回首向来萧瑟处！

最近将这半年学习的编程内容进行了整理，以飨读者，如有错误，烦请不吝指正。

Table 14-1.01: 人口学基线特征汇总表

# =============================================================================# 文件名: 01_table_14-1.01.R# 用途: 生成 Table 14-1.01 人口学及基线特征汇总表# 输入: ADaM ADSL# 输出: RTF 格式表格# =============================================================================source(here("02_programs/tfl/00_tfl_setup.R"))adsl <- read_xpt(here("03_output/adam/adsl.xpt"))# --- 方法一: 使用 rtables 包 (FDA 推荐) ---library(rtables)# 定义分析函数count_fraction <- function(x) {    in_rows(        "n (%)" = c(            sum(x == "Y", na.rm = TRUE),            sum(x == "Y", na.rm = TRUE) / length(x[!is.na(x)]) * 100        ),        format = "xx (xx.x)"    )}# 构建表格tbl <- basic_table() %>%  # 列分割: 按治疗组  split_cols_by("TRT01P") %>%  add_colcounts() %>%  # 总计数列  append_topleft("Table 14-1.01") %>%  append_topleft("Summary of Demographics and Baseline Characteristics") %>%  append_topleft("Safety Analysis Set") %>%  append_topleft("") %>%  # 年龄 (连续变量)  split_rows_by("AGEGR1", label_pos = "topleft") %>%  analyze("AGE", afun = list_wrap_x(summary),          format = "xx.xx", stat_names = c("Mean", "SD", "Median", "Min", "Max")) %>%  # 性别 (分类变量)  split_rows_by("SEX", label_pos = "topleft") %>%  analyze("SEX", afun = count_fraction) %>%  # 种族  split_rows_by("RACE", label_pos = "topleft") %>%  analyze("RACE", afun = count_fraction) %>%  # 年龄组  split_rows_by("AGEGR1", label_pos = "topleft") %>%  analyze("AGEGR1", afun = count_fraction) %>%  # 国家  split_rows_by("COUNTRY", label_pos = "topleft") %>%  analyze("COUNTRY", afun = count_fraction)# 过滤安全性分析集adsl_safety <- adsl %>% filter(SAFFL == "Y")# 生成表格result <- build_table(tbl, df = adsl_safety)# --- 输出为 RTF ---library(r2rtf)rtf_path <- here("03_output/tfls/tables/table_14-1.01.rtf")result %>%  rtf_encode() %>%  writeLines(rtf_path)message(sprintf("Table 14-1.01 已输出: %s", rtf_path))# --- 方法二: 使用 flextable (备选, 更灵活) ---library(flextable)summary_tbl <- adsl_safety %>%  group_by(TRT01P) %>%  summarise(    N = n(),    Age_Mean = mean(AGE, na.rm = TRUE),    Age_SD = sd(AGE, na.rm = TRUE),    Age_Median = median(AGE, na.rm = TRUE),    Male_n = sum(SEX == "M", na.rm = TRUE),    Male_pct = round(Male_n / N * 100, 1),    Female_n = sum(SEX == "F", na.rm = TRUE),    Female_pct = round(Female_n / N * 100, 1),    .groups = "drop"  )ft <- flextable(summary_tbl) %>%  set_header_labels(    TRT01P = "Treatment Group", N = "N",    Age_Mean = "Age (Years)\nMean",    Age_SD = "SD",    Age_Median = "Median",    Male_n = "Male\nn",    Male_pct = "(%)",    Female_n = "Female\nn",    Female_pct = "(%)"  ) %>%  add_header_lines(c(    "Table 14-1.01",    "Summary of Demographics and Baseline Characteristics",    "Safety Analysis Set"  )) %>%  theme_box() %>%  autofit()save_as_docx(ft, path = here("03_output/tfls/tables/table_14-1.01.docx"))