R语言Shiny应用新增数据库无对应日期的绘图条件判断逻辑

如下Shiny代码目前可针对数据库date2字段包含的日期（即2021-07-01、2021-07-02、2021-07-04）生成对应图表，当前日历组件虽已配置禁用不存在日期的逻辑，但存在配置范围错误导致禁用失效的问题，现需新增兜底判断逻辑：当所选日期不存在于数据库中时，执行指定代码段的逻辑，生成仅含m值对应水平线、无数据点的图表。

要求执行的逻辑代码 #

if (nrow(datas)<=2){
    abline(h=m,lwd=2) 
    points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,m+ .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")
}

需要修改两个核心位置：

• 修正日历组件的禁用日期范围错误，避免可选到不在date2范围内的日期
• 在绘图函数f1最开头新增日期存在性判断，作为兜底逻辑，不存在时直接生成仅含水平线的空图表

修改后完整可运行代码 #

library(shiny)
library(shinythemes)
library(dplyr)
library(tidyverse)
library(lubridate)
library(stringr)

function.test<-function(){
  
  df1 <- structure(
    list(date1= c("2021-06-28","2021-06-28","2021-06-28"),
         date2 = c("2021-07-01","2021-07-02","2021-07-04"),
         Category = c("ABC","ABC","ABC"),
         Week= c("Wednesday","Wednesday","Wednesday"),
         DR1 = c(4,1,0),
         DR01 = c(4,1,0), DR02= c(4,2,0),DR03= c(9,5,0),
         DR04 = c(5,4,0),DR05 = c(5,4,0),DR06 = c(5,4,0),DR07 = c(5,4,0),DR08 = c(5,4,0)),
    class = "data.frame", row.names = c(NA, -3L))
  return(df1)
}

f1 <- function(df1, dmda, CategoryChosse) {
  # 新增：判断所选日期是否存在于数据库中
  target_date <- ymd(dmda)
  exist_dates <- ymd(df1$date2)
  if (!target_date %in% exist_dates) {
    # 获取所选日期对应的星期
    target_week <- weekdays(target_date)
    # 计算对应Category和星期的m值
    m <- df1 %>%
      group_by(Category,Week) %>%
      summarize(across(starts_with("DR1"), mean),.groups = "drop") %>%
      filter(Week == target_week, Category == CategoryChosse) %>%
      pull(DR1)
    # 绘制空画布
    plot(1, type = "n", xlim = c(0, 10), ylim = c(0, max(m + 10, 35)),
         xaxs = 'i', xlab = "Days", ylab = "Numbers",
         main = paste0(dmda, "-", CategoryChosse, " (无匹配数据)"))
    # 执行要求的水平线逻辑
    abline(h=m,lwd=2) 
    points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,m+ .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")
    return()
  }
  
  x<-df1 %>% select(starts_with("DR0"))
  x<-cbind(df1, setNames(df1$DR1 - x, paste0(names(x), "_PV")))
  PV<-select(x, date2,Week, Category, DR1, ends_with("PV"))
  
  med<-PV %>%
    group_by(Category,Week) %>%
    summarize(across(ends_with("PV"), median),.groups = "drop")
  
  SPV<-df1%>%
    inner_join(med, by = c('Category', 'Week')) %>%
    mutate(across(matches("^DR0\\d+$"), ~.x + 
                    get(paste0(cur_column(), '_PV')),
                  .names = '{col}_{col}_PV')) %>%
    select(date1:Category, DR01_DR01_PV:last_col())
  
  SPV<-data.frame(SPV)
  
  mat1 <- df1 %>%
    filter(date2 == dmda, Category == CategoryChosse) %>%
    select(starts_with("DR0")) %>%
    pivot_longer(cols = everything()) %>%
    arrange(desc(row_number())) %>%
    mutate(cs = cumsum(value)) %>%
    filter(cs == 0) %>%
    pull(name)
  
  dropnames <- paste0(mat1,"_",mat1, "_PV")
  
