Upload app.R

app.R

@@ -1,51 +1,107 @@
+#  Numerical simulation for a low-pass filter
+#  Nacho garcia 2018
+#  [email protected]
+#  GPL v3
+
+#Library loading
 library(shiny)
-library(bslib)
-library(dplyr)
 library(ggplot2)
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_fillable(theme = bs_theme(bootswatch = "minty"),
-  layout_sidebar(fillable = TRUE,
-    sidebar(
-      varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-      varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-      checkboxGroupInput("species", "Filter by species",
-        choices = unique(df$Species), selected = unique(df$Species)
+# Define UI
+ui <- fluidPage(
+   
+   # Application title
+   titlePanel("Low-pass Filter Numerical Simulator"),
+   
+   # Sidebar 
+   sidebarLayout(
+      sidebarPanel(
+        img(src='https://upload.wikimedia.org/wikipedia/commons/thumb/3/3b/RC_Divider.svg/200px-RC_Divider.svg.png', align = "center"),
+        sliderInput("res", "R value (AU):", 1,100,50),
+        sliderInput("cap", "C value (AU):", 1,100,50),
+        sliderInput("noise", "Noise Level (AU):", 1,100,50),
+        actionButton("do", "RUN", align = "center"),
+        
+        h6("G2C", align = "left"),
+
+
       ),
-      hr(), # Add a horizontal rule
-      checkboxInput("by_species", "Show species", TRUE),
-      checkboxInput("show_margins", "Show marginal plots", TRUE),
-      checkboxInput("smooth", "Add smoother"),
-    ),
-    plotOutput("scatter")
-  )
+      
+      # Plot
+      mainPanel(
+        
+        tabsetPanel(
+          tabPanel("Plot", plotOutput("Plot")),
+          tabPanel("Table", tableOutput("table")),
+          tabPanel("Summary", verbatimTextOutput("summary")),
+          tabPanel("Info", htmlOutput("help"))
+        )
+        
+        
+        
+        
+
+      )
+   )
 )
 
-server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
-  
-  output$scatter <- renderPlot({
-    p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) + list(
-      theme(legend.position = "bottom"),
-      if (input$by_species) aes(color=Species),
-      geom_point(),
-      if (input$smooth) geom_smooth()
-    )
-
-    if (input$show_margins) {
-      margin_type <- if (input$by_species) "density" else "histogram"
-      p <- p |> ggExtra::ggMarginal(type = margin_type, margins = "both",
-        size = 8, groupColour = input$by_species, groupFill = input$by_species)
+# Define server 
+server <- function(input, output) {
+   
+  observeEvent(input$do, {
+
+    Noise <- as.data.frame(1:200)
+    stoch<-input$noise * 0.002
+    IN <- runif(200, 0, stoch)
+    Signal.Noise <- cbind(Noise, IN)
+    Signal.Noise$Out<-0.01
+    r<- input$res *0.6
+    c<-input$cap *0.3
+
+    for (i in 2:200) {
+      
+      Signal.Noise$Out[i]<-((Signal.Noise$IN[i-1]-Signal.Noise$Out[i-1])/(r*c*(Signal.Noise$Out[i-1])))
+      Signal.Noise$Out[i]<-Signal.Noise$Out[i-1]+Signal.Noise$Out[i]
     }
     
-    p
-  }, res = 100)
+    colnames(Signal.Noise)<-c("Time(AU)","IN","Vout")
+
+    output$Plot <- renderPlot( { 
+      
+      p<-ggplot(Signal.Noise)+
+        geom_line(aes(x=`Time(AU)`, y=Vout), colour="red")+
+        geom_line(aes(x=`Time(AU)`, y=IN), colour="black")+
+        annotate("text", x=15, y=0.20, label= " Output", colour="red", size=5)+
+        annotate("text", x=15, y=0.19, label= "Input", colour="black", size=5)+
+        xlim(2,200)+
+        ylim(0,0.2)+
+        theme_minimal()
+      
+      print(p)
+      
+    })
+    
+    output$summary <- renderPrint({
+      summary(Signal.Noise)
+    })
+    
+    output$table <- renderTable({
+      Signal.Noise 
+    })
+    
+
+
+    
+    output$help <- renderUI({
+     HTML(paste("","","Electrical filters are combinations of electronic components that transform input signals into modified outputs and one of the most simple filters is the low-pass filter.", 
+"","Canonical low-pass filters consist of a resistor and a capacitor connected in parallel (see the scheme on the left) and they have been traditionally used to remove high frequencies from sigmoidal inputs. In this very specific situation, the cut-off frequencies can be calculated from the resistance and capacitance values and the behavior of the filter is very well understood; however, the role of low-pass filters in noise suppression has not been fully explored.",
+"",
+"We became interested in the relationship between low-pass filters and noise-suppression because all biological pathways are very reliable systems which need to effectively distinguish between noise and signal to provide fidelity in the response. Unfortunately, we found that the differential equations that describe the behavior of low-pass filters are not able to integrate the noise in the system in a useful way so to circumvent that problem We developed this simulation.", sep = "</br>"))
+    })
+    
+
+  })        
 }
+# Run the application 
+shinyApp(ui = ui, server = server)
 
-shinyApp(ui, server)