), "Rank Sum" = round(t, 2))

        })

        output$WSR.ranking.out <- renderPrint({
            WSR.ranking()
        })



    # Box plot

       WSR.boxPlot <- function(){
            dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))

            boxplot(dat, las=1)
            beeswarm(dat, col = 4, pch = 16, vert = TRUE,  add = TRUE)

        }

        output$WSR.boxPlot <- renderPlot({
            print(WSR.boxPlot())
        })



    # Wilcoxon signed-rank test

        WSR.test <- reactive({

            dat <- read.csv(text=input$text2, sep="", na.strings=c("","NA","."))

            x <- dat[,1]
            x <- x[!is.na(x)]

            y <- dat[,2]
            y <- y[!is.na(y)]
          
            options(warn=-1) # Suppress a warning message
            result <- wilcox.test(x, y, paired=TRUE, correct=FALSE)

            pval <- result$p.value
            z <- qnorm(1-(pval/2))
            r1 <- z/sqrt(length(x)*2)
            esR.CI1 <- r.con(r1, length(x*2), p =.95, twotailed=TRUE)
            #r2 <- z/sqrt(length(x)-sum((y-x==0)))
            #esR.CI2 <- round(r.con(r2, length(x)-sum((y-x==0)), p =.95, twotailed=TRUE), 3)
            
            print(result)

            cat(" z-value =", round(z, 3), "\n",
            "\n",
            "Effect size r [95% CI] =", round(r1, 3), "[", round(esR.CI1, 3), "]", "\n"
            )
            #"Effect size r (without considering ties) [95% CI]=", round(r2, 3), "[", esR.CI1, "]", "\n")

        })

        output$WSR.test.out <- renderPrint({
            WSR.test()
        })



    # Info
        WSR.info <- reactive({
            info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
            info2 <- paste("It was executed on ", date(), ".", sep = "")
            cat(sprintf(info1), "\n")
            cat(sprintf(info2), "\n")
        })

        output$WSR.info.out <- renderPrint({
            WSR.info()
        })





#-----------------------------------------------------------------
# Kruskal-Wallis test (Differences between several independent groups)
#-----------------------------------------------------------------

    # Basic statistics

        KW.bs <- reactive({

            dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))
            describeBy(dat[,2], dat[,1])

        })

        output$KW.bs.out <- renderPrint({
            KW.bs()
        })



    # Rank

        KW.ranking <- reactive({

            dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))

            ranked <- rank(dat[,2])
            data <- data.frame(dat[,1], ranked)

            n <- round(tapply(data[,2], data[,1], length),2)
            m <- round(tapply(data[,2], data[,1], mean),2)
            t <- round(tapply(data[,2], data[,1], sum),2)
            ranks <- data.frame(n, m, t)
            colnames(ranks) <- c("n","Rank Mean","Rank Sum")

            print(ranks)

        })

        output$KW.ranking.out <- renderPrint({
            KW.ranking()
        })



    # Box plot

       KW.boxPlot <- function(){
            dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))

            boxplot(dat[,2] ~ dat[,1], las=1)
            beeswarm(dat[,2] ~ dat[,1], col = 4, pch = 16, vert = TRUE,  add = TRUE)

        }

        output$KW.boxPlot <- renderPlot({
            print(KW.boxPlot())
        })



    # Kruskal-Wallis test

        KW.test <- reactive({

            dat <- read.csv(text=input$text3, sep="", na.strings=c("","NA","."))

            result <- kruskal.test(dat[,2] ~ dat[,1])
            print(result)
            
            z <- qnorm(1 - (result$p.value/2))    # p to z
            esR <- abs(z)/sqrt(nrow(dat))         # z to r
            # 95%CI of r
            esR.CI <- r.con(esR, nrow(dat), p =.95, twotailed=TRUE)
            
            cat("Effect size r [95% CI] =", round(esR, 3), "[", round(esR.CI, 3), "]", "\n")
            cat("  *Converted from p-value (p -> z -> r)", "\n", "\n", "\n")
            
            #eta2 <- result$statistic[[1]]/(length(dat[,1])-1)
            
            #cat("Effect size (eta-squared) =", sprintf("%.3f",round(eta2,4)), "\n")
            #cat("*Kruskal-Wallis chi-squared / (sample size - 1)", "\n", "\n")
            
