R [ Shiny ] : Error trying to output reactive model summary-6ren

R [ Shiny ] : Error trying to output reactive model summary

转载作者：行者123 更新时间：2023-12-04 07:25:11

我最近一直在使用 gapminder 数据集探索 Shiny 和 Shiny 的仪表板，并遇到了我的最新问题，试图输出使用基于用户选择的响应变量和预测变量创建的 react 模型的摘要。目的是同时输出 3 个模型的 EDA 图和摘要(基数、对数和逆)，这就是为什么 var_EDA_plots()有点乱。正在返回的错误是:

# Error: argument 1 (type 'list') cannot be handled by 'cat'

与简单线性回归建模相关的代码是:
用户界面

 # LM Page Content
tabItem(tabName = "lm",
h2("Simple Linear Regression"),
h3(wellPanel(fluidRow(
    column(2,"Response Variable:"), 
        column(2, selectInput("response", "", colnames(data)[4:6], 
            multiple = FALSE, selected = "lifeExp")),
        column(2, "Predictor Variable: "),
        column(2, selectInput("predictor", "", colnames(data)[4:6],
            multiple = FALSE, selected = "gdpPercap"))))
        ),
                                    
        tabsetPanel(type = "tabs",
        tabPanel("Plot", 
            splitLayout(
                plotOutput("EDAplotBase"),
                plotOutput("EDAplotLog"),
                plotOutput("EDAplotInv"))),
        tabPanel("Summary", verbatimTextOutput("base.lm")),
        tabPanel("Table", tableOutput("EDAtable"))
        ))

服务器

# LINEAR MODELLING ----

# Format Predictor and Response col names
var_EDA_plots <- reactive({
    str_col_predictor <- as.character(input$predictor)
    str_col_response <- as.character(input$response)
    transformed_data <- data %>% select(str_col_response, str_col_predictor, continent, country, year)
    transformed_data$predictor_base <- unlist(transformed_data[2])
    transformed_data$predictor_log <- unlist(log(transformed_data[2]))
    transformed_data$predictor_inv <- unlist(1/transformed_data[2])
    transformed_data$response <- unlist(transformed_data[1])
    transformed_data %>% select(continent, country, year, response, predictor_base, predictor_log, predictor_inv)
})

# Build linear models reactively
var_lm_base <- reactive({
    fml <- as.formula("response ~ predictor_base")
    lm(fml, data = var_EDA_plots())
})

# Base LM Model
output$base.lm <- renderText({
    summary(var_lm_base())
})

output$EDAtable <- renderTable({var_EDA_plots()})

下面我将粘贴全码对于 Shiny 的仪表板，如果整体调试和测试更容易(线性建模部分位于服务器底部):

library(shiny)
library(tidyverse)
library(shinydashboard)
library(gapminder)

# LOAD DATA ----
data <- gapminder %>% as_tibble() %>% arrange(country, year)

# LINEAR MODELLING ----
set.seed(117)
train <- data %>% slice_sample(prop = 0.8)
test <- data %>% slice_sample(prop = 0.2)

# UI ----
ui <- dashboardPage(
                    dashboardHeader(title = "Gapminder Dashboard"),
                    dashboardSidebar(
                        sidebarMenu(id = "tabs",
                                    menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard"),
                                             menuSubItem("Life Expectancy", tabName = "life"),
                                             menuSubItem("GDP Per Capita", tabName = "gdp")),
                                    menuItem("Linear Modelling", icon = icon("th"), tabName = "lm", badgeLabel = "new", badgeColor = "green"),
                                    fluidPage(
                                        selectInput("dateStart", "Start date:", distinct(data, year),
                                                    multiple = FALSE, selected = 1952),
                                        selectInput("dateEnd", "End date:", distinct(data, year),
                                                    multiple = FALSE, selected = 2007),
                                        selectInput("country1", "Select primary country:", distinct(data, country),
                                                    multiple = FALSE, selected = "Australia"),
                                        selectInput("country2", "Select secondary country:", distinct(data, country),
                                                    multiple = FALSE, selected = "Greece")
                                    ))),
                    dashboardBody(
                        tabItems(
                            
