使用 ggplot2 在 Rshiny 中未显示绘图，但是没有错误

如何解决使用 ggplot2 在 Rshiny 中未显示绘图，但是没有错误

大家好，

我正在尝试使用 Rshiny 创建一个网页。它应该使用 ui 中的输入变量显示交互式绘图。当我尝试运行该应用程序时，它不会出现任何错误，但它也不会向我显示情节。你们有谁知道为什么它似乎没有显示情节？我相信我确实在 ui 和服务器中都指定了输出..我相信故障出在数据过滤（服务器）的某个地方。起初它过滤了我的数据，后来它突然不再起作用了。我真的不知道如何解决这个问题。

非常感谢任何帮助。

library(shiny)
  library(dplyr)
  library(ggplot2)
  
  
  df <- read.csv("data_merged.csv",header = TRUE,sep = ";")
  
  summarySE <- function(data=NULL,measurevar,groupvars=NULL,na.rm=TRUE,conf.interval=.95,.drop=TRUE) {
    # New version of length which can handle NA's: if na.rm==T,don't count them
    length2 <- function (x,na.rm=FALSE) {
      if (na.rm) sum(!is.na(x))
      else       length(x)
    }
    
    # This is does the summary; it's not easy to understand...
    datac <- ddply(data,groupvars,.drop=.drop,.fun= function(xx,col,na.rm) {
                     c( N    = length2(xx[,col],na.rm=na.rm),mean = mean   (xx[,sd   = sd     (xx[,na.rm=na.rm)
                     )
                   },na.rm
    )
    
    # Rename the "mean" column    
    datac <- rename(datac,c("mean"=measurevar))
    
    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
    
    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g.,if conf.interval is .95,use .975 (above/below),and use df=N-1
    ciMult <- qt(conf.interval/2 + .5,datac$N-1)
    datac$ci <- datac$se * ciMult
    
    return(datac)
  }
  
 
  normDataWithin <- function(data=NULL,idvar,betweenvars=NULL,na.rm=FALSE,.drop=TRUE) {
    # Measure var on left,idvar + between vars on right of formula.
    data.subjMean <- ddply(data,c(idvar,betweenvars),.fun = function(xx,na.rm) {
                             c(subjMean = mean(xx[,na.rm=na.rm))
                           },na.rm
    )
    
    # Put the subject means with original data
    data <- merge(data,data.subjMean)
    
    # Get the normalized data in a new column
    measurenormedVar <- paste(measurevar,"normed",sep="")
    data[,measurenormedVar] <- data[,measurevar] - data[,"subjMean"] +
      mean(data[,measurevar],na.rm=na.rm)
    
    # Remove this subject mean column
    data$subjMean <- NULL
    
    return(data)
  }
  
 
  summarySEwithin <- function(data=NULL,withinvars=NULL,idvar=NULL,.drop=TRUE) {
    
    # Ensure that the betweenvars and withinvars are factors
    factorvars <- sapply(data[,c(betweenvars,withinvars),drop=FALSE],FUN=is.factor)
    if (!all(factorvars)) {
      nonfactorvars <- names(factorvars)[!factorvars]
      message("Automatically converting the following non-factors to factors: ",paste(nonfactorvars,collapse = ","))
      data[nonfactorvars] <- lapply(data[nonfactorvars],factor)
    }
    
    # norm each subject's data    
    data <- normDataWithin(data,betweenvars,na.rm,.drop=.drop)
    
    # This is the name of the new column
    measurenormedVar <- paste(measurevar,sep="")
    
    # Replace the original data column with the normed one
    data[,measurevar] <- data[,measurenormedVar]
    
    # Collapse the normed data - Now we can treat between and within vars the same
    datac <- summarySE(data,groupvars=c(betweenvars,na.rm=na.rm,conf.interval=conf.interval,.drop=.drop)
    
    # Apply correction from Morey (2008) to the standard error and confidence interval
    #  Get the product of the number of conditions of within-S variables
    nWithinGroups    <- prod(sapply(datac[,withinvars,FUN=nlevels))
    correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
    
    # Apply the correction factor
    datac$sd <- datac$sd * correctionFactor
    datac$se <- datac$se * correctionFactor
    datac$ci <- datac$ci * correctionFactor
    
