Non-parametric Tests

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

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( # 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(), '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()) ) ) ) ))