                            # Life Expectancy Page Content
                            tabItem(tabName = "life",
                                    # Top 5 KPIs
                                    splitLayout(
                                        valueBoxOutput("kpi.top5.life1", width = NULL),
                                        valueBoxOutput("kpi.top5.life2", width = NULL),
                                        valueBoxOutput("kpi.top5.life3", width = NULL),
                                        valueBoxOutput("kpi.top5.life4", width = NULL),
                                        valueBoxOutput("kpi.top5.life5", width = NULL)
                                    ),
                                    
                                    # Life Expectancy Histogram
                                    fluidPage(
                                        plotOutput("histLifeExp")
                                    ),
                                    
                                    # Life Expectancy & Population Plots
                                    splitLayout(
                                        plotOutput("lineplotLife"),
                                        plotOutput("lineplotPopn")
                                    ),
                                    
                                    # Bottom 5 KPIs
                                    splitLayout(
                                        valueBoxOutput("kpi.btm5.life1", width = NULL),
                                        valueBoxOutput("kpi.btm5.life2", width = NULL),
                                        valueBoxOutput("kpi.btm5.life3", width = NULL),
                                        valueBoxOutput("kpi.btm5.life4", width = NULL),
                                        valueBoxOutput("kpi.btm5.life5", width = NULL)
                                    )),
                            
                            # GDP Page Content
                            tabItem(tabName = "gdp",
                                    # Top 5 KPIs
                                    splitLayout(
                                        valueBoxOutput("kpi.top5.gdp1", width = NULL),
                                        valueBoxOutput("kpi.top5.gdp2", width = NULL),
                                        valueBoxOutput("kpi.top5.gdp3", width = NULL),
                                        valueBoxOutput("kpi.top5.gdp4", width = NULL),
                                        valueBoxOutput("kpi.top5.gdp5", width = NULL)
                                    ),
                                    
                                    # GDP Histogram
                                    fluidPage(
                                        plotOutput("histGDP")
                                    ),
                                    
                                    # GDP & Population Plots
                                    splitLayout(
                                        plotOutput("lineplotgdp"),
                                        plotOutput("lineplotPopn2")
                                    ),
                                    
                                    # Bottom 5 KPIs
                                    splitLayout(
                                        valueBoxOutput("kpi.btm5.gdp1", width = NULL),
                                        valueBoxOutput("kpi.btm5.gdp2", width = NULL),
                                        valueBoxOutput("kpi.btm5.gdp3", width = NULL),
                                        valueBoxOutput("kpi.btm5.gdp4", width = NULL),
                                        valueBoxOutput("kpi.btm5.gdp5", width = NULL)
                                    )),
                            
                            # LM Page Content
                            tabItem(tabName = "lm",
                                    h2("Simple Linear Regression"),
                                    h3(wellPanel(fluidRow(
                                        column(2,"Response Variable:"), 
                                        column(2, selectInput("response", "", colnames(data)[4:6], 
                                                              multiple = FALSE, selected = "lifeExp")),
                                        column(2, "Predictor Variable: "),
                                        column(2, selectInput("predictor", "", colnames(data)[4:6],
                                                              multiple = FALSE, selected = "gdpPercap"))))
                                    ),
                                    
                                    tabsetPanel(type = "tabs",
                                        tabPanel("Plot", 
                                                 splitLayout(
                                                     plotOutput("EDAplotBase"),
                                                     plotOutput("EDAplotLog"),
                                                     plotOutput("EDAplotInv"))),
                                        tabPanel("Summary", verbatimTextOutput("base.lm")),
                                        tabPanel("Table", tableOutput("EDAtable"))
                                    ))
                            
