如何让R gtsum程序包tbl_strata()函数不为一个完全缺少某一层的值的变量打印带有(nA%)的零

解决这两个问题的一个 Select 是使用modify_table_body.首先，要摆脱0 (NA%)个单元格，您可以将Auto的stat列设置为NA，以获得"label"行.第二，为了得到正确的顺序，你可以将specific_beauty_shop_type转换为一个因子.但是，这将为非缺失类别添加0 (NA%)个单元格，我们可以通过将"level"行替换为NA来消除这些单元格.

library(gtsummary)
library(dplyr)

test.tb %>%
  mutate(specific_beauty_shop_type = factor(specific_beauty_shop_type)) |> 
  tbl_strata(
    strata = auto_or_beauty,
    .tbl_fun =
      ~ .x %>%
        tbl_summary(by = intervention_group)
  ) |>
  modify_table_body(
    fun = ~ .x |>
      mutate(
        across(
          c(stat_1_1, stat_2_1),
          ~ if_else(row_type %in% c("label", "level"), NA, .x)
        )
      )
  )

根据您的意见，您可以根据if_else中的variable来限制NA的更换:

test.tb <- tibble(
  auto_or_beauty = c("Beauty", "Beauty", "Beauty", "Beauty", "Beauty", "Auto", "Auto", "Beauty", "Beauty", "Beauty"),
  specific_beauty_shop_type = c("Hair only", "Hair and nails", "Hair only", "Hair and nails", "Hair only", NA, NA, "Hair and nails", "Hair only", "Hair only"),
  intervention_group = c("Delayed", "Delayed", "Delayed", "Immediate", "Immediate", "Delayed", "Immediate", "Delayed", "Immediate", "Immediate"),
  home_business = c("Yes", "Yes", "No", "No", "No", "No", "No", "Yes", "Yes", "No")
)

test.tb %>%
  mutate(specific_beauty_shop_type = factor(specific_beauty_shop_type)) |>
  tbl_strata(
    strata = auto_or_beauty,
    .tbl_fun =
      ~ .x %>%
        tbl_summary(by = intervention_group)
  ) |>
  modify_table_body(
    fun = ~ .x |>
      mutate(
        across(
          c(stat_1_1, stat_2_1),
          ~ if_else(
            variable %in% "specific_beauty_shop_type" &
              row_type %in% c("label", "level"), NA, .x
          )
        )
      )
  )

第EDIT 2章现在是一团糟我认为下一个附加问题需要另一个问题.(;不幸的是，这种情况没有简单的解决办法，但可以通过编写一个自定义测试函数来解决使用tryCatch的"错误"情况.这样我们就可以捕获错误并返回一个NA的列表.

my_fisher <- function(x, y) {
  tryCatch(
    fisher.test(x, y),
    error = function(e) {
      res <- list(
        p.value = NA,
        method = NA
      )
      class(res) <- "htest"
      res
    }
  )
}

fisher_test <- function(data, variable, by, ...) {
  data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
  my_fisher(data[[variable]], factor(data[[by]])) |>
    broom::tidy()
}

tbl <- test.tb %>%
  mutate(specific_beauty_shop_type = factor(specific_beauty_shop_type)) |>
  tbl_strata(
    strata = auto_or_beauty,
    .tbl_fun =
      ~ .x %>%
        tbl_summary(by = intervention_group) %>%
        add_p(everything() ~ "fisher_test") %>%
        separate_p_footnotes()
  ) |>
  modify_table_body(
    fun = ~ .x |>
      mutate(
        across(
          c(stat_1_1, stat_2_1),
          ~ if_else(
            variable %in% "specific_beauty_shop_type" &
              row_type %in% c("label", "level"), NA, .x
          )
        )
      )
  )