R 添加多个“；滑块“；到同一个图形

我正在使用R编程语言。使用“plotly”库，我能够制作以下交互式图形：

library(dplyr)
library(ggplot2)
library(shiny)
library(plotly)
library(htmltools)

library(dplyr)
#generate data
set.seed(123)

var = rnorm(731, 100,25)
date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
data = data.frame(var,date)

vals <- 90:100
combine <- vector('list', length(vals))
count <- 0
for (i in vals) {
    
    data$var_i = i
    data$new_var_i = ifelse(data$var >i,1,0)
    
    #percent of observations greater than i (each month)
    aggregate_i = data %>%
        mutate(date = as.Date(date)) %>%
        group_by(month = format(date, "%Y-%m")) %>%
        summarise( mean = mean(new_var_i))
    
    #combine files together
    
    aggregate_i$var = i
    aggregate_i$var = as.factor(aggregate_i$var)
    
    count <- count + 1
    combine[[count]] <- aggregate_i
    
}

result_1 <- bind_rows(combine)
result_1$group = "group_a"
result_1$group = as.factor(result_1$group)

######

var = rnorm(731, 85,25)
date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
data = data.frame(var,date)

vals <- 90:100
combine <- vector('list', length(vals))
count <- 0
for (i in vals) {
    
    data$var_i = i
    data$new_var_i = ifelse(data$var >i,1,0)
    
    #percent of observations greater than i (each month)
    aggregate_i = data %>%
        mutate(date = as.Date(date)) %>%
        group_by(month = format(date, "%Y-%m")) %>%
        summarise( mean = mean(new_var_i))
    
    #combine files together
    
    aggregate_i$var = i
    aggregate_i$var = as.factor(aggregate_i$var)
    
    count <- count + 1
    combine[[count]] <- aggregate_i
    
}

result_2 <- bind_rows(combine)
result_2$group = "group_b"
result_2$group = as.factor(result_2$group)

#combine all files

final = rbind(result_1, result_2)

gg <-ggplot(final, aes(frame = var, color = group)) + geom_line(aes(x=month, y=mean, group=1))+ theme(axis.text.x = element_text(angle=90)) + ggtitle("title")

gg = ggplotly(gg)

库（dplyr）
图书馆（GG2）
图书馆（闪亮）
图书馆（绘本）
图书馆（htmltools）
图书馆（dplyr）
#生成数据
种子集（123）
var=rnorm（731100,25）
日期=序号（截止日期（“2014/1/1”）、截止日期（“2016/1/1”）、by=“天”）
data=data.frame（变量，日期）
VAL%
分组依据（月=格式（日期，“%Y-%m”））%>%
总结（平均值=平均值（新变量i））
#将文件组合在一起
合计i$var=i
聚合i$var=as.factor（聚合i$var）
count我认为你不能用标准的
plotly
API来实现这一点

我认为对于这种情况，最好使用
shinny
并创建一个web应用程序。您可以根据需要添加任意多的滑块，然后根据需要过滤数据以更新绘图

这样做的缺点是，您只是使用新数据重新绘制绘图，而不是像以前那样进行动画。所以你最终失去了之前的平滑过渡

实际上，有一种方法可以保持动画方面的效果，我不知道，但您需要深入了解
的光泽
/
情节
。看一看。我不知道这件事，所以我没有试着去做。但是我以后会看的

以下是我的解决方案：

library(shiny)
library(plotly)
library(dplyr)

gendata <- function(){
    #generate data
    set.seed(123)
    
    var = rnorm(731, 100,25)
    date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
    data = data.frame(var,date)
    
    vals <- 90:100
    combine <- vector('list', length(vals))
    count <- 0
    for (i in vals) {
        
        data$var_i = i
        data$new_var_i = ifelse(data$var >i,1,0)
        
        #percent of observations greater than i (each month)
        aggregate_i = data %>%
            dplyr::mutate(date = as.Date(date)) %>%
            dplyr::group_by(month = format(date, "%Y-%m")) %>%
            dplyr::summarise(mean = mean(new_var_i), .groups='drop')
        