                        )
                    )
)

# SERVER ----
server <- function(input, output) {
    
    # REACTIVE DATA FILTERING ----
    
    # Top 5 Life Exp KPIs - date filtering
    var_maxDate_kpi_top5_life <- reactive({
        val <- as.integer(input$dateEnd)
        data %>% filter(year == val) %>% slice_max(n = 5, order_by = lifeExp)
    })
    
    # Bottom 5 Life Exp KPIs - date filtering
    var_maxDate_kpi_btm5_life <- reactive({
        val <- as.integer(input$dateEnd)
        data %>% filter(year == val) %>% slice_min(n = 5, order_by = lifeExp)
    })
    
    # Top 5 GDP KPIs - date filtering
    var_maxDate_kpi_top5_gdp <- reactive({
        val <- as.integer(input$dateEnd)
        data %>% filter(year == val) %>% slice_max(n = 5, order_by = gdpPercap)
    })
    
    # Bottom 5 GDP KPIs - date filtering
    var_maxDate_kpi_btm5_gdp <- reactive({
        val <- as.integer(input$dateEnd)
        data %>% filter(year == val) %>% slice_min(n = 5, order_by = gdpPercap)
    })
    
    # General Life Expectancy Reactive Filtering
    var_date_and_country <- reactive({
        startVal <- as.integer(input$dateStart)
        endVal <- as.integer(input$dateEnd)
        country1 <- as.character(input$country1)
        country2 <- as.character(input$country2)
        
        data %>% filter(year >= startVal & year <= endVal & country %in% c(country1, country2))
    })
    
    # LIFE EXPECTANCY ----
    
    # Value Boxes - Top 5 KPIs | Life Expectancy
    output$kpi.top5.life1 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_top5_life()$lifeExp[1],1), "years"),
                 paste(var_maxDate_kpi_top5_life()$country[1], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "green")
    })
    
    output$kpi.top5.life2 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_top5_life()$lifeExp[2],1), "years"),
                 paste(var_maxDate_kpi_top5_life()$country[2], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "green")
    })
    
    output$kpi.top5.life3 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_top5_life()$lifeExp[3],1), "years"),
                 paste(var_maxDate_kpi_top5_life()$country[3], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "green")
    })
    
    output$kpi.top5.life4 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_top5_life()$lifeExp[4],1), "years"),
                 paste(var_maxDate_kpi_top5_life()$country[4], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "green")
    })
    
    output$kpi.top5.life5 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_top5_life()$lifeExp[5],1), "years"),
                 paste(var_maxDate_kpi_top5_life()$country[5], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "green")
    })
    
    # GGPLOT - Life Expectancy
    output$lineplotLife <- renderPlot({
        ggplot(var_date_and_country(), aes(x = year, y = lifeExp, color = country)) +
            geom_line(lwd = 1.5) +
            theme_grey() +
            labs(x = "Year",
                 y = "Life Expectancy",
                 title = paste("Life Expectancy Trend over time (", input$country1, " v ", input$country2, ")", sep = ""))
    })
    
    output$lineplotPopn <- renderPlot({
        ggplot(var_date_and_country(), aes(x = year, y = (pop/10^6), color = country)) +
            geom_line(lwd = 1.5) +
            theme_grey() +
            labs(x = "Year",
                 y = "Population (Millions)",
                 title = paste("Country Population in millions over time (", input$country1, " v ", input$country2, ")", sep = ""))
    })
    
    output$histLifeExp <- renderPlot({
        ggplot(data, aes(x = lifeExp, color = continent, fill = continent)) +
            geom_histogram() +
            theme_grey() +
            labs(x = "Life Expectancy",
                 y = "Frequency",
                 title = "Life Expectancy distribution by Continent")
    })
    
