我试图根据不同因素过滤反应数据集。不过，根据用户过滤数据的方式来更新我的数据集有一些困难。下面是我使用的数据集的一个子集，以及我用来创建闪亮的Ap的ui和服务器文件。过滤反光数据集R

负载包

library (shiny) 
    library(ggvis) 
    library(dplyr)

我使用的数据框：

Flux_Data_df<- structure(list(Site_ID = structure(c(1L, 3L, 5L, 7L, 8L), .Label = c("AR-Slu", 
"AR-Vir", "AU-Tum", "AU-Wac", "BE-Bra", "BE-Jal", "BE-Vie", "BR-Cax", 
"BR-Ma2", "BR-Sa1", "BR-Sa3", "BW-Ma1", "CA-Ca1", "CA-Ca2", "CA-Ca3", 
"CA-Gro", "Ca-Man", "CA-NS1", "CA-NS2", "CA-NS3", "CA-NS4", "CA-NS5", 
"CA-NS6", "CA-NS7", "CA-Oas", "CA-Obs", "CA-Ojp", "CA-Qcu", "CA-Qfo", 
"CA-SF1", "CA-SF2", "CA-SF3", "CA-SJ1", "CA-SJ2", "CA-SJ3", "CA-TP1", 
"CA-TP2", "CA-TP3", "CA-TP4", "CA-Wp1", "CN-Bed", "CN-Cha", "CN-Din", 
"CN-Ku1", "CN-Qia", "CZ-Bk1", "De-Bay", "DE-Hai", "DE-Har", "DE-Lkb", 
"DE-Meh", "DE-Obe", "DE-Tha", "DE-Wet", "DK-Sor", "ES-Es1", "FI-Hyy", 
"FI-Sod", "FR-Fon", "FR-Hes", "FR-Lbr", "FR-Pue", "GF-Guy", "ID-Pag", 
"IL-Yat", "IS-Gun", "IT-Col", "IT-Cpz", "IT-Lav", "IT-Lma", "IT-Noe", 
"IT-Non", "IT-Pt1", "IT-Ro1", "IT-Ro2", "IT-Sro", "JP-Tak", "JP-Tef", 
"JP-Tom", "MY-Pso", "NL-Loo", "PA-Spn", "PT-Esp", "RU-Fyo", "RU-Skp", 
"RU-Zot", "SE-Abi", "SE-Fla", "SE-Nor", "SE-Sk1", "SE-Sk2", "SE-St1", 
"UK-Gri", "UK-Ham", "US-Bar", "US-Blo", "US-Bn1", "US-Bn2", "Us-Bn3", 
"US-Dk2", "US-Dk3", "US-Fmf", "US-Fuf", "US-Fwf", "US-Ha1", "US-Ha2", 
"US-Ho1", "US-Ho2", "US-Lph", "US-Me1", "US-Me3", "US-Me4", "US-Me6", 
"US-Moz", "US-NC1", "US-Nc2", "US-NR1", "US-Oho", "US-So2", "US-So3", 
"US-Sp1", "US-Sp2", "US-Sp3", "US-Syv", "US-Umb", "US-Wbw", "US-Wcr", 
"US-Wi0", "US-Wi1", "US-Wi2", "US-Wi4", "US-Wi8", "VU-Coc", "CA-Cbo", 
"CN-Lao", "ID-Buk", "JP-Fuj", "RU-Ab", "RU-Be", "RU-Mix"), class = "factor"), 
    Ecosystem = structure(c(5L, 3L, 5L, 5L, 3L), .Label = c("DBF", 
    "DNF", "EBF", "ENF", "MF", "SHB", "WSA"), class = "factor"), 
    Climate = structure(c(3L, 3L, 3L, 3L, 4L), .Label = c("Arid", 
    "Continental", "Temperate", "Tropical"), class = "factor"), 
    Management = structure(c(4L, 2L, 3L, 4L, 4L), .Label = c("High", 
    "Low", "Moderate", "None"), class = "factor"), Stand_Age = c(50, 
    99, 77.0833333333333, 66.2, 97), NEP = c(1262.24986565392, 
    251.665998718498, 89.590110051402, 467.821910494384, 560), 
    GPP = c(2437.9937774539, 1837.82835206203, 1353.91140903122, 
