r - ggvis 与 Shiny 的集成

Question

这是一个相当简单的问题。我阅读了其他主题，发现要将 GGVIS 可视化插入 Shiny，您需要:

1. 在 ui.R 中 - 调用 ggvisOutput("EvolucionVisitas")
2. 在 server.R 中 - 使用函数 bind_shiny("EvolucionVisitas")

我在为“Evolución Visitas”选项卡绘制图表时遇到问题。我都做了，但我在某个地方失败了。

我的选项卡中没有打印任何内容:EvoluciónVisitas。其他一切正常。

这是我的数据:

structure(list(date = structure(1:31, .Label = c("2014-12-01", 
"2014-12-02", "2014-12-03", "2014-12-04", "2014-12-05", "2014-12-06", 
"2014-12-07", "2014-12-08", "2014-12-09", "2014-12-10", "2014-12-11", 
"2014-12-12", "2014-12-13", "2014-12-14", "2014-12-15", "2014-12-16", 
"2014-12-17", "2014-12-18", "2014-12-19", "2014-12-20", "2014-12-21", 
"2014-12-22", "2014-12-23", "2014-12-24", "2014-12-25", "2014-12-26", 
"2014-12-27", "2014-12-28", "2014-12-29", "2014-12-30", "2014-12-31"
), class = "factor"), sessions = c(1932L, 1828L, 2349L, 8192L, 
3188L, 3277L, 2846L, 2541L, 5434L, 4290L, 2059L, 2080L, 2111L, 
3776L, 1989L, 1844L, 3641L, 1283L, 1362L, 1568L, 2882L, 1212L, 
957L, 851L, 928L, 1435L, 1115L, 1471L, 1128L, 1022L, 768L), id = 1:31), .Names = c("date", 
"sessions", "id"), row.names = c(NA, -31L), drop = TRUE, class = c("tbl_df", 
"tbl", "data.frame"))

这是我的代码，谢谢。

ui.R

library(shiny)
library(ggvis)

# Define the overall UI
shinyUI(

  # Use a fluid Bootstrap layout
  fluidPage(    

    # Give the page a title
    br(),
    br(),
    titlePanel("Visitas por fuente"),

    # Generate a row with a sidebar
    sidebarLayout(      

      # Define the sidebar with one input



      sidebarPanel(
        dateRangeInput("dates", label = h3("Date range"),
                       start = "2014-12-01", end = "2014-12-31")

      ),


      mainPanel(
        tabsetPanel(
          tabPanel('Visitas por fuente',
                   plotOutput("VisitasFuente")),
          tabPanel('Evolución de las visitas',
                   ggvisOutput("EvolucionVisitas")),
          tabPanel('Comentarios',
                   dataTableOutput("Comentarios"))
        )

    )
  )
))

服务器.R

library(shiny)
library(ggvis)



Visitas_Por_Fuente <- read.csv("D:\\RCoursera\\Star-App-2\\Visitas_Por_Fuente_Dic.csv")
labelsF = c("Directo", "Email", "Referencias", "SEO", "Social Media", "Campañas", "Adwords")
Visitas_Por_Fuente$date <- as.Date(Visitas_Por_Fuente$date)
ComentariosDic <- read.csv("D:\\RCoursera\\Star-App-2\\ComentariosDic2014.csv",header = TRUE, sep = ";")
ComentariosDic$date <- as.Date(ComentariosDic$date)


shinyServer(


  function(input, output) {



    output$VisitasFuente <- renderPlot({

      # Filter the data based on user selection month     
      date_seq <- seq(input$dates[1], input$dates[2], by = "day")


      VisitasData <- filter(Visitas_Por_Fuente, date %in% date_seq  & Fuentes %in% labelsF)

      VisitasData <- VisitasData %>% group_by(Fuentes) %>%
                                     summarise(sessions = sum(sessions))




      # Bar graph using ggplot2 library 
      ggplot(VisitasData, aes(factor(Fuentes), sessions, fill = Fuentes)) + 
        geom_bar(stat="identity", position = "dodge") +
        geom_text(aes(label = comma(sessions)), position=position_dodge(width=0.9), vjust=-0.25) +
        scale_fill_manual(breaks = c("0", "1", "3", "6", "9", "12", "15"),
                          labels = labelsF,
                          values = c("#E69F00", "#56B4E9", "#009E73", 
                                     "#F0E442", "#0072B2", "#A082F8", "#F072A2"))

    })

    **############# Evolución de las visitas ##############################################
    #####################################################################################**


    output$EvolucionVisitas <- renderPlot({

      # Filter the data based on user selection month     
      date_seq <- seq(input$dates[1], input$dates[2], by = "day")


      EvolucionVisitas <-  filter(Visitas_Por_Fuente, date %in% date_seq) 


      mysessions <- function(x) {
        if(is.null(x)) return(NULL)
        #notice below the id column is how ggvis can understand which session to show 
        row <- EvolucionVisitas[EvolucionVisitas$id == x$id, ]
        #prettyNum shows the number with thousand-comma separator  
        paste0("Sessions:", "&nbsp;",prettyNum(row$sessions, big.mark=",",scientific=F)) 
      }




      EvolucionVisitas %>% 
        ggvis(x= ~date, y= ~sessions, key := ~id) %>%
        layer_points()  %>%
        add_tooltip(mysessions ,"hover") %>%
        layer_paths() %>%
        add_axis("x", value=c(as.character(EvolucionVisitas$date[1]),as.character(EvolucionVisitas$date[round(length(EvolucionVisitas$date)/2,0)]),
                              as.character(tail(EvolucionVisitas$date, n=1)))) %>%
        bind_shiny("EvolucionVisitas")







    #####################################################################################
    #####################################################################################


    output$Comentarios = renderDataTable({

      date_seq <- seq(input$dates[1], input$dates[2], by = "day")


      ComentariosDic <- filter(ComentariosDic, date %in% date_seq)

      ComentariosDic <- filter(ComentariosDic, !grepl("^$", Comentarios))


    })

})

Answer 1

仅对 ggvis 进行故障排除，您的问题主要是由于您尝试自定义 x 轴造成的。 ggvis 试图通过将日期解释为时间来变得聪明。出于此图的目的，我认为最好将它们视为因素。

这是一个完整的可重现的答案。

shiny::runGist("https://gist.github.com/cdeterman/0ac102cd68a7987a8a90")

您会注意到其他一些差异。最好使您的数据集具有反应性，这样您就可以在多个地方重用它而无需额外的开销。此外，正如 @jalapic 最初建议的那样，您希望使 ggvis 对象具有反应性，以便绘图可以是动态的并使用漂亮的工具提示。