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| library(shiny) | |
| shinyServer(function(input,output,session){ | |
| dt <- reactive({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, | |
| sep=input$sep) | |
| }) | |
| observe({ | |
| updateSelectInput(session, "variable", choices=names(dt())) | |
| }) | |
| observeEvent(input$variable, { | |
| column_levels <- as.character(sort(unique(dt()[[input$variable]]))) | |
| updateSelectInput(session, "niveles", choices = column_levels) | |
| }) | |
| output$inputData <- renderTable({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, | |
| sep=input$sep) | |
| dt | |
| }) | |
| output$appPlot <- renderPlot({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, | |
| sep=input$sep) | |
| Niveles <- na.omit(dt[, input$variable]) # Para sacar los NA de la variable | |
| tabla <- table(Niveles) | |
| ptabla <- prop.table(tabla) | |
| xx <- barplot(ptabla, las=1, col='deepskyblue3', | |
| ylab='Frecuencia relativa', | |
| xlab='Niveles', ylim=c(0, max(ptabla)+0.1)) | |
| text(x=xx, y=ptabla, pos=3, cex=0.8, col="black", | |
| label=round(ptabla, 4)) | |
| }) | |
| output$consolidado <- renderTable({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, | |
| sep=input$sep) | |
| y <- na.omit(dt[, input$variable]) # Para sacar los NA de la variable | |
| tabla <- table(y) | |
| x <- tabla[input$niveles] | |
| n <- sum(tabla) | |
| res <- cbind(x, n, x/n) | |
| colnames(res) <- c('N煤mero de 茅xitos', | |
| 'N煤mero de casos', | |
| 'Proporci贸n observada') | |
| res | |
| }, align='c', bordered = TRUE, digits=4) | |
| output$resul1 <- renderText({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, sep=input$sep) | |
| dt <- na.omit(dt) # Para eliminar obs con NA | |
| y <- na.omit(dt[, input$variable]) # Para sacar los NA de la variable | |
| tabla <- table(y) | |
| x <- tabla[input$niveles] | |
| n <- sum(tabla) | |
| ph <- prop.test(x=x, n=n, alternative=input$h0, | |
| conf.level=input$alfa, p=input$p0, | |
| correct=input$correct) | |
| ph$statistic <- sign(ph$estimate - ph$null.value) * sqrt(ph$statistic) | |
| paste0('El estad铆stico de prueba es z0=', round(ph$statistic, 4), | |
| ' con un valor-P de ', round(ph$p.value, 2), '.') | |
| }) | |
| output$resul2 <- renderText({ | |
| inFile <- input$file1 | |
| if(is.null(inFile)) | |
| dt <- read.table('datos.txt', header=T, sep='\t') | |
| else dt <- read.csv(inFile$datapath, header=input$header, sep=input$sep) | |
| dt <- na.omit(dt) # Para eliminar obs con NA | |
| y <- na.omit(dt[, input$variable]) # Para sacar los NA de la variable | |
| tabla <- table(y) | |
| x <- tabla[input$niveles] | |
| n <- sum(tabla) | |
| ph <- prop.test(x=x, n=n, alternative=input$h0, | |
| conf.level=input$alfa, p=input$p0, | |
| correct=input$correct) | |
| intervalo <- paste("(", round(ph$conf.int[1], digits=4), | |
| ", ", | |
| round(ph$conf.int[2], digits=4), | |
| ").", sep='') | |
| paste0('El intervalo de confianza del ', 100*input$alfa, | |
| '% para proporci贸n poblacional es ', | |
| intervalo) | |
| }) | |
| }) | |