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knitr::opts_chunk$set(echo = FALSE) library(knitr) library(corrplot) library(Hmisc) library(magrittr) library(kableExtra)
CORRELATION ANALYSIS
csvfile = reactive({ csvfile = input$file1 if (is.null(csvfile)) { return(NULL) } dt = read.csv(csvfile$datapath, header = input$header, sep = ",", check.names = FALSE) dt }) # output if (input$req1 == "correlation") { if (input$submit > 0) { a = as.vector(csvfile()[, input$dvar]) y = as.vector(csvfile()[, input$ivar]) x = cor.test(a, y, method = input$req, conf.level = as.numeric(input$ci), alternative = input$alt, exact = FALSE ) t_value = round(x$statistic, 3) correlation = round(x$estimate, 3) df = x$parameter pvalue = round(x$p.value, 3) alt.Hypothesis = x$alternative result = cbind(correlation, t_value, df, pvalue, alt.Hypothesis) nam = x$method rownames(result) = nam result = as.data.frame(result) kable(result, caption = "Correlation Analysis", row.names = FALSE) %>% kable_styling() %>% kable_paper("hover", full_width = F) } } if (input$req1 == "correlation") { if (input$submit > 0) { if (input$req == "pearson") { a = as.vector(csvfile()[, input$dvar]) y = as.vector(csvfile()[, input$ivar]) x = cor.test(a, y, method = input$req, conf.level = as.numeric(input$ci), alternative = input$alt, exact = FALSE ) ci = x$conf.int ci_nw = melt(ci, value.name = "Lower Limit and Upper limit") kable(round(ci_nw, 3), caption = "Confidence Interval", row.names = FALSE) %>% kable_styling() %>% kable_paper("hover", full_width = F) } } } if (input$req1 == "corrmat") { if (input$submit2 > 0) { x = as.data.frame(csvfile()[, input$selvar]) cormat = rcorr(as.matrix(x), type = input$req) R = round(cormat$r, 3) p = cormat$P ## Define notions for significance levels; spacing is important. mystars = ifelse(p < .001, "*** ", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " "))) Rnew = matrix(paste(R, mystars, sep = ""), ncol = ncol(x)) diag(Rnew) = paste(diag(R), " ", sep = "") row.names(Rnew) = names(x) colnames(Rnew) = names(x) kable(Rnew, caption = "Correlation Matrix") %>% kable_styling() %>% kable_paper("hover", full_width = F) } } tags$br() if (input$req1 == "corrmat") { if (input$submit2 > 0) { cat("*** Correlation is significant at 0.001 level (two tailed) \n** Correlation is significant at 0.01 level (two tailed)\n* Correlation is significant at 0.05 level (two tailed)") } } tags$br() if (input$req1 == "corrmat") { if (input$submit2 > 0) { x = as.data.frame(csvfile()[, input$selvar]) cormat = rcorr(as.matrix(x), type = input$req) correlmat1 = cormat$P row.names(correlmat1) = names(x) kable(round(correlmat1, 3), caption = "Matrix of P-values") %>% kable_styling() %>% kable_paper("hover", full_width = F) } } h3("")
package: grapesAgri1, Version; 1.0.0
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