Replace iris dataset with dataset that comes as csv input in shiny app and maintain the same funtionality

ggplot2

#1

Below I have a working app with theiris dataset. My actual dataset comes from a csv file into reactive context and is namedrt().The user also can choose different variables to correlate instead of only Sepal.Length andSepal.Width every time with input$lx1 and input$lx2. I would like to know how to replace iris with rt(), Sepal.Length with input$lx1 and Sepal.Width with input$lx2 in my code in order to make it work properly.

#ui.r
library(shiny)
library(ggplot2)
library(plotly)


fluidPage(

  # App title ----
  titlePanel(div("CROSS CORRELATION",style = "color:blue")),

  # Sidebar layout with input and output definitions ----
  sidebarLayout(

    # Sidebar panel for inputs ----
    sidebarPanel(

      # Input: Select a file ----
      fileInput("file1", "Input CSV-File",
                multiple = TRUE,
                accept = c("text/csv",
                           "text/comma-separated-values,text/plain",
                           ".csv")),

      # Horizontal line ----
      tags$hr(),

      # Input: Checkbox if file has header ----
      checkboxInput("header", "Header", TRUE),

      # Input: Select separator ----
      radioButtons("sep", "Separator",
                   choices = c(Comma = ",",
                               Semicolon = ";",
                               Tab = "\t"),
                   selected = ","),


      # Horizontal line ----
      tags$hr(),

      # Input: Select number of rows to display ----
      radioButtons("disp", "Display",
                   choices = c(Head = "head",
                               All = "all"),
                   selected = "head")





    ),
    # Main panel for displaying outputs ----
    mainPanel(

      tabsetPanel(type = "tabs",
                  tabPanel("Table",
                           shiny::dataTableOutput("contents")),
                  tabPanel("Correlation Plot",
                           tags$style(type="text/css", "
           #loadmessage {
                                      position: fixed;
                                      top: 0px;
                                      left: 0px;
                                      width: 100%;
                                      padding: 5px 0px 5px 0px;
                                      text-align: center;
                                      font-weight: bold;
                                      font-size: 100%;
                                      color: #000000;
                                      background-color: #CCFF66;
                                      z-index: 105;
                                      }
                                      "),conditionalPanel(condition="$('html').hasClass('shiny-busy')",
                                                          tags$div("Loading...",id="loadmessage")
                                      ),
                           fluidRow(
                             column(3, uiOutput("lx1")),
                           column(3,uiOutput("lx2"))),
                           hr(),
                           fluidRow(
                             tags$style(type="text/css",
                                        ".shiny-output-error { visibility: hidden; }",
                                        ".shiny-output-error:before { visibility: hidden; }"
                             ),
                           column(3,uiOutput("td")),
                           column(3,uiOutput("an"))),
                           fluidRow(
                           plotlyOutput("sc"))
      ))
  )))

#server.r
function(input, output) {
  rt<-reactive({
    req(input$file1)

    csvdata <- read.csv(input$file1$datapath,
                        header = input$header
    )
    if(input$disp == "head"){
      head(csvdata)
    } else{
      csvdata
    } 
    csvdata$Lex1=as.numeric(levels(csvdata$Lex1))[csvdata$Lex1]
    csvdata$Lex2=as.numeric(levels(csvdata$Lex2))[csvdata$Lex2]
    csvdata$Lex3=as.numeric(levels(csvdata$Lex3))[csvdata$Lex3]
    csvdata$Lex4=as.numeric(levels(csvdata$Lex4))[csvdata$Lex4]
    csvdata$Lex5=as.numeric(levels(csvdata$Lex5))[csvdata$Lex5]
    csvdata$Lex6=as.numeric(levels(csvdata$Lex6))[csvdata$Lex6]
    csvdata$Lex7=as.numeric(levels(csvdata$Lex7))[csvdata$Lex7]
    csvdata$Lex8=as.numeric(levels(csvdata$Lex8))[csvdata$Lex8]
    csvdata$Lex9=as.numeric(levels(csvdata$Lex9))[csvdata$Lex9]
    csvdata$Lex10=as.numeric(levels(csvdata$Lex10))[csvdata$Lex10]
    csvdata$Lex11=as.numeric(levels(csvdata$Lex11))[csvdata$Lex11]
    csvdata$Lex12=as.numeric(levels(csvdata$Lex12))[csvdata$Lex12]


    capture.output(csvdata[rowSums(is.na(csvdata)) > 0,],file = "Missing_genes.csv")


    row.has.na <- apply(csvdata, 1, function(x){any(is.na(x))})
    csvdata2 <- csvdata[!row.has.na,]

    csvdata2
  }) 

  output$contents <- shiny::renderDataTable({

iris  })


  output$lx1<-renderUI({
    selectInput("lx1", label = h4("Select 1st Expression Profile"), 
                choices = colnames(iris[,1:4]), 
                selected = "Lex1")
  })
  output$lx2<-renderUI({
    selectInput("lx2", label = h4("Select 2nd Expression Profile"), 
                choices = colnames(iris[,1:4]), 
                selected = "Lex2")
  })

  output$td<-renderUI({
    radioButtons("td", label = h4("Trendline"),
                 choices = list("Add Trendline" = "lm", "Remove Trendline" = ""), 
                 selected = "")
  })

  output$an<-renderUI({

    radioButtons("an", label = h4("Correlation Coefficient"),
                 choices = list("Add Cor.Coef" = cor(subset(iris, select=c(input$lx1)),subset(iris, select=c(input$lx2))), "Remove Cor.Coef" = ""), 
                 selected = "")
  })  


 output$sc<-renderPlotly({

   p1 <- ggplot(iris, aes_string(x = input$lx1, y = input$lx2,text = "Species"))+
     # Change the point options in geom_point
     geom_point(color = "darkblue") +
     # Change the title of the plot (can change axis titles
     # in this option as well and add subtitle)
     labs(title = "Cross Correlation") +
     # Change where the tick marks are
     scale_x_continuous(breaks = seq(0, 2.5, 30)) +
     scale_y_continuous(breaks = seq(0, 2.5, 30)) +
     # Change how the text looks for each element
     theme(title = element_text(family = "Calibri", 
                                size = 10, 
                                face = "bold"), 
           axis.title = element_text(family = "Calibri Light", 
                                     size = 16, 
                                     face = "bold", 
                                     color = "darkgrey"), 
           axis.text = element_text(family = "Calibri", 
                                    size = 11))+
     theme_bw()+
     geom_smooth(method = input$td)+
     annotate("text", x = 5, y = 5, label = as.character(input$an))+
     geom_text(data=subset(iris, Sepal.Length > 6),
               aes(Sepal.Length,Sepal.Width,label=Species))
   # get clicked point
   click_data <- event_data("plotly_click", source = "select")
   # if a point has been clicked, add a label to the plot
   if(!is.null(click_data)) {
     pos <- click_data$pointNumber+1
     label_data <- data.frame(x = iris$Sepal.Length[pos],
                              y = iris$Sepal.Width[pos],
                              label = iris$Species[pos],
                              stringsAsFactors = FALSE)
     p1 <<- p1 + 
       geom_text(data = label_data,
                 aes(x = x, y = y, label = label),
                 inherit.aes = FALSE, nudge_y=.1)
   }
   ggplotly(p1,source = "select", tooltip = c("text")) %>%
     layout(hoverlabel = list(bgcolor = "white", 
                              font = list(family = "Calibri", 
                                          size = 9, 
                                          color = "black")))

 }) 




}