Unable to generate plots in R Shiny

Question

Below is my code to generate a Pareto from a data set, based on selected dates by the user. When I run the app, I am only able to generate the date selection but no graphs. Any ideas why I am not seeing a graph? I want a graph that updates when the date range updates. Thanks. I assume the code in my ui is fine to output code. I just can't seem to get the server to output my graph.

#load libraries
library(shiny)
library(readr)
library(dplyr)
library(ggplot2)
#read in data
Rawdata <- read_csv("Rawdata.csv")
#change time from chr to Date
Rawdata$timestamp= as.Date(Rawdata$timestamp, format= "%m/%d/%Y")

#defining a ui
ui <- fluidPage(

   # Application title
   titlePanel("COCBRN Pareto"),

dateRangeInput("daterange",
               label="Select the date range",
               start=min(Rawdata$timestamp),
               end=max(Rawdata$timestamp),
               min=min(Rawdata$timestamp),
               max=max(Rawdata$timestamp),
               format="mm/dd/yyyy",
               separator="to"
               ),

               textOutput("startdate"),
               textOutput("enddate"),
               textOutput("range"),
               tableOutput("subdata"),

#this is where I am trying to output the plot
mainPanel(plotOutput("pareto"))

)

#writing the server
server <- function(input, output,session) {

  output$startdate<-renderText({
    as.character(input$daterange[1])
  })

  output$enddate<-renderText({
    as.character(input$daterange[2])
  })

  output$range<-renderText({
    paste("Selected date range is", input$daterange[1], "to", input$daterange[2])
  })



  #Creating a vector to convert failure codes to text
  srp.codes<-c("Reset"= "00","Blower leakage due to incomplete adhesive joint"="11A", "Uncured adhesive" ="11B", "Motor to fan cover installation incorrect"="11C", "Hood nonconformance cosmetic"="13D", "Retest after first time failure"="13AA", "Hood leak @ unknown location"="13A", "Neckdam holes/tears"="13B", "Blower leakage due to inc adhesive joint"="18B", "Leakage at filter crimp"="18A", "Impeller gap incorrect"="16A", "Blower outlet threads stripped"="13AF", "Uncured adhesive"="18C","NPF at hood"="18V", "Perimeter seal leak"="13P", "Hole in hood (not in seal)"="13E", "Exhaust valve cover missing or installed incorrectly"="13M", "Air flow fails on low side of tolerance"="12LVL", "Low Flow Motor Wires Reversed"= "17A", "No Problem Found (Blower Test)"="12V", "Crimp Nonconformance"="18G")

  #removing a code from my data
  Data_no_00<-subset(Rawdata, code!="00")
  func_year<-as.numberic(format(input$daterange[1],"%Y"))
  func_month<-as.numberic(format(input$daterange[1],"%m"))
  func_days=c(as.numberic(format(input$daterange[1],"%d")):as.numberic(format(input$daterange[2],"%d")))

  #filtering data based on user input
  filt<-
    subset(Data_no_00,test==0& Year==func_year& Month==func_month &Day%in%func_days)%>%
    group_by(code)%>%
    summarise(freq=n())%>%
    arrange(desc(freq))

  #matching failure codes to text
  filt$code<-names(srp.codes)[match(filt$code, srp.codes)]


  plotting<-filt[1:5,]%>%
    mutate(relative_freq=freq/sum(freq), cumulative_freq=cumsum(relative_freq))
  the_order<- plotting$code



    p<-plotting%>%
    ggplot(aes(x=code, weight= relative_freq))+
    geom_bar(width=0.5,fill="blue")+
    scale_x_discrete(limits=the_order)+
    scale_y_continuous(label=scales::percent)+
    geom_point(aes(x=code,y=cumulative_freq))+
    geom_line(aes(x=code,y=cumulative_freq, group=1))+
    labs(x="",y="Relative Frequency", title= "February COCBRN Pareto 2019")+
    theme(plot.title=element_text(hjust=0.5))+
    theme(axis.text.x=element_text(angle=270))


    output$pareto<-renderPlot({p})


}

# Run the application 
shinyApp(ui = ui, server = server)

Answer 1

as MrFlick said above, the issue lies in the use of reactive elements. There would be many different ways to implement your code with reactive elements. This would be one:

server <- function(input, output,session) {

  output$startdate<-renderText({
    as.character(input$daterange[1])
  })

  output$enddate<-renderText({
    as.character(input$daterange[2])
  })

  output$range<-renderText({
    paste("Selected date range is", input$daterange[1], "to", input$daterange[2])
  })



  #Creating a vector to convert failure codes to text
  srp.codes<-c("Reset"= "00","Blower leakage due to incomplete adhesive joint"="11A", "Uncured adhesive" ="11B", "Motor to fan cover installation incorrect"="11C", "Hood nonconformance cosmetic"="13D", "Retest after first time failure"="13AA", "Hood leak @ unknown location"="13A", "Neckdam holes/tears"="13B", "Blower leakage due to inc adhesive joint"="18B", "Leakage at filter crimp"="18A", "Impeller gap incorrect"="16A", "Blower outlet threads stripped"="13AF", "Uncured adhesive"="18C","NPF at hood"="18V", "Perimeter seal leak"="13P", "Hole in hood (not in seal)"="13E", "Exhaust valve cover missing or installed incorrectly"="13M", "Air flow fails on low side of tolerance"="12LVL", "Low Flow Motor Wires Reversed"= "17A", "No Problem Found (Blower Test)"="12V", "Crimp Nonconformance"="18G")

  #removing a code from my data
  Data_no_00<-subset(Rawdata, code!="00")

  #filtering data based on user input
  filt<- reactive({
    func_year<-as.numberic(format(input$daterange[1],"%Y"))
    func_month<- as.numberic(format(input$daterange[1],"%m"))
    func_days <- c(as.numberic(format(input$daterange[1],"%d")):as.numeric(format(input$daterange[2],"%d")))

    df <- subset(Data_no_00,test==0& Year==func_year& Month==func_month &Day%in%func_days)%>%
      group_by(code)%>%
      summarise(freq=n())%>%
      arrange(desc(freq))

    df$code<-names(srp.codes)[match(df$code, srp.codes)]

    df
  })

  output$pareto<-renderPlot({

    #matching failure codes to text
    plotting<-filt()[1:5,]%>%
      mutate(relative_freq=freq/sum(freq), cumulative_freq=cumsum(relative_freq))
    the_order<- plotting$code


    p<-plotting%>%
      ggplot(aes(x=code, weight= relative_freq))+
      geom_bar(width=0.5,fill="blue")+
      scale_x_discrete(limits=the_order)+
      scale_y_continuous(label=scales::percent)+
      geom_point(aes(x=code,y=cumulative_freq))+
      geom_line(aes(x=code,y=cumulative_freq, group=1))+
      labs(x="",y="Relative Frequency", title= "February COCBRN Pareto 2019")+
      theme(plot.title=element_text(hjust=0.5))+
      theme(axis.text.x=element_text(angle=270))

    p
  })


}

In addition to the webinar MrFlick posted above you can also look at the example in the Shiny gallery that explains reactivity: https://shiny.rstudio.com/gallery/reactivity.html