    # Value Boxes - Bottom 5 KPIs | Life Expectancy
    output$kpi.btm5.life1 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_btm5_life()$lifeExp[1],1), "years"),
                 paste(var_maxDate_kpi_btm5_life()$country[1], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "red")
    })
    
    output$kpi.btm5.life2 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_btm5_life()$lifeExp[2],1), "years"),
                 paste(var_maxDate_kpi_btm5_life()$country[2], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "red")
    })
    
    output$kpi.btm5.life3 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_btm5_life()$lifeExp[3],1), "years"),
                 paste(var_maxDate_kpi_btm5_life()$country[3], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "red")
    })
    
    output$kpi.btm5.life4 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_btm5_life()$lifeExp[4],1), "years"),
                 paste(var_maxDate_kpi_btm5_life()$country[4], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "red")
    })
    
    output$kpi.btm5.life5 <- renderValueBox({
        valueBox(paste(round(var_maxDate_kpi_btm5_life()$lifeExp[5],1), "years"),
                 paste(var_maxDate_kpi_btm5_life()$country[5], " (", input$dateEnd, ")", sep = ""), icon = icon("heart"), color = "red")
    })
    
    # GDP PER CAPITA ----
    
    # Value Boxes - Top 5 KPIs | GDP Per Capita
    output$kpi.top5.gdp1 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_top5_gdp()$gdpPercap[1]/1000,1), "k"),
                 paste(var_maxDate_kpi_top5_gdp()$country[1], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "green")
    })
    
    output$kpi.top5.gdp2 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_top5_gdp()$gdpPercap[2]/1000,1), "k"),
                 paste(var_maxDate_kpi_top5_gdp()$country[2], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "green")
    })
    
    output$kpi.top5.gdp3 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_top5_gdp()$gdpPercap[3]/1000,1), "k"),
                 paste(var_maxDate_kpi_top5_gdp()$country[3], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "green")
    })
    
    output$kpi.top5.gdp4 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_top5_gdp()$gdpPercap[4]/1000,1), "k"),
                 paste(var_maxDate_kpi_top5_gdp()$country[4], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "green")
    })
    
    output$kpi.top5.gdp5 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_top5_gdp()$gdpPercap[5]/1000,1), "k"),
                 paste(var_maxDate_kpi_top5_gdp()$country[5], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "green")
    })
    
    # GGPLOT - GDP Per Capita
    output$lineplotgdp <- renderPlot({
        ggplot(var_date_and_country(), aes(x = year, y = gdpPercap/1000, color = country)) +
            geom_line(lwd = 1.5) +
            theme_grey() +
            labs(x = "Year",
                 y = "GDP Per Capita (000's)",
                 title = paste("GDP Per Capita Trend over time (", input$country1, " v ", input$country2, ")", sep = ""))
    })
    
    output$lineplotPopn2 <- renderPlot({
        ggplot(var_date_and_country(), aes(x = year, y = (pop/10^6), color = country)) +
            geom_line(lwd = 1.5) +
            theme_grey() +
            labs(x = "Year",
                 y = "Population (Millions)",
                 title = paste("Country Population in millions over time (", input$country1, " v ", input$country2, ")", sep = ""))
    })
    
    output$histGDP <- renderPlot({
        ggplot(data, aes(x = gdpPercap, color = continent, fill = continent)) +
            geom_histogram() +
            theme_grey() +
            labs(x = "GDP Per Capita",
                 y = "Frequency",
                 title = "GDP Per Capita distribution by Continent")
    })
    
