多重选择和光泽

Question

密谋在我闪亮的应用程序，我有一个checkboxGroupInput

我应该如何在服务器功能做绘图命令，在我绘制TurbInt_mean对MeanWindSpeed_mean并添加线（曲线）的方式按用户选择的情节？

我试图总结我的闪亮的应用，因为放型能够代码如下（你必须先加载，我提供的样本数据）

library(shiny) 
ui <- fluidPage(
    checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c")), 
    plotOutput("plotmeanTI",width = "100%")) 

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


    output$plotmeanTI <- renderPlot({ 
    plot(as.matrix(TI_plot[,1]),as.matrix(TI_plot[,2]),t='o',ylim=c(0,1),xaxs="i", 
     xlab="Mean Wind Speed", ylab="<TI>") 

    if(input$variable=="ap"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_A_Plus_mean,col=6)} 
    if(input$variable=="a"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_A_mean,col=2)} 
    if(input$variable=="b"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_B_mean,col=3)} 
    if(input$variable=="c"){lines(as.matrix(TI_plot[,1]),TI_plot$NTM_C_mean,col=4)} 
}) 

} 

shinyApp(ui=ui,server=server) 

如果用户选择1，一个曲线应加如果选择多个曲线，我想要将多条曲线添加到我的plot.I可以做单个选择，就像我在代码中所解释的那样，但是当我有多个选择时它不起作用。

我的数据集的样子：

dput(TI_plot) 
structure(list(MeanWindSpeed_mean = c(0.292023070097604, 1.12011882699226, 
2.0283906614786, 3.00947886508396, 4.01428066037736, 5.01250749719984, 
6.0080377166157, 7.00777409860191, 8.0049941822883, 9.00201938353988, 
9.99646762244478, 10.9883558855227, 11.9798700705476, 12.976996101646, 
13.9653724394786, 14.9495068163593, 15.9628459343795, 16.9708685581934, 
17.9623943661972, 18.992621231979, 19.9643220338983, 20.9834693877551, 
22.0170278637771, 22.9658904109589, 24.0025266903915, 24.9935025380711 
), TurbInt_mean = c(3.02705430346051, 0.420402191213343, 0.264195029831388, 
0.215109260166585, 0.18794121258946, 0.16699392997796, 0.148261539245668, 
0.134479958525654, 0.122038442146089, 0.110595865904036, 0.097103704211826, 
0.0836329541372291, 0.0708397249149876, 0.0622491842333237, 0.0591184473929236, 
0.0611678829190056, 0.0652080242510699, 0.0690131441806601, 0.073762588028169, 
0.0756961992136304, 0.0805696610169492, 0.0817446428571429, 0.0830263157894737, 
0.0827277397260274, 0.0749537366548043, 0.0765532994923858), 
    NTM_A_Plus_mean = c(Inf, 1.10260388189292, 0.642329939163608, 
    0.473065816856713, 0.387417559923049, 0.336769624752903, 
    0.303163441845455, 0.27908457313955, 0.261084722917897, 0.247090026094941, 
    0.235918715179959, 0.226796351934008, 0.219190019655214, 
    0.212713243118379, 0.20720881268079, 0.202452008587075, 0.19816685602934, 
    0.19441329542209, 0.191131377464549, 0.188086340606011, 0.185500707351721, 
    0.18304730715887, 0.180790073836667, 0.178898058874634, 0.177002145398197, 
    0.175335040729601), NTM_A_mean = c(Inf, 0.98009233946037, 
    0.570959945923208, 0.420502948317078, 0.344371164376044, 
    0.299350777558136, 0.269478614973738, 0.248075176124045, 
    0.232075309260353, 0.219635578751059, 0.209705524604408, 
    0.201596757274674, 0.194835573026857, 0.189078438327448, 
    0.184185611271814, 0.179957340966289, 0.176148316470525, 
    0.172811818152969, 0.169894557746266, 0.167187858316455, 
    0.164889517645975, 0.162708717474551, 0.160702287854815, 
    0.159020496777452, 0.157335240353953, 0.155853369537423), 
    NTM_B_mean = c(Inf, 0.857580797027824, 0.499589952682807, 
    0.367940079777444, 0.301324768829038, 0.261931930363369, 
    0.23579378810202, 0.217065779108539, 0.203065895602809, 0.192181131407176, 
    0.183492334028857, 0.176397162615339, 0.1704811263985, 0.165443633536517, 
    0.161162409862837, 0.157462673345503, 0.154129776911709, 
    0.151210340883848, 0.148657738027983, 0.146289376026898, 
    0.144278327940228, 0.142370127790232, 0.140614501872963, 
    0.139142934680271, 0.137668335309708, 0.136371698345246), 
    NTM_C_mean = c(Inf, 0.735069254595278, 0.428219959442406, 
    0.315377211237809, 0.258278373282033, 0.224513083168602, 
    0.202108961230303, 0.186056382093034, 0.174056481945265, 
    0.164726684063294, 0.157279143453306, 0.151197567956005, 
    0.146126679770143, 0.141808828745586, 0.13813920845386, 0.134968005724717, 
    0.132111237352894, 0.129608863614727, 0.127420918309699, 
    0.125390893737341, 0.123667138234481, 0.122031538105913, 
    0.120526715891111, 0.119265372583089, 0.118001430265464, 
    0.116890027153068)), class = c("tbl_df", "tbl", "data.frame" 
), row.names = c(NA, -26L), .Names = c("MeanWindSpeed_mean", 
"TurbInt_mean", "NTM_A_Plus_mean", "NTM_A_mean", "NTM_B_mean", 
"NTM_C_mean")) 