        #combine files together
        
        aggregate_i$var = i
        aggregate_i$var = as.factor(aggregate_i$var)
        
        count <- count + 1
        combine[[count]] <- aggregate_i
        
    }
    
    result_1 <- bind_rows(combine)
    result_1$group = "group_a"
    result_1$group = as.factor(result_1$group)
    
    ######
    
    var = rnorm(731, 85,25)
    date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
    data = data.frame(var,date)
    
    vals <- 90:100
    combine <- vector('list', length(vals))
    count <- 0
    for (i in vals) {
        
        data$var_i = i
        data$new_var_i = ifelse(data$var >i,1,0)
        
        #percent of observations greater than i (each month)
        aggregate_i = data %>%
            dplyr::mutate(date = as.Date(date)) %>%
            dplyr::group_by(month = format(date, "%Y-%m")) %>%
            dplyr::summarise(mean = mean(new_var_i), .groups='drop')
        
        #combine files together
        
        aggregate_i$var = i
        aggregate_i$var = as.factor(aggregate_i$var)
        
        count <- count + 1
        combine[[count]] <- aggregate_i
        
    }
    
    result_2 <- bind_rows(combine)
    result_2$group = "group_b"
    result_2$group = as.factor(result_2$group)
    
    # combine all files
    # note: sliderInput needs numeric data, so I converted values of "var" to numeric
    final <- rbind(result_1, result_2)
    final$var <- as.integer(as.character(final$var))

    return(final)
}

final <- gendata()

ui <- fluidPage(
    fluidRow(column=12,
             plotlyOutput("lineplot")),
    fluidRow(column=12,
             # create slider for group a
             sliderInput("groupa", "Group A:",
                         min = min(final$var), max = max(final$var),
                         value = min(final$var), step = 1,
                         animate =
                             animationOptions(interval = 300, loop = FALSE),
                         width='95%')),
    fluidRow(column=12,
             # create slider for group b
             sliderInput("groupb", "Group B:",
                         min = min(final$var), max = max(final$var),
                         value = min(final$var), step = 1,
                         animate =
                             animationOptions(interval = 300, loop = FALSE),
                         width='95%')))

server <- function(input, output, session){
    
    # create a reactive dataframe with filtered data for group a at current value of var
    df.a <- reactive({
        final %>% dplyr::filter(group == 'group_a') %>%
            dplyr::filter(var == input$groupa)
    })
    
    # create a reactive dataframe with filtered data for group b at current value of var
    df.b <- reactive({
        final %>% dplyr::filter(group == 'group_b') %>%
            dplyr::filter(var == input$groupb)
    })
    
    # Create plotly with filtered data
    output$lineplot <- renderPlotly({
        plot_ly() %>%
            add_trace(data=df.a(), x=~month, y=~mean, color=~group, type = 'scatter', mode = 'lines', colors = 'Set1') %>%
            add_trace(data=df.b(), x=~month, y=~mean, color=~group, type = 'scatter', mode = 'lines', colors = 'Set1')
    })
}

shinyApp(ui, server)

库（闪亮）
图书馆（绘本）
图书馆（dplyr）
性别百分比
dplyr:：总结（平均值=平均值（新变量i），.groups='drop'）
#将文件组合在一起
合计i$var=i
聚合i$var=as.factor（聚合i$var）
伯爵，您应该决定是否使用
对不起，我会设法解决的，非常感谢您的回答！一切都很好！我想在plotly中这样做的唯一原因是，将最终输出保存为html文件要容易得多。我不确定是否可以将rshiny输出保存为html文件？是和否“是”，因为当您使用shiny时，实际上是在客户端通过UI创建html文件。“否”，因为此html文件不包含所有数据。它与拥有数据并执行所有数据操作的服务器“对话”，然后将数据发送回客户端。如果您想要一个自包含的html，那么您可能需要您的方法。也许这是有可能的。让我看看我是否能做到，我会补充我的答案。我在这里贴了一个类似的问题，我试图做这一切都是有计划的：谢谢你的帮助
library(shiny)
library(plotly)
library(dplyr)