    # Value Boxes - Bottom 5 KPIs | GDP Per Capita
    output$kpi.btm5.gdp1 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_btm5_gdp()$gdpPercap[1]/1000,1), "k"),
                 paste(var_maxDate_kpi_btm5_gdp()$country[1], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "red")
    })
    
    output$kpi.btm5.gdp2 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_btm5_gdp()$gdpPercap[2]/1000,1), "k"),
                 paste(var_maxDate_kpi_btm5_gdp()$country[2], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "red")
    })
    
    output$kpi.btm5.gdp3 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_btm5_gdp()$gdpPercap[3]/1000,1), "k"),
                 paste(var_maxDate_kpi_btm5_gdp()$country[3], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "red")
    })
    
    output$kpi.btm5.gdp4 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_btm5_gdp()$gdpPercap[4]/1000,1), "k"),
                 paste(var_maxDate_kpi_btm5_gdp()$country[4], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "red")
    })
    
    output$kpi.btm5.gdp5 <- renderValueBox({
        valueBox(paste("$", round(var_maxDate_kpi_btm5_gdp()$gdpPercap[5]/1000,1), "k"),
                 paste(var_maxDate_kpi_btm5_gdp()$country[5], " (", input$dateEnd, ")", sep = ""), icon = icon("dollar-sign"), color = "red")
    })
    
    # LINEAR MODELLING ----
    
    # Format Predictor and Response col names
    var_EDA_plots <- reactive({
        str_col_predictor <- as.character(input$predictor)
        str_col_response <- as.character(input$response)
        transformed_data <- data %>% select(str_col_response, str_col_predictor, continent, country, year)
        transformed_data$predictor_base <- unlist(transformed_data[2])
        transformed_data$predictor_log <- unlist(log(transformed_data[2]))
        transformed_data$predictor_inv <- unlist(1/transformed_data[2])
        transformed_data$response <- unlist(transformed_data[1])
        transformed_data %>% select(continent, country, year, response, predictor_base, predictor_log, predictor_inv)
    })
    
    # Build linear models reactively
    var_lm_base <- reactive({
        fml <- as.formula("response ~ predictor_base")
        lm(fml, data = var_EDA_plots())
    })
    
    # Base EDA Scatter plot
    output$EDAplotBase <- renderPlot({
        ggplot(var_EDA_plots(), aes(x = predictor_base, y = response)) +
            geom_point(aes(color = continent)) +
            geom_smooth(method = "lm", se = TRUE) +
            theme_grey() +
            labs(x = paste(input$predictor, "(Predictor)"),
                 y = paste(input$response, "(Response)"),
                 title = paste(input$predictor, "v", input$response))
    })
    
    # Log EDA Scatter plot
    output$EDAplotLog <- renderPlot({
        ggplot(var_EDA_plots(), aes(x = predictor_log, y = response)) +
            geom_point(aes(color = continent)) +
            geom_smooth(method = "lm", se = TRUE) +
            theme_grey() +
            labs(x = paste("log(", input$predictor, ") (Predictor)", sep = ""),
                 y = paste(input$response, "(Response)"),
                 title = paste("log(", input$predictor, ") v ", input$response, sep = ""))
    })
    
    # Inv EDA Scatter plot
    output$EDAplotInv <- renderPlot({
        ggplot(var_EDA_plots(), aes(x = predictor_inv, y = response)) +
            geom_point(aes(color = continent)) +
            geom_smooth(method = "lm", se = TRUE) +
            theme_grey() +
            labs(x = paste("1/", input$predictor, " (Predictor)", sep = ""),
                 y = paste(input$response, "(Response)"),
                 title = paste("1/", input$predictor, " v ", input$response, sep = ""))
    })
    
    # Base LM Model
    output$base.lm <- renderText({
        summary(var_lm_base())
    })
    
    output$EDAtable <- renderTable({var_EDA_plots()})
    
}

shinyApp(ui, server)

任何帮助将不胜感激，谢谢:)

最佳答案

renderText()无法处理列表。使用 summary使用 lm 对象将产生长度为 11 的列表。修复它的最简单方法是将渲染函数更改为 renderPrint()反而。

# Base LM Model
output$base.lm <- renderPrint({
  summary(var_lm_base()) 
})

关于R [ Shiny ] : Error trying to output reactive model summary，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68241763/

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R [ Shiny ] : Error trying to output reactive model summary