    1740.68843840394, 3630), NEP_GPP = c(0.517741217113419, 0.143353622997247, 
    0.0760076059028116, 0.270737440100469, 0.1542699725), Uncert = c(7.29706486170583, 
    12.3483066698996, 7.59406340226036, 8.2523670901841, 12.1 
    ), Gap_filled = c(0.953310527540233, 0.969648973753497, 0.9395474605477, 
    0.923408280276339, 1), MAT = c(19.0438821722383, 9.67003296799878, 
    10.7728316162948, 8.2796213684244, 27.341666667), MAT_An = c(-0.0413522012578611, 
    0.840055031446541, 0.705896226415094, 0.805524109014675, 
    0.191666666666667), MAT_Trend = c(0.0119577487502016, 0.0196238509917756, 
    0.0305871364833632, 0.0381007095629741, 0.0194619147449338 
    ), MAP = c(351.700001291931, 1107.49999958277, 844.158499979666, 
    998.205467054248, 2279.5), MAP_CRU = c(592.2, 850.925, 852.591666666667, 
    1098.98, 2279.5), SPI_CRU_Mean = c(-0.352735702252502, 0.188298093749456, 
    0.0830157542916604, 0.397632136136383, 1.31028809089487), 
    MAP_An = c(4.14188988095238, -15.8198660714286, 5.39074900793651, 
    2.28799107142857, 1.55565476190476), MAP_Trend = c(1.38787584993337, 
    0.147192657259031, 0.747167885331603, 0.104885622031644, 
    0.841903850753408), CEC_Total_1km = c(14.05, 10.25, 17.975, 
    21, 9.95), Clay_Silt = c(36.65, 42.125, 32.275, 55, 54.825 
    ), Clay_1km = c(26.425, 31.425, 11.25, 22.45, 38.075), Silt_1km = c(10.225, 
    10.7, 21.025, 32.55, 16.75), Sand_1km = c(63.35, 57.325, 
    67.65, 45, 45.275), NOy = c(1.73752826416889, 2.76055219091326, 
    4.96187381895543, 5.06857284157762, 0.90948457442513), NHx = c(2.50363534311763, 
    2.99675999696687, 11.2747222582845, 13.9207300067467, 1.53292533883169 
    ), Soil_C_1km = c(3.6, 17, 23.575, 26.65, 8.15), Lat = c(-33.4648, 
    -35.6566, 51.3092, 50.3051, -1.72000003), Long = c(-66.4598, 
    148.1516, 4.5206, 5.9981, -51.4500008)), .Names = c("Site_ID", 
"Ecosystem", "Climate", "Management", "Stand_Age", "NEP", "GPP", 
"NEP_GPP", "Uncert", "Gap_filled", "MAT", "MAT_An", "MAT_Trend", 
"MAP", "MAP_CRU", "SPI_CRU_Mean", "MAP_An", "MAP_Trend", "CEC_Total_1km", 
"Clay_Silt", "Clay_1km", "Silt_1km", "Sand_1km", "NOy", "NHx", 
"Soil_C_1km", "Lat", "Long"), row.names = c(NA, 5L), class = "data.frame")