        # pair-wise comparisons
            cat("=============================================================", "\n")
            cat("\n", "Pairwise comparisons (Test statistics and effect sizes)", "\n", "\n")
            cat("=============================================================", "\n", "\n")


            U.test <- function(x, y, correct = TRUE) # this is used in "pairWiseU"
            {
                x <- x[!is.na(x)]
                y <- y[!is.na(y)]
                n1 <- length(x)
                n2 <- length(y)
                n <- n1+n2
                xy <- c(x, y)
                r <- rank(xy)
                U1 <- n1*n2+n1*(n1+1)/2-sum(r[1:n1])
                tie <- table(r)
                U <- min(U1, n1*n2-U1) # U
                V <- n1*n2*(n^3-n-sum(tie^3-tie))/12/(n^2-n) # variance ties considered
                E <- n1*n2/2 # Expected
                z <- round(((U-E)-ifelse(correct, 0.5, 0))/sqrt(V),3)  # z-value
                EffectSize.r <- round(abs(z)/sqrt(n),3)
                P <- pnorm(abs(z), lower.tail=FALSE)*2
                return(structure(list(statistic=c(U=U, "E(U)"=E, "V(U)"=V, "Z-value"=z), p.value=P), class="htest"))
            }


            pairWiseU <- function(x,y) {

                uniqY <- unique(y)
                xx <- data.frame(x,y)
                yy <- unstack(xx)

                for (i in 1:length(uniqY)) {
                    for (j in 1:length(uniqY)) {
                        if (i >= j) {
                            next
                        } else {
                            x <- data.frame(yy[i])
                            y <- data.frame(yy[j])
                            n1 <- nrow(x)
                            n2 <- nrow(y)
                            n <- n1 + n2
                            resultU <- U.test(x, y, correct=FALSE)
                            r <- abs(resultU[[1]][[4]])/sqrt(n)
                            esR.CI <- r.con(r, n, p =.95, twotailed=TRUE)
                            
                            cat("Comparisons", colnames(x), "-", colnames(y), ":", "\n",
                            "Mann-Whitney's U:",sprintf("%.3f",round(resultU[[1]][1],4)),"\n",
                            "z-value:",sprintf("%.3f",round(resultU[[1]][4],4)), "\n",
                            #p-value (two-tailed without adjustment):",substr(sprintf("%.3f",round(resultU[[2]],4)),2,5), "\n",
                            "Effect size (r) [95% CI]=", sprintf("%.3f",r), "[", round(esR.CI, 3), "]", "\n",
                            "\n"
                            )

                        }
                    }
                }
            }

            pairWiseU(dat[,2], dat[,1])
            
            cat("\n")  
            cat("=============================================================", "\n")
            cat("\n", "Pairwise comparisons (p-values)", "\n", "\n")
            # cat("\n", "For details see, http://www.med.osaka-u.ac.jp/pub/kid/clinicaljournalclub1.html", "\n")
            cat("=============================================================", "\n")
            
            # Bonferroni
            cat("\n",
                "<< Bonferroni method >>", "\n")
            bon <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="bonferroni", exact=F, correct = F)
            print(bon)
            
            cat("\n",
                "--------------------------------------------------------", "\n")
            cat("\n",
                "<< Holm-Bonferroni method >>", "\n")
            # Holm
            holm <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="holm", exact=F, correct = F)
            print(holm)
            
            
            cat("\n",
                "--------------------------------------------------------", "\n")
            cat("\n",
                "<< False Discovery Rate >>", "\n")
            # false discovery rate
            fdr <- pairwise.wilcox.test(dat[,2], dat[,1], p.adj="fdr", exact=F, correct = F)
            print(fdr)
            
            
            cat("\n",
                "--------------------------------------------------------", "\n")
                
        })

        output$KW.test.out <- renderPrint({
            KW.test()
        })



    # Info
        KW.info <- reactive({
            info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
            info2 <- paste("It was executed on ", date(), ".", sep = "")
            cat(sprintf(info1), "\n")
            cat(sprintf(info2), "\n")
        })

        output$KW.info.out <- renderPrint({
            KW.info()
        })





#-----------------------------------------------------------------
# Friedman test (Differences between several related groups)
#-----------------------------------------------------------------
# For two treatments (k = 2), Friedman test is equivalent to the sign test (not "signed" rank test).
# Wilcoxon signed-rank test is better for k = 2 design.


