总结一组数据帧——改进一个笨拙的解决方案

Question

我有一组数据框，df_i，代表一组患者第 i 次到医院就诊。我想总结每个数据框以确定第 i 次就诊时男性、女性和患者总数。虽然我可以解决这个问题，但我的解决方案很笨拙。有没有更简单的方法来获得我想要的最终数据框？示例如下：

df_1 <- data.frame(
  ID     = c(rep("A",4), rep("B",3), rep("C",2), "D"),
  Dates  = seq.Date(from = as.Date("2020-01-01"), to = as.Date("2020-01-10"), by = "day"),
  Sex    = c(rep("Male",4), rep("Male",3), rep("Female",2), "Female"),
  Weight = seq(100, 190, 10),
  Visit  = rep(1, 10)
)

df_2 <- data.frame(
  ID     = c(rep("A",4), rep("B",3), rep("C",2)),
  Dates  = seq.Date(from = as.Date("2020-02-01"), to = as.Date("2020-02-9"), by = "day"),
  Sex    = c(rep("Male",4), rep("Male",3), rep("Female",2)),
  Weight = seq(100, 180, 10),
  Visit  = rep(2, 5)
)

df_3 <- data.frame(
  ID     = c(rep("A",4), rep("B",3)),
  Dates  = seq.Date(from = as.Date("2020-03-01"), to = as.Date("2020-03-07"), by = "day"),
  Sex    = rep("Male",7),
  Weight = seq(140, 200, 10),
  Visit  = rep(3, 7)
)

我希望生成以下结果：

> df_sum
  Visit Patients Men Women
1     1        4   2     2
2     2        3   2     1
3     3        2   2     0

我可以用一种非常笨拙的方式来做到这一点：首先创建一个临时数据框来汇总 df_1

中的信息

df_tmp <- df_1 %>%
            group_by(ID) %>%
            filter(Dates == min(Dates)) %>%
            summarize(n = n(), Men = sum(Sex == "Male"), Women = sum(Sex == "Female"))
> df_tmp
# A tibble: 4 x 4
  ID        n   Men Women
  <chr> <int> <int> <int>
1 A         1     1     0
2 B         1     1     0
3 C         1     0     1
4 D         1     0     1

接下来，对 df_tmp 中的每一列求和以创建摘要列的第一行。

r1 <- c(sum(df_tmp$n), sum(df_tmp$Men), sum(df_tmp$Women))

重复第二个和第三个数据帧。最后将行绑定在一起以创建摘要数据框。虽然这有效，但它非常笨拙，并且不能推广到我有可变访问次数的情况。有人可以为我的问题指出一个更优雅的解决方案吗？

非常感谢

托马斯·飞利浦

Answer 1

将数据放入列表中并通过 map 遍历它们，这样您就不必为每个数据帧重复代码。使用 janitor::adorn_totals 您可以在输出中添加一个包含总计的新行，并以宽格式获取数据。

library(tidyverse)

list_df <- list(df_1, df_2, df_3)

map_df(list_df, ~.x %>% 
              group_by(ID) %>%
              filter(Dates == min(Dates)) %>%
              ungroup %>%
              count(Sex) %>%
              janitor::adorn_totals(name = 'Patients'), .id = 'Visit') %>%
  pivot_wider(names_from = Sex, values_from = n, values_fill = 0)

#  Visit Female  Male Patients
#  <chr>  <int> <int>    <int>
#1 1          2     2        4
#2 2          1     2        3
#3 3          0     2        2

Answer 2

也可以用 bind_rows:

library(tidyverse)
bind_rows(df_1, df_2, df_3, .id = "day") %>%
  group_by(day, ID) %>%
  slice_min(Dates) %>%
  group_by(day) %>%
  summarize(n = n(), Men = sum(Sex == "Male"), Women = sum(Sex == "Female"))

结果

# A tibble: 3 x 4
  day       n   Men Women
* <chr> <int> <int> <int>
1 1         4     2     2
2 2         3     2     1
3 3         2     2     0

总结一组数据帧——改进一个笨拙的解决方案

Summarizing a collection of data frames - improving upon a clumsy solution

r

dataframe

summarize