选择x和y变量来选择

axis_vars <- c(
    "NEP observed [gC.m-2.y-1]" = "NEP", 
    "NEP predicted [gC.m-2.y-1]" = "prediction", 
    "CUEe" = "NEP_GPP", 
    "GPP [gC.m-2.y-1]" = "GPP", 
    "Forest Age [years]" = "Stand_Age", 
    "MAT [°C]" = "MAT", 
    "SPI" = "SPI_CRU_Mean", 
    "MAP [mm.y-1]" = "MAP", 
    "MAP trend [mm.y-1]" = "MAP_Trend", 
    "MAT tremd [°C.y-1]" = "MAT_Trend", 
    "Clay content [kg.kg-1]" = "Clay_1km", 
    "N deposition [kg N.ha-1.y-1]" = "NHx" 
)

更新

的UI文件：

ui<- shinyUI(fluidPage(
    titlePanel("Data exploration"), 
    p('Interactive tool for data exploration'), 
    em('by, ', a('Simon Besnard', href = 'http://www.bgc-jena.mpg.de/bgi/index.php/People/SimonBesnard')), 

    fluidRow(
    column(4, 
      wellPanel(
      h4("Filter data"), 

      sliderInput("Gap_Filled", "Fraction gap filling", 0, 1, value = c(0, 1)), 
      sliderInput("Uncert", "Uncertainties", 0, 45, value = c(0, 45), 
         step = 1), 
      sliderInput("GPP", "GPP [gC.m-2.y-1]", 0, 4000, value = c(0, 4000), 
         0, 4000, 4000, step = 100), 
      checkboxGroupInput("Management", "Intensity of management", c("None", "Low", "Moderate", "High"), 
           selected= c("None", "Low", "Moderate", "High"), inline = T), 
      checkboxGroupInput("Disturbance", "Type of disturbance", 
         c("Afforestation\\Reforestation", "Harvest", "None", "Wildfire"), 
         selected = c("Afforestation\\Reforestation", "Harvest", "None", "Wildfire"), inline=T), 
      checkboxGroupInput("Climate", "Type of climate", 
            c("Arid", "Continental", "Temperate", "Tropical"), 
           selected=c("Arid", "Continental", "Temperate", "Tropical"), inline=T), 
      checkboxGroupInput("Ecosystem", 
          label="PFTs", 
          choices=list("DBF", "DNF", "EBF", "ENF", "MF", "SHB"), 
          selected=c("DBF", "DNF", "EBF", "ENF", "MF","SHB"), inline=T) 
      ), 

    wellPanel(
      selectInput("xvar", "X-axis variable", axis_vars, selected = "Stand_Age"), 
      selectInput("yvar", "Y-axis variable", axis_vars, selected = "NEP") 
      )), 

    column(8, 
    tabsetPanel(
     tabPanel("Bokeh Plot", ggvisOutput("rBokeh")) 
    )) 
)))

而服务器文件：

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

# Apply filters 
FLux_Data <- reactive({ 
    gap_filled<- input$Gap_filled 
    uncert<- input$Uncert 
    gpp<- input$GPP 
    management<- input$Management 
    disturbance <- input$Disturbance 
    climate <- input$Climate 
    ecosystem <- input$Ecosystem 

    m<- Flux_Data_df %>% 
     filter(
     gap_filled >= Gap_filled & 
     uncert >= Uncert & 
     gpp >= GPP & 
     management >= Management & 
     disturbance %in% Disturbance & 
     climate %in% Climate & 
     ecosystem %in% Ecosystem 
    ) 
    m <- as.data.frame(m) 
    m 
    }) 

# Function for generating tooltip text 
Site_tooltip <- function(x) { 
    if (is.null(x)) return(NULL) 
    if (is.null(x$Site_ID)) return(NULL) 

    # Pick out the movie with this ID 
    df <- isolate(FLux_Data()) 
    Flux_ID <- df[df$Site_ID == x$Site_ID, ] 
    paste0("<b>", Flux_ID$Site_ID, "</b><br>", 
     Flux_ID$year, "<br>") 
} 

# A reactive expression with the ggvis plot 
Bokeh <- reactive({ 
    # Lables for axes 
    xvar_name <- names(axis_vars)[axis_vars == input$xvar] 
    yvar_name <- names(axis_vars)[axis_vars == input$yvar] 

    # Normally we could do something like props(x = ~BoxOffice, y = ~Reviews), 
    # but since the inputs are strings, we need to do a little more work. 
    xvar <- prop("x", as.symbol(input$xvar)) 
    yvar <- prop("y", as.symbol(input$yvar)) 

FLux_Data %>% 
    ggvis(x = xvar, y = yvar) %>% 
    layer_points(size := 50, size.hover := 200, 
       fillOpacity := 0.2, fillOpacity.hover := 0.5) %>% 
    add_tooltip(Site_tooltip, "hover") %>% 
    add_axis("x", title = xvar_name) %>% 
    add_axis("y", title = yvar_name) %>% 
    set_options(width = 500, height = 500) 
}) 
observe({ 
    Bokeh %>% bind_shiny("rBokeh") 
    }) 
}) 

shinyApp(ui, server)

基本上，我想更新我的数据框随时过滤操作是在做基于ui文件中的过滤设置。我尝试使用dplyr包中的filter函数，但无法完成此操作。任何人都可以帮助我吗？

来源

2016-06-09 SimonB

过滤功能应该是像'过滤器（Gap_filled ==输入$ Gap_FIlled＆Uncert =输入$ ...' –

通过使用EVAL的==这是行不通的。'RROR（替代（表达式），envir，enclos）：不正确的长度（0），期望：716'。我尝试在％中使用'％in％'，而不是像％'input $ Gap_FIlled＆'中的Gap_filled％ – SimonB

有几个问题需要解决：1.对于sliderInputs，您需要使用'>'或'<'或类似的代替'％in％'2.对于selectInputs，您需要确保选项与您的数据中的内容相同（例如“造林 - 再造林”，但数据使用正斜杠），也可能是提供默认值的一个好主意3.当您使用反应值时，使用它作为'Bokeh（）'的函数，但是也可以把这行放在'observe'中。你可能只需要一个ggVisOutput而不是rbokehoutput。 –