    # Basic statistics

        Friedman.bs <- reactive({

            dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))
            describe(dat)

        })

        output$Friedman.bs.out <- renderPrint({
            Friedman.bs()
        })



    # Rank

        Friedman.ranking <- reactive({

            dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))

            dtf <- data.frame()
            for (i in 1:nrow(dat)){
                dtf <- rbind(dtf, rank(dat[i,]))
            }
            colnames(dtf) <- colnames(dat)

            Rank.Mean <- round(apply(dtf[,1:ncol(dtf)], 2, mean),2)
            print(Rank.Mean)

        })

        output$Friedman.ranking.out <- renderPrint({
            Friedman.ranking()
        })



    # Box plot

       Friedman.boxPlot <- function(){
            dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))

            boxplot(dat, las=1)
            beeswarm(dat, col = 4, pch = 16, vert = TRUE,  add = TRUE)

        }

        output$Friedman.boxPlot <- renderPlot({
            print(Friedman.boxPlot())
        })



    # Friedman test
    
        Friedman.test <- reactive({

            dat <- read.csv(text=input$text4, sep="", na.strings=c("","NA","."))

            result <-friedman.test(as.matrix(dat))

            print(result)

            z <- qnorm(1 - (result$p.value/2))     # p to z
            esR <- abs(z)/sqrt(nrow(dat))    # z to r
            # 95%CI of r
            esR.CI <- r.con(esR, nrow(dat), p =.95, twotailed=TRUE)
            
            cat("Effect size r [95% CI] =", round(esR, 3), "[", round(esR.CI, 3), "]", "\n")
            cat("  *Converted from p-value (p -> z -> r)", "\n", "\n", "\n")
            
            #eta2 <- result$statistic[[1]]/((length(dat[,1])*(length(dat[1,])-1)))
            #names(eta2) <- NULL

            #cat("Effect size (eta-squared) =", sprintf("%.3f",round(eta2,4)), "\n", "\n")
            

        # pair-wise comparisons
        cat("=============================================================", "\n")
        cat("\n", "Pairwise comparisons (Test statistics and effect sizes)", "\n", "\n")
        cat("=============================================================", "\n", "\n")
          
        pairWiseW <- function(x) {
          colnames(x) <- c(1:length(x))
          a <- as.numeric(colnames(x))
          
          for (i in 1:length(a)) {
            for (j in 1:length(a)) {
              if (i >= j) {
                next
              } else {
                
                res <- wilcox.test(x[,i], x[,j], paired=TRUE, correct=FALSE)
                pval <- res$p.value
                z <- qnorm(1-(pval/2))
                r1 <- z/sqrt(length(x[,i])*2)
                esR.CI1 <- r.con(r1, length(x[,i]*2), p =.95, twotailed=TRUE)
                
                #r2 <- z/sqrt(length(x[,1])-sum((x[,2]-x[,1])==0))
                
                cat("Comparisons", a[i], "-", a[j], ":", "\n",
                    "V =", res[[1]][1],"\n",
                    "z-value =",sprintf("%.3f",round(z,4)), "\n",
                    #"p-value =",sprintf("%.3f",round(pval,4)), "\n",
                    "Effect size r [95% CI] =", round(r1, 3), "[", round(esR.CI1, 3), "]", "\n", 
                    #"Effect eize r (without considering ties) =", round(r2, 3), "\n",
                    "\n")
              }
            }
          }
        }
        
        pairWiseW(dat)
        
        
        cat("\n") 
			  cat("=============================================================", "\n")
  			cat("\n", "Pairwise comparisons (p-values)", "\n", "\n")
            #cat("\n", "For details see, http://www.med.osaka-u.ac.jp/pub/kid/clinicaljournalclub1.html", "\n")
			  cat("=============================================================", "\n")

            x <- stack(dat)
            x1 <- x[,1]
            x2 <- x[,2]

         		# Bonferroni
         		cat("\n",
         		"<< Bonferroni method >>", "\n")
                  bon <- pairwise.wilcox.test(x1, x2, p.adj="bonferroni", exact=F, paired=T, correct = F)
            print(bon)
      
      			cat("\n",
        			"--------------------------------------------------------", "\n")
      			cat("\n",
      			"<< Holm-Bonferroni method >>", "\n")
      			# Holm
      			holm <- pairwise.wilcox.test(x1, x2, p.adj="holm", exact=F, paired=T, correct = F)
      			print(holm)
      
      
      			cat("\n",
        			"--------------------------------------------------------", "\n")
      			cat("\n",
      			"<< False Discovery Rate >>", "\n")
      			# false discovery rate
      			fdr <- pairwise.wilcox.test(x1, x2, p.adj="fdr", exact=F, paired=T, correct = F)
      			print(fdr)


            cat("\n",
            "--------------------------------------------------------", "\n", "\n")

