R Shiny绘制雷达图报错：数据需为data.frame问题求助

核心问题 #

1. 数据类型不匹配：你通过industry$Centralgov提取的是单列向量，而radarchart()要求输入包含最大值（Max）、最小值（Min）的完整数据框，且格式要符合fmsb包的要求。
2. 变量名笔误："Other"分支里写了counties$Other，但你的数据框变量是industry，属于拼写错误。

修正后的服务器逻辑 #

把server部分替换成以下代码，核心是构建符合雷达图要求的数据结构：

server <- function(input, output) {
  output$radarPlot <- renderPlot({
    # 映射用户选择到数据框的列名
    col_name <- switch(input$var, 
                   "Central government" = "Centralgov",
                   "Local government" = "Localgov",
                   "IT and Telecoms" = "IT",
                   "Professional services, law and accountancy" = "PS",
                   "Finance, banking and insurance" = "Finance",
                   "Health" = "Health",
                   "Social care/housing association" = "Social",
                   "Other charity/voluntary sector" = "Charity",
                   "Retail" = "Retail",
                   "Engineering" = "Engineering",
                   "Manufacturing" = "Manufacturing",
                   "Pharmaceutical" = "Pharmaceutical",
                   "Transport" = "Transport",
                   "Utilities" = "Utilities",
                   "Hospitality" = "Hospitality",
                   "Education (HE, FE)" = "Education",
                   "Consulting" = "Consulting",
                   "Art, media and design" = "Art",
                   "Other" = "Other")
    
    # 提取Max、Min和选中行业的数据，转置后符合radarchart要求的格式
    data <- industry[, c("Max", "Min", col_name)]
    data_t <- t(data)
    rownames(data_t) <- c("Max", "Min", input$var)
    
    # 绘制雷达图，添加样式优化可读性
    radarchart(data_t,
               title = paste("L&D Capabilities -", input$var),
               vlabels = rownames(data),
               cglcol = "gray", cglty = 1, cglwd = 0.8,
               pcol = "#2E86AB", plwd = 2, plty = 1)
  })
}

为什么这样改？ #

• fmsb的雷达图要求数据结构为：每行是一个观测（Max、Min、目标行业），每列是一个指标（L&D能力），你的原始数据是指标在行、观测在列，所以需要用t()转置。
• 必须包含Max和Min行，雷达图才能确定每个指标的刻度范围，否则无法生成有效图表。
• 修正了counties$Other的笔误，确保数据提取正确。

