我在forestplot软件包中遇到了同样的问题，特别是分组forestplot --这个问题是唯一的“标签”，在这种情况下，你需要为每一行都有完全相同的标签才能有一个分组图。
在您的示例中，您有9个唯一值标签，而您只需要在尝试绘制的三个组之间共享三个标签。

library(tidyverse)
library(forestplot)
#> Loading required package: grid
#> Loading required package: checkmate
#> Loading required package: abind
# Checking erros in 'forestplot' package
# 1. Loading data-Frame
x <- structure(list(
      cyl = c("4", "4", "4", "6", "6", "6", "8", "8", "8"), 
      term = c("(Intercept)", "hp", "drat", "(Intercept)", "hp",  "drat", "(Intercept)", "hp", "drat"), 
      estimate = c(22.5842331369155, -0.0896163727194673, 2.82123086860038, 19.276290671286, -0.00955656724590332, 0.45603134914307, 11.5738076494898, -0.0286147185507174, 2.94579083260769), 
      std.error = c(20.1598442589198, 0.0714382978379768, 4.09205373078535, 5.95554159948471, 0.030110518391565, 1.53447737693666, 6.2934127394834, 0.018398031862226, 2.5187181104732), 
      statistic = c(1.120258313847, -1.25445839880898, 0.689441306055121, 3.23669818257232, -0.317383019502595, 0.297190011398842, 1.83903521484908, -1.55531410995476, 1.16955955506043), 
      p.value = c(0.29510529382976, 0.245084968663166, 0.510044820506449, 0.0317731650193357, 0.766829525981112, 0.781115912768507, 0.093035417836973, 0.148154964992997, 0.266896538389878)), 
      row.names = c(NA, -9L), class = c("tbl_df", "tbl", "data.frame")) %>%
      mutate(
            mean = estimate,
            lower= estimate-(1.96*std.error),
            upper= estimate+(1.96*std.error)) %>%
      select(cyl, term, mean, std.error, lower, upper, p.value) %>%
      group_by(cyl)

# 2. Setting unique terms do display on forest plot and getting mean, lower and upper
lblid <- substitute(c(term, p.value))
mean <- rlang::as_name("mean")
lower <- rlang::as_name("lower")
upper <- rlang::as_name("upper")

# 3. Getting group variable Names
groups <- attr(x, "groups") |>
      dplyr::select(-.rows & where(\(col) length(unique(col)) > 1)) |>
      colnames()

# 4. Creating a fixed Column
core_data <- x |>
      dplyr::ungroup() |>
      dplyr::select({{ lblid }},
                    mean = {{ mean }},
                    lower = {{ lower }},
                    upper = {{ upper }}) |>
      dplyr::bind_cols(x |>
                             tidyr::unite(".fp_groups", dplyr::all_of(groups), sep = " > ", remove = FALSE) |>
                             tidyr::unite(".fp_labels", {{lblid}}, sep = " > ") |>
                             dplyr::select(dplyr::starts_with(".fp"), dplyr::all_of(groups))) |>
      dplyr::group_by(.fp_groups)

# 5. Creating all possible Labels to be Created
all_labels <- core_data |>
      tidyr::nest() |>
      dplyr::pull(data) |>
      lapply(\(x) x$.fp_labels) |>
      unlist() |>
      unique()

all_labels
#> [1] "(Intercept) > 0.29510529382976"   "hp > 0.245084968663166"          
#> [3] "drat > 0.510044820506449"         "(Intercept) > 0.0317731650193357"
#> [5] "hp > 0.766829525981112"           "drat > 0.781115912768507"        
#> [7] "(Intercept) > 0.093035417836973"  "hp > 0.148154964992997"          
#> [9] "drat > 0.266896538389878"

# 6. Cheking problems with Levels
bad_rows <- core_data |>
      dplyr::mutate(level = sapply(.fp_labels, \(lbl) which(all_labels == lbl)[[1]])) |>
      dplyr::filter(level > dplyr::lead(level))
if (nrow(bad_rows) > 0) {
      stop("There are seem be invalid the labels: ", bad_rows$.fp_labels |> paste(collapse = ", "),
           "\n appear in the wrong position.")
}

# 7. Checking problems with Duplicates
bad_rows <- core_data |>
      dplyr::group_by(.fp_groups, .fp_labels) |>
      dplyr::summarise(n = dplyr::n(), .groups = "drop") |>
      dplyr::filter(n > 1)

if (nrow(bad_rows) > 0) {
      stop("There are seem be non-unique labels: ", bad_rows$.fp_labels |> paste(collapse = ", "))
}

# 8. Nesting data-frame and checking unique values per group (they must share same values)
temp_df <- core_data |>
      tidyr::nest()

# -- Showing Lables per Group
temp_df$data[[1]][['.fp_labels']]
#> [1] "(Intercept) > 0.29510529382976" "hp > 0.245084968663166"        
#> [3] "drat > 0.510044820506449"
temp_df$data[[2]][['.fp_labels']]
#> [1] "(Intercept) > 0.0317731650193357" "hp > 0.766829525981112"          
#> [3] "drat > 0.781115912768507"
temp_df$data[[3]][['.fp_labels']]
#> [1] "(Intercept) > 0.093035417836973" "hp > 0.148154964992997"         
#> [3] "drat > 0.266896538389878"

for(df in temp_df$data){
      for (i in 1:length(all_labels)){
            if (df$.fp_labels[i] != all_labels[i]) {
                  new_row <- core_data |>
                        dplyr::ungroup() |>
                        dplyr::select({{lblid}}, .fp_labels) |>
                        dplyr::filter(.fp_labels == all_labels[i]) |>
                        dplyr::distinct(.fp_labels, .keep_all = TRUE)
                  
                  df <- tibble::add_row(df,
                                        new_row,
                                        .before = i)
            }
            
            
      }
}
#> Error in if (df$.fp_labels[i] != all_labels[i]) {: missing value where TRUE/FALSE needed