 
        })

        output$Friedman.test.out <- renderPrint({
            Friedman.test()
        })



    # Info
        Friedman.info <- reactive({
            info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
            info2 <- paste("It was executed on ", date(), ".", sep = "")
            cat(sprintf(info1), "\n")
            cat(sprintf(info2), "\n")
        })

        output$Friedman.info.out <- renderPrint({
            Friedman.info()
        })



})
library(shiny)
library(shinyAce)



shinyUI(bootstrapPage(


    headerPanel("Non-parametric Tests"),


########## loading message #######################################

tags$head(tags$style(type="text/css", "
#loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 10px 0px 10px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),

conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),

###################################################################



    mainPanel(
        tabsetPanel(position = "left", 


# Mann-Whitney U-test (Comparing two independent conditions)

        tabPanel("Mann-Whitney U-test",

                h2("Mann-Whitney U-test"),

                h4("Comparing two independent conditions"),

                p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),

                p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),

                aceEditor("text1", value="Class\tScore\n1\t78\n1\t70\n1\t73\n1\t86\n1\t65\n1\t74\n1\t59\n1\t78\n1\t86\n1\t56\n1\t4\n1\t66\n1\t100\n1\t53\n1\t57\n2\t42\n2\t3\n2\t51\n2\t21\n2\t45\n2\t100\n2\t39\n2\t57\n2\t32\n2\t46\n2\t26\n2\t54\n2\t28\n2\t42\n2\t30", mode="r", theme="cobalt"),

                br(),

                h3("Basic statistics"),
                verbatimTextOutput("MWU.bs.out"),

                br(),

                h3("Ranks"),
                verbatimTextOutput("MWU.ranking.out"),

                br(),

                h3("Box plots with individual data points"),
                plotOutput("MWU.boxPlot", width="80%"),

                h3("Test result"),
                verbatimTextOutput("MWU.test.out"),

                br(),
                br(),

                strong('R session info'),
                verbatimTextOutput("MWU.info.out")

        ),






# Wilcoxon signed-rank test (Comparing two related conditions)

        tabPanel("Wilcoxon signed-rank test",

                h2("Wilcoxon signed-rank test"),

                h4("Comparing two related conditions"),

                p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),

                p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),

                aceEditor("text2", value="First\tSecond\n78\t42\n6\t3\n73\t51\n86\t21\n45\t45\n74\t98\n59\t59\n78\t57\n86\t84\n56\t46\n4\t26\n66\t54\n100\t28\n53\t42\n57\t30", mode="r", theme="cobalt"),

                br(),

                h3("Basic statistics"),
                verbatimTextOutput("WSR.bs.out"),

                br(),

                h3("Ranks"),
                verbatimTextOutput("WSR.ranking.out"),

                br(),

                h3("Box plots with individual data points"),
                plotOutput("WSR.boxPlot", width="80%"),

                h3("Test result"),
                verbatimTextOutput("WSR.test.out"),

                br(),
                br(),

                strong('R session info'),
                verbatimTextOutput("WSR.info.out")

        ),






# Kruskal-Wallis test (Differences between several independent groups)

        tabPanel("Kruskal-Wallis test",

                h2("Kruskal-Wallis test"),

                h4("Differences between several independent groups"),

                p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),

                p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),

                aceEditor("text3", value="Class\tScore\n1\t78\n1\t70\n1\t73\n1\t86\n1\t65\n1\t74\n1\t59\n1\t78\n1\t86\n1\t56\n1\t4\n1\t66\n1\t100\n1\t53\n1\t57\n2\t42\n2\t3\n2\t51\n2\t21\n2\t45\n2\t100\n2\t39\n2\t57\n2\t32\n2\t46\n2\t26\n2\t54\n2\t28\n2\t42\n2\t30\n3\t2\n3\t42\n3\t86\n3\t85\n3\t53\n3\t90\n3\t1\n3\t69\n3\t53\n3\t74\n3\t80\n3\t66\n3\t100\n3\t70\n3\t37",mode="r", theme="cobalt"),

                br(),

                h3("Basic statistics"),
                verbatimTextOutput("KW.bs.out"),

                br(),

                h3("Ranks"),
                verbatimTextOutput("KW.ranking.out"),

                br(),

                h3("Box plots with individual data points"),
                plotOutput("KW.boxPlot", width="80%"),

                h3("Test result"),
                verbatimTextOutput("KW.test.out"),

                br(),
                br(),

                strong('R session info'),
                verbatimTextOutput("KW.info.out")