  SPV <- SPV %>%
    filter(date2 == dmda, Category == CategoryChosse) %>%
    select(-any_of(dropnames))
  
  if(length(grep("DR0", names(SPV))) == 0) {
    SPV[head(mat1,10)] <- NA_real_
  }
  
  datas <-SPV %>%
    filter(date2 == ymd(dmda)) %>%
    group_by(Category) %>%
    summarize(across(starts_with("DR0"), sum),.groups = "drop") %>%
    pivot_longer(cols= -Category, names_pattern = "DR0(.+)", values_to = "val") %>%
    mutate(name = readr::parse_number(name))
  colnames(datas)[-1]<-c("Days","Numbers")
  
  
  datas <- datas %>% 
    group_by(Category) %>% 
    slice((as.Date(dmda) - min(as.Date(df1$date1) [
      df1$Category == first(Category)])):max(Days)+1) %>%
    ungroup
  
  m<-df1 %>%
    group_by(Category,Week) %>%
    summarize(across(starts_with("DR1"), mean),.groups = "drop")
  
  m<-subset(m, Week == df1$Week[match(ymd(dmda), ymd(df1$date2))] & Category == CategoryChosse)$DR1
  
  maxrange <-  range(min(0, datas$Numbers, na.rm = TRUE), na.rm = TRUE)
  maxrange[2] <- maxrange[2] - (maxrange[2] %%10) + 35
  
  max<-max(0, datas$Days, na.rm = TRUE)+1
  
  plot(Numbers ~ Days,  xlim= c(0,max),  ylim= c(0,maxrange[2]),
       xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))
  
  if (nrow(datas)<=2){
    abline(h=m,lwd=2) 
    points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,m+ .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")}
  
  else if(any(table(datas$Numbers) >= 3) & length(unique(datas$Numbers)) == 1){
    yz <- unique(datas$Numbers)
    lines(c(0,datas$Days), c(yz, datas$Numbers), lwd = 2)
    points(0, yz, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,yz+ .5,round(yz,1), cex=1.1,pos=4,offset =1,col="black")}
  
  else{
    mod <- nls(Numbers ~ b1*Days^2+b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
    new.data <- data.frame(Days = with(datas, seq(min(Days),max(Days),len = 45)))
    new.data <- rbind(0, new.data)
    lines(new.data$Days,predict(mod,newdata = new.data),lwd=2)
    coef<-coef(mod)[2]
    points(0, coef, col="red",pch=19,cex = 2,xpd=TRUE)
    text(.99,coef + 1,max(0, round(coef,1)), cex=1.1,pos=4,offset =1,col="black")
  }
  
}


ui <- fluidPage(
  
  shiny::navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
                          br(),
                          
                          tabPanel("",
                                   sidebarLayout(
                                     sidebarPanel(
                                       
                                       uiOutput("date"),
                                       uiOutput("mycode"),

                                       br(),
                                       
                                       
                                     ),
                                     
                                     mainPanel(
                                       tabsetPanel(
                                         tabPanel("", plotOutput("graph",width = "100%", height = "600")
                                         )
                                       ),
                                     ))
                          )))


server <- function(input, output,session) {
  
  data <- reactive(function.test())
  
  output$date <- renderUI({
    req(data())
    # 修正：日期范围改为覆盖date2的实际范围，避免禁用逻辑失效
    all_dates <- seq(min(ymd(data()$date2))-3, max(ymd(data()$date2))+3, by = "day")
    disabled <- as.Date(setdiff(all_dates, as.Date(data()$date2)), origin = "1970-01-01")
    dateInput(input = "date2", 
              label = h4("Data"),
              min = min(data()$date2),
              value = min(data()$date2),
              format = "dd-mm-yyyy",
              datesdisabled = disabled)
    
  })
  
  output$mycode <- renderUI({
    req(input$date2)
    df1 <- data()
    # 新增：如果所选日期不存在，也显示可选Category，避免下拉框为空
    df2 <- df1[as.Date(df1$date2) %in% input$date2,]
    if(nrow(df2) == 0) {
      choices = unique(df1$Category)
    } else {
      choices = unique(df2$Category)
    }
    selectInput("code", label = h4("Category"),choices=choices)
  })
  
  output$graph <- renderPlot({
    req(input$date2,input$code)
    f1(data(),as.character(input$date2),as.character(input$code))
  })
  
}

shinyApp(ui = ui, server = server)

修改说明 #

• 修正了原代码中日历组件日期范围设置错误的问题，原范围设置为2021年1月，和实际date2的7月范围不匹配，导致禁用逻辑完全失效
• 在f1函数开头新增了日期存在性兜底判断，即使后续数据库更新或者前端逻辑有漏洞，只要所选日期不存在就会直接生成仅含m值水平线的图表
• 优化了Category下拉框的逻辑，日期不存在时也会显示所有可选的Category，保证界面交互正常
• 新增了无数据时图表标题的提示，方便用户识别当前状态

内容的提问来源于stack exchange，提问作者Antonio