gendata <- function(){
    #generate data
    set.seed(123)
    
    var = rnorm(731, 100,25)
    date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
    data = data.frame(var,date)
    
    vals <- 90:100
    combine <- vector('list', length(vals))
    count <- 0
    for (i in vals) {
        
        data$var_i = i
        data$new_var_i = ifelse(data$var >i,1,0)
        
        #percent of observations greater than i (each month)
        aggregate_i = data %>%
            dplyr::mutate(date = as.Date(date)) %>%
            dplyr::group_by(month = format(date, "%Y-%m")) %>%
            dplyr::summarise(mean = mean(new_var_i), .groups='drop')
        
        #combine files together
        
        aggregate_i$var = i
        aggregate_i$var = as.factor(aggregate_i$var)
        
        count <- count + 1
        combine[[count]] <- aggregate_i
        
    }
    
    result_1 <- bind_rows(combine)
    result_1$group = "group_a"
    result_1$group = as.factor(result_1$group)
    
    ######
    
    var = rnorm(731, 85,25)
    date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")
    data = data.frame(var,date)
    
    vals <- 90:100
    combine <- vector('list', length(vals))
    count <- 0
    for (i in vals) {
        
        data$var_i = i
        data$new_var_i = ifelse(data$var >i,1,0)
        
        #percent of observations greater than i (each month)
        aggregate_i = data %>%
            dplyr::mutate(date = as.Date(date)) %>%
            dplyr::group_by(month = format(date, "%Y-%m")) %>%
            dplyr::summarise(mean = mean(new_var_i), .groups='drop')
        
        #combine files together
        
        aggregate_i$var = i
        aggregate_i$var = as.factor(aggregate_i$var)
        
        count <- count + 1
        combine[[count]] <- aggregate_i
        
    }
    
    result_2 <- bind_rows(combine)
    result_2$group = "group_b"
    result_2$group = as.factor(result_2$group)
    
    # combine all files
    # note: sliderInput needs numeric data, so I converted values of "var" to numeric
    final <- rbind(result_1, result_2)
    final$var <- as.integer(as.character(final$var))

    return(final)
}

final <- gendata()

ui <- fluidPage(
    fluidRow(column=12,
             plotlyOutput("lineplot")),
    fluidRow(column=12,
             # create slider for group a
             sliderInput("groupa", "Group A:",
                         min = min(final$var), max = max(final$var),
                         value = min(final$var), step = 1,
                         animate =
                             animationOptions(interval = 300, loop = FALSE),
                         width='95%')),
    fluidRow(column=12,
             # create slider for group b
             sliderInput("groupb", "Group B:",
                         min = min(final$var), max = max(final$var),
                         value = min(final$var), step = 1,
                         animate =
                             animationOptions(interval = 300, loop = FALSE),
                         width='95%')))

server <- function(input, output, session){
    
    # create a reactive dataframe with filtered data for group a at current value of var
    df.a <- reactive({
        final %>% dplyr::filter(group == 'group_a') %>%
            dplyr::filter(var == input$groupa)
    })
    
    # create a reactive dataframe with filtered data for group b at current value of var
    df.b <- reactive({
        final %>% dplyr::filter(group == 'group_b') %>%
            dplyr::filter(var == input$groupb)
    })
    
    # Create plotly with filtered data
    output$lineplot <- renderPlotly({
        plot_ly() %>%
            add_trace(data=df.a(), x=~month, y=~mean, color=~group, type = 'scatter', mode = 'lines', colors = 'Set1') %>%
            add_trace(data=df.b(), x=~month, y=~mean, color=~group, type = 'scatter', mode = 'lines', colors = 'Set1')
    })
}

shinyApp(ui, server)