完整可运行代码 #

# Load packages ----
library(shiny)
library(fmsb)

# Load data ----
industry <- structure(list(Max = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), Min = c(0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Centralgov = c(0.6, 
0.18, 0.27, 0.27, 0.27, 0.27, 0.36, 0.3, 0.55, 0.45, 0.1, 0, 
0.1, 0.27, 0.64, 0.09, 0.09, 0.18, 0.27, 0, 0.09, 0.18, 0.25, 
0.29, 0.14), Localgov = c(0.36, 0.5, 0.36, 0.5, 0.42, 0.42, 0.09, 
0.27, 0.36, 0.55, 0.3, 0.36, 0.55, 0.45, 0.73, 0.36, 0.18, 0.45, 
0.64, 0.36, 0.27, 0.18, 0.3, 0.2, 0.6), IT = c(0.73, 0.33, 0.47, 
0.51, 0.38, 0.18, 0.34, 0.38, 0.62, 0.41, 0.19, 0.38, 0.49, 0.41, 
0.62, 0.32, 0.22, 0.38, 0.58, 0.51, 0.33, 0.34, 0.41, 0.15, 0.37
), PS = c(0.73, 0.4, 0.56, 0.6, 0.48, 0.48, 0.29, 0.24, 0.63, 
0.56, 0.29, 0.41, 0.27, 0.36, 0.71, 0.28, 0.16, 0.48, 0.4, 0.52, 
0.36, 0.38, 0.29, 0.25, 0.13), Finance = c(0.9, 0.44, 0.66, 0.66, 
0.61, 0.52, 0.44, 0.5, 0.86, 0.62, 0.32, 0.39, 0.48, 0.59, 0.86, 
0.3, 0.27, 0.5, 0.52, 0.52, 0.57, 0.51, 0.56, 0.33, 0.29), Health = c(0.88, 
0.33, 0.47, 0.65, 0.28, 0.37, 0.33, 0.29, 0.78, 0.47, 0.18, 0.13, 
0.47, 0.5, 0.78, 0.26, 0.16, 0.41, 0.58, 0.5, 0.38, 0.39, 0.33, 
0.13, 0.29), Social = c(0.7, 0.25, 0.5, 0.33, 0.3, 0.2, 0.1, 
0.4, 0.5, 0.2, 0, 0.22, 0, 0.2, 0.4, 0.1, 0.3, 0.1, 0.3, 0.3, 
0.33, 0.3, 0.33, 0, 0.11), Charity = c(0.8, 0.55, 0.62, 0.44, 
0.5, 0.31, 0.08, 0.33, 0.58, 0.5, 0.4, 0.36, 0.33, 0.38, 0.82, 
0.15, 0.08, 0.36, 0.22, 0.42, 0.2, 0.42, 0.18, 0.22, 0.11), Retail = c(0.62, 
0.38, 0.46, 0.27, 0.25, 0.09, 0.08, 0.31, 0.82, 0.46, 0.25, 0.27, 
0.25, 0.54, 0.69, 0.08, 0.17, 0.31, 0.67, 0.5, 0.33, 0.5, 0.38, 
0.18, 0.08), Engineering = c(0.6, 0, 0.4, 0.25, 0.17, 0.17, 0, 
0, 0.33, 0.5, 0.25, 0.33, 0.6, 0.17, 0.33, 0, 0, 0.33, 0.33, 
0.17, 0.17, 0.5, 0.2, 0, 0), Manufacturing = c(0.56, 0.22, 0.35, 
0.42, 0.42, 0.4, 0.24, 0.2, 0.56, 0.41, 0.24, 0.11, 0.21, 0.3, 
0.63, 0.1, 0, 0.25, 0.42, 0.58, 0.21, 0.35, 0.25, 0.33, 0.06), 
    Pharmaceutical = c(0.43, 0.25, 0, 0.71, 0.63, 0.25, 0.13, 
    0.13, 0.63, 0.43, 0, 0, 0, 0, 0.38, 0.25, 0.13, 0.38, 0.38, 
    0.5, 0, 0, 0.33, 0, 0.17), Transport = c(0.77, 0.62, 0.79, 
    0.57, 0.71, 0.64, 0.14, 0.5, 0.79, 0.46, 0.38, 0.21, 0.36, 
    0.38, 0.64, 0.43, 0.29, 0.21, 0.57, 0.64, 0.29, 0.54, 0.57, 
    0.36, 0.15), Utilities = c(1, 0.6, 0.4, 0.33, 0.2, 0.2, 0.6, 
    0.6, 0.8, 0.6, 0.25, 0.2, 0.8, 0.4, 1, 0.4, 0.4, 0.6, 0.4, 
    0.6, 0.2, 0.2, 0.2, 0.2, 0), Hospitality = c(0.67, 0, 0.67, 
    0.4, 0.67, 0.33, 0.33, 0.83, 0.83, 0.2, 0.67, 0.17, 0.2, 
    0.33, 0.83, 0.33, 0.33, 0, 0.67, 1, 0.5, 0.33, 0.33, 0.6, 
    0.33), Education = c(0.87, 0.33, 0.47, 0.53, 0.41, 0.38, 
    0.5, 0.47, 0.65, 0.41, 0.2, 0.31, 0.47, 0.65, 0.53, 0.24, 
    0.29, 0.38, 0.56, 0.41, 0.31, 0.19, 0.38, 0.27, 0.35), Consulting = c(0.67, 
    0.5, 0.67, 1, 0.33, 0.33, 0.17, 0.5, 1, 0.6, 0.33, 0.6, 0.4, 
    0.67, 0.5, 0.17, 0.17, 0.4, 0.6, 0.5, 0.5, 0.33, 0.4, 0.25, 
    0.25), Art = c(1, 0.2, 0.6, 0.5, 0.4, 0.2, 0.2, 0.6, 0.6, 
    0.4, 0.2, 0.2, 0.6, 0.5, 0.5, 0.2, 0.2, 0.2, 0.2, 0.6, 0.4, 
    0.4, 0.4, 0.2, 0.25), Other = c(0.67, 0.57, 0.71, 0.29, 0.57, 
    0.43, 0.14, 0.5, 0.67, 0.29, 0.57, 0.29, 0.43, 0.57, 0.71, 
    0.29, 0.43, 0.29, 0.43, 0.57, 0.71, 0.43, 0.5, 0.6, 0.4)), class = "data.frame", row.names = c("In-person classroom delivery", 
"Strategy and governance", "Stakeholder engagement", "Instructional design", 
"Crafting learning journeys / blended solutions", "Supporting ongoing workplace performance", 
"Facilitating social and collaborative learning", "Understanding learner behaviour", 
"Virtual classroom / webinar delivery", "Digital content development", 
"Performance consulting", "Business acumen", "Marketing and communications", 
"Coaching and mentoring", "Learning management / administration", 
"Analytics / data management", "Evaluating impact", "Technology/infrastructure", 
"Project management", "Leveraging L&D expertise", "Knowledge management", 
"Negotiation, persuasion, and influence", "Learning experience design", 
"Community engagement", "Research capabilities"))

# User interface ----
ui <- fluidPage(
  titlePanel("L&D Capabilities 2023"),
  
  sidebarLayout(
    sidebarPanel(
      helpText("Check which L&D capabilities your industry
      has in-house in 2023."),
      
      selectInput("var", 
                  label = "Choose a variable to display",
                  choices = c("Central government", 
                              "Local government",
                              "IT and Telecoms", 
                              "Professional services, law and accountancy", 
                              "Finance, banking and insurance", 
                              "Health", 
                              "Social care/housing association", 
                              "Other charity/voluntary sector", 
                              "Retail", 
                              "Engineering", 
                              "Manufacturing", 
                              "Pharmaceutical", 
                              "Transport", 
                              "Utilities", 
                              "Hospitality", 
                              "Education (HE, FE)", 
                              "Art, media and design", 
                              "Other", 
                              "Consulting"),
                  selected = "Central government"),
    ),
    
    mainPanel(plotOutput("radarPlot"))
  )
)

# Server logic ----
server <- function(input, output) {
  output$radarPlot <- renderPlot({
    col_name <- switch(input$var, 
                   "Central government" = "Centralgov",
                   "Local government" = "Localgov",
                   "IT and Telecoms" = "IT",
                   "Professional services, law and account