R 将多个汇总表与子目合并

我有几个人口统计因素的数据

我正在尝试创建一个出版物质量汇总表，如下所示：

                     N  
Sex
      M             150
      F             150
Marital Status
      Single        100
      Married       100
      Divorced      100
Age
      <25           75
      25-34         75
      35-44         75
      >= 45         75

但我找不到一种方法将它们组合成一个单独的表格，每个类别都有一个副标题。单独的表格有不同的列宽，手动对齐并插入中间的子目行（在raw LaTeX中？）似乎是一个糟糕的解决方案

---

这是一种非常常见的报告格式——许多期刊文章的标准表1——我想找到一个创建它的通用解决方案。

这不是一个完整的解决方案，但一个很好的起点可能是软件包

库（pander）
pander（列表（`Age groups`=年龄组，`Sex`=性别，`MULTINAL`=婚姻））
# 
# 
#***年龄组**：
# 
#     ---------------
#年龄组
#     ----------- ---
#         = 55     1
# 
#        25-34     1
# 
#        35-44     1
# 
#        45-54     1
#     ---------------
# 
#***性别**：
# 
#     ---------
#性别
#     ----- ---
#f2
# 
#M3
#     ---------
# 
#***婚姻**：
# 
#     --------------
#婚姻的
#     ---------- ---
#离婚1
# 
#已婚1
# 
#单曲3
#     --------------
# 
# 
# 
# 
# 

第一个输出：

第二输出：

---

我遇到了同样的问题，在用组标签添加空行之后，我偶然发现了kableExtra:：group_rows选项

通过这种方式，您可以对表中的某些行进行分组并给它们一个标签

在这里添加这些答案是因为我在三年后发现了这个问题，也许有人可以从中受益

require(dplyr)

dd <- data.frame(barcode = c("16929", "64605", "03086", "29356", "23871"),
                 sex = factor(c("M", "F", "M", "F", "M")), 
                 marital = factor(c("Married", "Single", "Single", "Single", "Divorced")),
                 age_group = factor(c("<25", "25-34", "35-44", "45-54", ">= 55")))

require(dplyr)

age_groups <- dd %>%
  group_by(age_group) %>%
  count() %>% 
  rename(variable = age_group) %>% 
  ungroup

sex <- dd %>% 
  group_by(sex) %>% 
  count() %>% 
  rename(variable = sex)%>% 
  ungroup

marital <- dd %>% 
  group_by(marital) %>% 
  count() %>% 
  rename(variable = marital)%>% 
  ungroup

freq_table <- age_groups %>% 
  add_row(sex) %>% 
  add_row(marital)

knitr::kable(freq_table) %>% 
  kableExtra::group_rows(group_label = "Age groups", start_row = 1, end_row = 5) %>% 
  kableExtra::group_rows(group_label = "Sex", start_row = 6, end_row = 7) %>% 
  kableExtra::group_rows(group_label = "Marital status", start_row = 8, end_row = 10)

require（dplyr）
dd%
计数（）%>%
重命名（变量=年龄组）%>%
解组
性别%
按性别划分的组别%>%
计数（）%>%
重命名（变量=性别）%>%
解组
婚姻%
组别(婚姻)%>%
计数（）%>%
重命名（变量=婚姻）%>%
解组
频率表%
添加_行（性别）%%>%
添加_行（婚姻）
knitr:：kable（频率表）%>%
kableExtra:：组行（组标签=“年龄组”，开始行=1，结束行=5）%>%
kableExtra:：组行（组标签=“性别”，开始行=6，结束行=7）%>%
kableExtra:：组行（组标签=“婚姻状况”，开始行=8，结束行=10）

---

不幸的是，我不能直接在这里添加图像，因为我还没有足够的声誉。

我通常的解决方案是将这些值放入一个矩阵中，其中的行和列名符合我在表中想要的，NA值符合我想要的空格。然后，我使用同名包中的

xtable

构建一个Latex表。关于如何让它跨越多行的帖子太多了，但要开始工作可能有点棘手。

require(knitr)
kable(age_groups)
kable(sex)
kable(marital)

library(pander)
pander(list(`Age groups` = age_groups, `Sex` = sex, `Marital` = marital))
# 
# 
#   * **Age groups**:
# 
#     ---------------
#      age_group   n
#     ----------- ---
#         <25      1
# 
#        >= 55     1
# 
#        25-34     1
# 
#        35-44     1
# 
#        45-54     1
#     ---------------
# 
#   * **Sex**:
# 
#     ---------
#      sex   n
#     ----- ---
#       F    2
# 
#       M    3
#     ---------
# 
#   * **Marital**:
# 
#     --------------
#      marital    n
#     ---------- ---
#      Divorced   1
# 
#      Married    1
# 
#       Single    3
#     --------------
# 
# 
# <!-- end of list -->
# 
# 

library(expss)
library(knitr)
dd = data.frame(barcode = c("16929", "64605", "03086", "29356", "23871"),
                 sex = factor(c("M", "F", "M", "F", "M")), 
                 marital = factor(c("Married", "Single", "Single", "Single", "Divorced")),
                 age_group = factor(c("<25", "25-34", "35-44", "45-54", ">= 55"), 
                                    levels = c("<25", "25-34", "35-44", "45-54", ">= 55")))


dd %>% tab_cells("Sex" = sex, "Marital status" = marital, "Age" = age_group) %>% 
    tab_cols(total(label = "N")) %>% 
    tab_stat_cases(total_row_position = "none") %>% 
    tab_pivot()

dd %>% tab_cells("Sex" = sex, "Marital status" = marital, "Age" = age_group) %>% 
    tab_cols(total(label = "N")) %>% 
    tab_stat_cases(total_row_position = "none") %>% 
    tab_pivot() %>% 
    split_columns() %>% 
    kable()

require(dplyr)

dd <- data.frame(barcode = c("16929", "64605", "03086", "29356", "23871"),
                 sex = factor(c("M", "F", "M", "F", "M")), 
                 marital = factor(c("Married", "Single", "Single", "Single", "Divorced")),
                 age_group = factor(c("<25", "25-34", "35-44", "45-54", ">= 55")))

require(dplyr)

age_groups <- dd %>%
  group_by(age_group) %>%
  count() %>% 
  rename(variable = age_group) %>% 
  ungroup

sex <- dd %>% 
  group_by(sex) %>% 
  count() %>% 
  rename(variable = sex)%>% 
  ungroup

marital <- dd %>% 
  group_by(marital) %>% 
  count() %>% 
  rename(variable = marital)%>% 
  ungroup

freq_table <- age_groups %>% 
  add_row(sex) %>% 
  add_row(marital)

knitr::kable(freq_table) %>% 
  kableExtra::group_rows(group_label = "Age groups", start_row = 1, end_row = 5) %>% 
  kableExtra::group_rows(group_label = "Sex", start_row = 6, end_row = 7) %>% 
  kableExtra::group_rows(group_label = "Marital status", start_row = 8, end_row = 10)