Update server.R

server.R

@@ -1,23 +1,114 @@
-function(input, output, session) {
+library(shiny)
+library(ggplot2)
 
-  # Combine the selected variables into a new data frame
-  selectedData <- reactive({
-    iris[, c(input$xcol, input$ycol)]
-  })
+source("www/auxiliar_functions.R")
 
-  clusters <- reactive({
-    kmeans(selectedData(), input$clusters)
-  })
+# Define server logic required to draw a histogram
+shinyServer(function(input, output) {
 
-  output$plot1 <- renderPlot({
-    palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
-      "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
+    output$distPlot <- renderPlot({
+      
+      if (input$Distribucion == "Normal") {
+        p <- ggplot() + 
+          geom_function(colour="indianred1", fun=dnorm, lwd=2,
+                        args=list(mean=input$mu_normal, sd=input$sigma)) + 
+          xlim(input$min_normal, input$max_normal) +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Density", 
+               title=bquote(paste("X ~ N(",
+                                  mu, "=", .(input$mu_normal),
+                                  ", ",
+                                  sigma^2, "=", .(input$sigma), ")")))
+        print(p)
+      }
+      
+      if (input$Distribucion == "Gamma") {
+        p <- ggplot() + 
+          geom_function(color="dodgerblue2", fun=dgamma_glm, lwd=2,
+                        args=list(mu=input$mu_gamma, phi=input$phi_gamma)) + 
+          xlim(input$min_gamma, input$max_gamma) +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Density", 
+               title=bquote(paste("X ~ Gamma(",
+                                  mu, "=", .(input$mu_gamma),
+                                  ", ",
+                                  phi, "=", .(input$phi_gamma), ")")))
+        print(p)
+      }
+      
+      if (input$Distribucion == "Inv. gaussian") {
+        p <- ggplot() + 
+          geom_function(color="darkslategray3", fun=dinvgaus_glm, lwd=2,
+                        args=list(mu=input$mu_invgaus, phi=input$phi_invgaus)) + 
+          xlim(input$min_invgaus, input$max_invgaus) +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Density", 
+               title=bquote(paste("X ~ InvGaussian(",
+                                  mu, "=", .(input$mu_invgaus),
+                                  ", ",
+                                  phi, "=", .(input$phi_invgaus), ")")))
+        print(p)
+      }
+      
+      if (input$Distribucion == "Binomial") {
+        xs <- seq(from=0, to=input$m)
+        ys <- dbinom(x=xs, size=input$m, prob=input$mu_binom)
+        
+        p <- ggplot(data=data.frame(x=xs, y=ys,
+                                    yend=rep(0, input$m+1)),
+                    aes(x=x, y=y, xend=x, yend=yend)) + 
+          geom_point(colour="mediumpurple1") +
+          geom_segment(colour="mediumpurple1") +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Probability", 
+               title=bquote(paste("X ~ Binomial(",
+                                  n, "=", .(input$m),
+                                  ", ",
+                                  mu, "=", .(input$mu_binom), ")")))
+        print(p)
+      }
+      
+      if (input$Distribucion == "Poisson") {
+        xs <- seq(from=0, to=input$max_pois)
+        ys <- dpois(x=xs, lambda=input$mu_pois)
+        
+        p <- ggplot(data=data.frame(x=xs, y=ys,
+                                    yend=rep(0, input$max_pois+1)),
+                    aes(x=x, y=y, xend=x, yend=yend)) + 
+          geom_point(colour="lightsalmon2") +
+          geom_segment(colour="lightsalmon2") +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Probability", 
+               title=bquote(paste("X ~ Poisson(",
+                                  mu, "=", .(input$mu_pois),
+                                  # ", ",
+                                  # mu, "=", .(input$mu_binom), 
+                                  ")")))
+        print(p)
+      }
+      
+      if (input$Distribucion == "Negative binomial") {
+        xs <- seq(from=0, to=input$max_negbin)
+        ys <- dnbinom(x=xs, mu=input$mu_negbin, size=input$k)
 
-    par(mar = c(5.1, 4.1, 0, 1))
-    plot(selectedData(),
-         col = clusters()$cluster,
-         pch = 20, cex = 3)
-    points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
-  })
+        p <- ggplot(data=data.frame(x=xs, y=ys,
+                                    yend=rep(0, input$k+1)),
+                    aes(x=x, y=y, xend=x, yend=yend)) + 
+          geom_point(colour="olivedrab3") +
+          geom_segment(colour="olivedrab3") +
+          theme(legend.position = "none") + 
+          labs(x="X", y="Probability", 
+               title=bquote(paste("X ~ NegBin(",
+                                  mu, "=", .(input$mu_negbin),
+                                  ", ",
+                                  k, "=", .(input$k), ")")))
+        print(p)
+      }
+      
 
-}
+    }, res = 96)
+
+})
+
+
+