不知道你是否找到了解决这个问题的办法，但以下是我所做的：

• 如果你使用tab_style()，你不需要尝试创建颜色矢量，而是可以根据dose列设置你想要的背景颜色。如果你想根据dose给值不同的颜色，除了我在这里已经着色的，然后为所需的值创建另一个tab_style()。

library(gt)
 library(dplyr)
 library(scales)
 library(glue)

 ToothGrowth %>%
   mutate(len_dose = glue('{len}: ({dose})')) %>%
   gt(rowname_col = 'supp') %>%
   tab_style(
     style = cell_fill(color = "palegreen"),
     location = cells_body(
       columns = len_dose,
       rows = dose >= 1.0
     )
   ) %>%
   cols_hide(c(len, dose))

更新日期：2023年2月

gt包中添加了基于另一列的颜色选项-data_color()获得了一个taregt_columns参数，因此这变得简单多了：

library(gt)
library(dplyr)

ToothGrowth %>%
  mutate(len_dose = glue('{len}: ({dose})')) %>%
  gt(rowname_col = 'supp') %>%
  cols_hide(c(len, dose)) %>%
  data_color(columns = "dose", target_columns = "len_dose",
             palette = "ggsci::green_material")

过时

我也遇到了同样的问题，并调整了gt：：data_color函数，使其接受单独的源列和目标列--这样，下面的代码就可以产生所需的输出。

# Distinguish SOURCE_columns and TARGET_columns

my_data_color <- function (data, SOURCE_columns, TARGET_columns, colors, alpha = NULL, apply_to = c("fill", 
                                                                                                    "text"), autocolor_text = TRUE) 
{
  stop_if_not_gt(data = data)
  apply_to <- match.arg(apply_to)
  colors <- rlang::enquo(colors)
  data_tbl <- dt_data_get(data = data)
  colors <- rlang::eval_tidy(colors, data_tbl)
  resolved_source_columns <- resolve_cols_c(expr = {
    {
      SOURCE_columns
    }
  }, data = data)
  resolved_target_columns <- resolve_cols_c(expr = {
    {
      TARGET_columns
    }
  }, data = data)
  rows <- seq_len(nrow(data_tbl))
  data_color_styles_tbl <- dplyr::tibble(locname = character(0), 
                                         grpname = character(0), colname = character(0), locnum = numeric(0), 
                                         rownum = integer(0), colnum = integer(0), styles = list())
  for (i in seq_along(resolved_source_columns)) {
    data_vals <- data_tbl[[resolved_source_columns[i]]][rows]
    if (inherits(colors, "character")) {
      if (is.numeric(data_vals)) {
        color_fn <- scales::col_numeric(palette = colors, 
                                        domain = data_vals, alpha = TRUE)
      }
      else if (is.character(data_vals) || is.factor(data_vals)) {
        if (length(colors) > 1) {
          nlvl <- if (is.factor(data_vals)) {
            nlevels(data_vals)
          }
          else {
            nlevels(factor(data_vals))
          }
          if (length(colors) > nlvl) {
            colors <- colors[seq_len(nlvl)]
          }
        }
        color_fn <- scales::col_factor(palette = colors, 
                                       domain = data_vals, alpha = TRUE)
      }
      else {
        cli::cli_abort("Don't know how to map colors to a column of class {class(data_vals)[1]}.")
      }
    }
    else if (inherits(colors, "function")) {
      color_fn <- colors
    }
    else {
      cli::cli_abort("The `colors` arg must be either a character vector of colors or a function.")
    }
    color_fn <- rlang::eval_tidy(color_fn, data_tbl)
    color_vals <- color_fn(data_vals)
    color_vals <- html_color(colors = color_vals, alpha = alpha)
    color_styles <- switch(apply_to, fill = lapply(color_vals, 
                                                   FUN = function(x) cell_fill(color = x)), text = lapply(color_vals, 
                                                                                                          FUN = function(x) cell_text(color = x)))
    data_color_styles_tbl <- dplyr::bind_rows(data_color_styles_tbl, 
                                              generate_data_color_styles_tbl(column = resolved_target_columns[i], rows = rows, 
                                                                             color_styles = color_styles))
    if (apply_to == "fill" && autocolor_text) {
      color_vals <- ideal_fgnd_color(bgnd_color = color_vals)
      color_styles <- lapply(color_vals, FUN = function(x) cell_text(color = x))
      data_color_styles_tbl <- dplyr::bind_rows(data_color_styles_tbl, 
                                                generate_data_color_styles_tbl(column = resolved_target_columns[i], 
                                                                               rows = rows, color_styles = color_styles))
    }
  }
  dt_styles_set(data = data, styles = dplyr::bind_rows(dt_styles_get(data = data), 
                                                       data_color_styles_tbl))
}

# Add function into gt namespace (so that internal gt functions can be called)
library(gt)
tmpfun <- get("data_color", envir = asNamespace("gt"))
environment(my_data_color) <- environment(tmpfun)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(glue)

# Map dose to color
ToothGrowth %>%
  mutate(len_dose = glue('{len}: ({dose})')) %>%
  gt(rowname_col = 'supp') %>%
  cols_hide(c(len, dose)) %>%
  my_data_color(SOURCE_columns = "dose", TARGET_columns = "len_dose", 
             colors = scales::col_numeric(palette = c("red", "green"), domain = c(min(ToothGrowth$dose), max(ToothGrowth$dose))))

创建于2022年11月3日，使用reprex v2.0.2