    return(datac)
  }
  
  ui <- fluidPage(
    titlePanel("                                       "),sidebarPanel(
      checkBoxGroupInput("gender","Gender",choices = list("Man" = "Man","Vrouw" = "Vrouw","Beiden" = "Beiden"),selected = "Beiden"),sliderInput("age","Leeftijd",min = 0,max = 100,value = c(18,100)),selectInput("province",h3("Provincie"),choices = list("Utrecht","Brabant","Drenthe","Flevoland","Friesland","Gelderland","Groningen","Limburg","Noord-Holland","Overijssel","Zeeland","Zuid-Holland","Allemaal"),selected = "Allemaal")),mainPanel(
      h1("Gewoonteverandering in Nederland ten tijden van de SARS-Covid-19 pandemie",align = "center"),p("hier komt informatie over het onderzoek",column(3,selectInput("v1","Selecteer een gewoonte",choices = list("handen wassen bij thuiskomst" = 1,"Handen wassen voor het eten" = 2,"Handen wassen na een toiletbezoek" = 3,"Handen wassen in het algemeen" = 4,"Afstand houden met gasten thuis" = 5,"Afstand houden met vrienden buiten" = 6,"Afstand houden met vreemden in de supermarkt" = 7,"Afstand houden in het algemeen" = 8),selected = 1),column(9,plotOutput("plot1")
      )
    )
  )
  )
  
  
  server <- function(input,output,session){
    
    s <- reactive({
      age <- as.numeric(input$age)
      df2 <- df %>% filter(between(Age,age[1],age[2]))
      if (input$gender != "Beiden")
        df2 <- filter(df2,Gender == input$gender)
      if (input$province != "Allemaal")
        df2 <- filter(df2,Province %in% input$province)
      df2
    })
 
    renderPlot(
    if(input$v1 == 1){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,wave=df2$wave,compliance=df2$compliance_h1),data.frame(ppn=df2$ppn,compliance=df2$compliance_h1))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",nrow(df2)),rep("hand-washing (eating)",rep("hand-washing (toilet)",rep("1.5m distance (home)",rep("1.5m distance (outside)",rep("1.5m distance (shopping)",nrow(df2)))
      df_compliance2 <- summarySE(df_compliance,measurevar="compliance",groupvars=c("wave","behavior"))
      
      output$plot1 <- renderPlot(
        ggplot(df_compliance2,aes(x = wave,y = compliance_h1)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=compliance_h1-1.96*se,ymax=compliance_h1+1.96*se),size=1,width=0.3) + theme_light() + ylim(1,9) + scale_x_continuous(limits=c(0.5,10.5),breaks=seq(1,10,1)) +
          scale_linetype_manual(values=c(rep("solid",3),rep("dashed",3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if (input$v1 == 2){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$compliance_h2),compliance=df2$compliance_h2))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if(input$v1 == 3){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$compliance_h3),compliance=df2$compliance_h3))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",nrow(df2),nrow(df2))))
      df_compliance2 <- summarySE(df_compliance,y = compliance_h3)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=compliance_h3-1.96*se,ymax=compliance_h3+1.96*se),3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if(input$v1 == 4){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$norm_h),compliance=df2$norm_h))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",y = norm_h)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=norm_h-1.96*se,ymax=norm_h+1.96*se),3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if (input$v1 == 5){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$compliance_d1),compliance=df2$compliance_d1))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",y = compliance_d1)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=compliance_d1-1.96*se,ymax=compliance_d1+1.96*se),3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if(input$v1 == 6){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$compliance_d2),compliance=df2$compliance_d2))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",y = compliance_d2)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=compliance_d2-1.96*se,ymax=compliance_d2+1.96*se),3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)"))
    }else if(input$v1 == 7){
      df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$compliance_d3),compliance=df2$compliance_d3))
      df_compliance$behavior <- c(rep("hand-washing (returning home)",y = compliance_d3)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=compliance_d3-1.96*se,ymax=compliance_d3+1.96*se),3))) +
          theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
          theme(legend.text=element_text(size=18)) +
          theme(legend.title=element_blank()) + 
          theme(axis.text.x = element_text(size = 18)) + 
          theme(axis.text.y = element_text(size = 18)) +
          xlab("Wave") + ylab("Behavior (rated compliance)")) 
    }else if(input$v1 == 8){
        df_compliance <- rbind(data.frame(ppn=df2$ppn,compliance=df2$norm_d),compliance=df2$norm_d))
        df_compliance$behavior <- c(rep("hand-washing (returning home)",nrow(df2)))
        df_compliance2 <- summarySE(df_compliance,"behavior"))
        
        output$plot1 <- renderPlot(
          ggplot(df_compliance2,y = norm_d)) + geom_point(size=3) + geom_line() + geom_errorbar(aes(ymin=norm_d-1.96*se,ymax=norm_d+1.96*se),1)) +
            scale_linetype_manual(values=c(rep("solid",3))) +
            theme(axis.title=element_text(size=18)) +  theme(legend.position="bottom") + 
            theme(legend.text=element_text(size=18)) +
            theme(legend.title=element_blank()) + 
            theme(axis.text.x = element_text(size = 18)) + 
            theme(axis.text.y = element_text(size = 18)) +
            xlab("Wave") + ylab("Behavior (rated compliance)"))  
    }                                                                                                           
    )
  }

shinyApp(ui,server)