TI_plot的脑袋就像是：

head(TI_plot) 
# A tibble: 6 x 6 
    MeanWindSpeed_mean TurbInt_mean NTM_A_Plus_mean NTM_A_mean NTM_B_mean NTM_C_mean 
       <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl> 
1   0.2920231 3.0270543    Inf  Inf  Inf  Inf 
2   1.1201188 0.4204022  1.1026039 0.9800923 0.8575808 0.7350693 
3   2.0283907 0.2641950  0.6423299 0.5709599 0.4995900 0.4282200 
4   3.0094789 0.2151093  0.4730658 0.4205029 0.3679401 0.3153772 
5   4.0142807 0.1879412  0.3874176 0.3443712 0.3013248 0.2582784 
6   5.0125075 0.1669939  0.3367696 0.2993508 0.2619319 0.2245131 

Answer 1

我们可以使用switch

library(shiny) 
ui <- fluidPage(
    checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c"), 
       selected = c("A Plus" = "ap")), 
    plotOutput("plotmeanTI",width = "100%") 
     ) 

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


    output$plotmeanTI <- renderPlot({ 
    f1 <- function(nm1){ 
     switch(nm1, 
      ap = lines(TI_plot[[1]],TI_plot$NTM_A_Plus_mean,col=6), 
      a = lines(TI_plot[[1]],TI_plot$NTM_A_mean,col=2), 
      b = lines(TI_plot[[1]],TI_plot$NTM_B_mean,col=3), 
      c = lines(TI_plot[[1]],TI_plot$NTM_C_mean,col=4) 

    ) 

    } 

    if(is.null(input$variable)) { 
     plot(TI_plot[[1]], TI_plot[[2]],t='o',ylim=c(0,1),xaxs="i", 
      xlab="Mean Wind Speed", ylab="<TI>") 
    } else { 
    plot(TI_plot[[1]], TI_plot[[2]],t='o',ylim=c(0,1),xaxs="i", 
     xlab="Mean Wind Speed", ylab="<TI>") 
    f1(input$variable) 

    } 
    }) 

} 

shinyApp(ui=ui,server=server) 

- 输出

---

使用ggplot2

library(shiny) 
library(ggplot2) 
library(tidyr) 
library(dplyr) 

ui <- fluidPage(
    checkboxGroupInput("variable", "Select IEC Classes for TI",c("A Plus" = "ap","A" = "a","B" = "b","C"="c"), 
        selected = c("A Plus" = "ap")), 
    plotOutput("plotmeanTI",width = "100%")) 

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


    output$plotmeanTI <- renderPlot({ 
     keyvaldata <- data.frame(key = c('NTM_A_Plus_mean', 'NTM_A_mean', 'NTM_B_mean', 'NTM_C_mean'), 
           Var = c('ap', 'a', 'b', 'c'), stringsAsFactors = FALSE) 


    p1 <- gather(TI_plot, key, val, -MeanWindSpeed_mean, -TurbInt_mean) %>% 
        left_join(., keyvaldata) %>% 
        filter(Var %in% input$variable) %>% 
        ggplot(., aes(MeanWindSpeed_mean, TurbInt_mean, colour = Var)) + 
        geom_line() + 
        geom_line(aes(y =val)) + 
        labs(x = "Mean Wind Speed", y = "<TI>") + 
        theme_bw() 

    if(is.null(input$variable)) { 

     ggplot(TI_plot, aes(MeanWindSpeed_mean, TurbInt_mean)) + 
     geom_line() + 
     labs(x = "Mean Wind Speed", y = "<TI>") + 
     theme_bw() 

    } else { 
     p1 

    } 
    }) 

} 

shinyApp(ui=ui,server=server) 

- 输出