        ),






# Friedman test (Differences between several related groups)

        tabPanel("Friedman test",

                h2("Friedman test"),

                h4("Differences between several related groups"),

                p('Note: Input values must be separated by tabs. Copy and paste from Excel/Numbers.'),

                p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>Your data needs to have the header (variable names) in the first row.</div></b>")),

                aceEditor("text4", value="First\tSecond\tThird\n78\t42\t42\n6\t3\t6\n73\t51\t86\n86\t21\t85\n45\t45\t53\n74\t98\t90\n59\t59\t1\n78\t57\t69\n86\t84\t53\n56\t46\t74\n4\t26\t80\n66\t54\t66\n100\t28\t100\n53\t42\t70\n57\t30\t37", mode="r", theme="cobalt"),

                br(),

                h3("Basic statistics"),
                verbatimTextOutput("Friedman.bs.out"),

                br(),

                h3("Ranks"),
                verbatimTextOutput("Friedman.ranking.out"),

                br(),

                h3("Box plots with individual data points"),
                plotOutput("Friedman.boxPlot", width="80%"),

                h3("Test result"),
                verbatimTextOutput("Friedman.test.out"),

                br(),
                br(),

                strong('R session info'),
                verbatimTextOutput("Friedman.info.out")

        ),




# About

        tabPanel("About",

            strong('Note'),
            p('This web application is developed with',
            a("Shiny.", href="http://www.rstudio.com/shiny/", target="_blank"),
            ''),

            br(),

            strong('List of Packages Used'), br(),
            code('library(shiny)'),br(),
            code('library(shinyAce)'),br(),
            code('library(psych)'),br(),
            code('library(beeswarm)'),br(),

            br(),

            strong('Code'),
            p('Source code for this application is based on',
            a('"The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012).', href='http://mizumot.com/handbook/', target="_blank")),

            p('The code for this web application is available at',
            a('GitHub.', href='https://github.com/mizumot/npt', target="_blank")),

            p('If you want to run this code on your computer (in a local R session), run the code below:',
            br(),
            code('library(shiny)'),br(),
            code('runGitHub("npt","mizumot")')
            ),

            br(),

            strong('Citation in Publications'),
                p('Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp'),

            br(),

            strong('Article'),
                p('Mizumoto, A., & Plonsky, L. (2015).', a("R as a lingua franca: Advantages of using R for quantitative research in applied linguistics.", href='http://applij.oxfordjournals.org/content/early/2015/06/24/applin.amv025.abstract', target="_blank"), em('Applied Linguistics,'), 'Advance online publication. doi:10.1093/applin/amv025'),

            br(),

            strong('Recommended'),
            p('To learn more about R, I suggest this excellent and free e-book (pdf),',
            a("A Guide to Doing Statistics in Second Language Research Using R,", href="http://cw.routledge.com/textbooks/9780805861853/guide-to-R.asp", target="_blank"),
            'written by Dr. Jenifer Larson-Hall.'),

            p('Also, if you are a cool Mac user and want to use R with GUI,',
            a("MacR", href="https://sites.google.com/site/casualmacr/home", target="_blank"),
            'is defenitely the way to go!'),

            br(),

            strong('Author'),
            p(a("Atsushi MIZUMOTO,", href="http://mizumot.com", target="_blank"),' Ph.D.',br(),
            'Associate Professor of Applied Linguistics',br(),
            'Faculty of Foreign Language Studies /',br(),
            'Graduate School of Foreign Language Education and Research,',br(),
            'Kansai University, Osaka, Japan'),

            br(),

            a(img(src="http://i.creativecommons.org/p/mark/1.0/80x15.png"), target="_blank", href="http://creativecommons.org/publicdomain/mark/1.0/"),

            p(br())

        )
    )
    )
))
Code license: GPL-3