这是一个工作版本。请注意，我删除了许多过滤条件以及工具提示功能以简化它。

library(shiny) 
library(ggvis) 
library(dplyr) 

axis_vars <- c(
    "NEP observed [gC.m-2.y-1]" = "NEP", 
    "NEP predicted [gC.m-2.y-1]" = "prediction", 
    "CUEe" = "NEP_GPP", 
    "GPP [gC.m-2.y-1]" = "GPP", 
    "Forest Age [years]" = "Stand_Age", 
    "MAT [°C]" = "MAT", 
    "SPI" = "SPI_CRU_Mean", 
    "MAP [mm.y-1]" = "MAP", 
    "MAP trend [mm.y-1]" = "MAP_Trend", 
    "MAT tremd [°C.y-1]" = "MAT_Trend", 
    "Clay content [kg.kg-1]" = "Clay_1km", 
    "N deposition [kg N.ha-1.y-1]" = "NHx" 
) 

ui<- shinyUI(fluidPage(
    titlePanel("Data exploration"), 
    p('Interactive tool for data exploration'), 
    em('by, ', a('Simon Besnard', href = 'http://www.bgc-jena.mpg.de/bgi/index.php/People/SimonBesnard')), 

    fluidRow(
     column(3, 
       wellPanel(
        h4("Filter data"), 

        sliderInput("Gap_Filled", "Fraction gap filling", 0, 1, value = c(0, 1)), 
        sliderInput("Uncert", "Uncertainties", 0, 45, value = c(0, 45), 
           step = 1), 
        sliderInput("GPP", "GPP [gC.m-2.y-1]", 0, 4000, value = c(0, 4000), 
           0, 4000, 4000, step = 100), 
        sliderInput("Management", "Intensity of management", 0, 3, value = c(0, 3), 
           step = 1), 
        selectInput("Disturbance", "Type of disturbance", multiple = TRUE, 
           c("Afforestation-Reforestation", "Harvest", "Undisturbed", "Wildfire")), 
        selectInput("Climate", "Type of climate", multiple = TRUE, 
           c("Arid", "Continental", "Temperate", "Tropical"), selected = c("Temperate")), 
        selectInput("Ecosystem", "PFTs", multiple = TRUE, 
           c("DBF", "DNF", "EBF", "ENF", "MF", "SHB"), selected=c("MF")) 
       ), 

       wellPanel(
        selectInput("xvar", "X-axis variable", axis_vars, selected = "Stand_Age"), 
        selectInput("yvar", "Y-axis variable", axis_vars, selected = "NEP") 
       )), 

    column(8, 
      mainPanel(
       ggvisOutput("ggvis")) 
       )) 
)) 

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

    # A reactive expression with the ggvis plot 
    Bokeh <- reactive({ 
     # Lables for axes 
     xvar_name <- names(axis_vars)[axis_vars == input$xvar] 
     yvar_name <- names(axis_vars)[axis_vars == input$yvar] 
     xvar <- prop("x", as.symbol(input$xvar)) 
     yvar <- prop("y", as.symbol(input$yvar)) 

    test <- Flux_Data_df %>% 
     filter(
      Gap_filled > input$Gap_filled[1] & 
      Gap_filled < input$Gap_filled[2]) %>% 
     filter(
      Climate %in% input$Climate & 
      Ecosystem %in% input$Ecosystem 
     ) %>% as.data.frame() 

     test %>% 
      ggvis(x = xvar, y = yvar) %>% 
      layer_points(size := 50, size.hover := 200, 
         fillOpacity := 0.2, fillOpacity.hover := 0.5) %>% 
      add_axis("x", title = xvar_name) %>% 
      add_axis("y", title = yvar_name) %>% 
      set_options(width = 500, height = 500) 
    }) 

    observe({ 
     Bokeh() %>% bind_shiny("ggvis") 
    }) 
}) 

shinyApp(ui, server)

来源

2016-06-09 18:51:15

非常感谢任何关于如何设置服务器滑动条过滤器的想法。例如， 'Gap_filled> =输入$ Gap_filled＆'不起作用。 – SimonB

sliderInput返回两个值（最小，最大），所以你需要做'Gap_filled> =输入$ Gap_filled [1]＆Gap_filled <输入$ Gap_filled [2]' –

是的，我已经试过了，我有同样的错误信息'在eval错误：不正确的长度（0），期待：524' – SimonB

过滤反光数据集R

更新

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