使用group_by進行更廣泛的總結

我有一個資料集，其中包含具有疫苗接種狀態的 COVID-19 患者以及他們是死是活。

ID <- c(1:20)
Group <- c("1. vacc   unvacc", "2. vacc", "3. vacc", "1. vacc   unvacc", "2. vacc", "3. vacc", "1. vacc   unvacc", "2. vacc", "3. vacc",
           "1. vacc   unvacc", "2. vacc", "3. vacc", "1. vacc   unvacc", "2. vacc", "3. vacc", "1. vacc   unvacc", "2. vacc", "3. vacc",
           "1. vacc   unvacc", "2. vacc")
Status <- c("Dead", "Alive", "Dead", "Alive", "Dead", "Alive", "Dead", "Alive", "Dead", "Alive", "Dead", "Alive", 
            "Dead", "Alive", "Dead", "Alive", "Dead", "Alive", "Dead", "Alive")

df <- data.frame(ID, Group, Status)

到目前為止，我已經嘗試撰寫了一個代碼，我可以做到這一點：

library(tidyverse)

df_organ %>% 
  mutate_at("Group", as.character) %>%
  list(group_by(.,Group, Status), .) %>%
  map(~summarize(.,cnt = n())) %>%
  bind_rows() %>%
  replace_na(list(Group="Overall"))

給我輸出：

    `summarise()` has grouped output by 'Group'. You can override using the `.groups` argument.
# A tibble: 7 x 3
# Groups:   Group [4]
  Group            Status   cnt
  <chr>            <chr>  <int>
1 1. vacc   unvacc Alive      3
2 1. vacc   unvacc Dead       4
3 2. vacc          Alive      4
4 2. vacc          Dead       3
5 3. vacc          Alive      3
6 3. vacc          Dead       3
7 Overall          NA        20

我正在尋找的輸出是這樣的：

    `summarise()` has grouped output by 'Group'. You can override using the `.groups` argument.
    # A tibble: 10 x 3
    # Groups:   Group [4]
      Group            Status   cnt
      <chr>            <chr>  <int>
    1 1. vacc   unvacc Alive      3
    2 1. vacc   unvacc Dead       4
    3 1. uvac   unvacc All        7
    4 2. vacc          Alive      4
    5 2. vacc          Dead       3
    6 2. vacc          All        7
    5 3. vacc          Alive      3
    6 3. vacc          Dead       3
    7 3. vacc          All        6
    8 Overall          Alive     10
    9 Overall          Dead      10
   10 Overall          All       20 

uj5u.com熱心網友回復：

我們可以這樣做：

1. 首先我們數數。我們使用count來自 的函式dplyr。好處count是它繼承了group_by和summarise。
2. 然后我們用pivot_widerfromtidyr包制作寬格式
3. 接下來我們使用方便的janitor包來獲取 rowsums 和 colsums。（我們也可以用 base 來做到這一點......）
4. 然后通過重命名列回到長格式

library(dplyr)
library(tidyr)
library(janitor)

df %>% 
  count(Group, Status) %>% 
  pivot_wider(
    names_from = Status,
    values_from = n
  ) %>% 
  adorn_totals("col", name = "All") %>% 
  adorn_totals("row", name = "Ovreall") %>% 
  pivot_longer(
    cols= -Group,
    names_to = "Status", 
    values_to = "cnt"
  )

   Group            Status   cnt
   <chr>            <chr>  <dbl>
 1 1. vacc   unvacc Alive      3
 2 1. vacc   unvacc Dead       4
 3 1. vacc   unvacc All        7
 4 2. vacc          Alive      4
 5 2. vacc          Dead       3
 6 2. vacc          All        7
 7 3. vacc          Alive      3
 8 3. vacc          Dead       3
 9 3. vacc          All        6
10 Ovreall          Alive     10
11 Ovreall          Dead      10
12 Ovreall          All       20

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