如何對 R 中的數據集的值進行重新分類並聚合行？

Question

我需要聚合數據集的行以折疊年齡范圍。 我的數據集目前有 5 歲的年齡范圍。 我試圖將這些年齡范圍組合成類別，同時對一些變量（人口、X1、X2、X3 和 X4）求和，同時保持變量“類別”，該變量對於該特定 ID 中的每一行都是相同的。

我的數據集如下所示：

ID    Age.Range    Population   X1   X2   X3   X4   Category
1     05-09 years  10           1    0    0    1    a
1     10-14 years  20           0    0    1    0    a
1     30-34 years  10           0    0    1    0    a
1     40-44 years  15           2    0    0    1    a
2     05-09 years  15           1    1    0    2    b
2     25-29 years  10           0    0    0    0    b
3     10-14 years  15           0    1    2    0    a
3     15-19 years  10           1    0    0    1    a
3     20-24 years  15           0    0    1    3    a
3     30-34 years  20           0    0    1    0    a
3     35-39 years  10           0    1    0    0    a

我正在嘗試生成一個新的 dataframe，它結合了年齡，以便我的新年齡范圍為 05-29 歲、30-39 歲和 40-49 歲，所以它看起來像這樣：

ID    Age.Range    Population   X1   X2   X3   X4   Category
1     05-29 years  30           1    0    1    1    a
1     30-39 years  10           0    0    1    0    a
1     40-49 years  15           2    0    0    1    a
2     05-29 years  25           1    1    0    2    a
3     05-29 years  40           1    1    3    4    a
3     30-39 years  30           0    1    1    0    a

我試過用 dplyr 這樣做但沒有成功。 任何幫助，將不勝感激！

Answer 1

這應該有效：

your_data %>%
  mutate(
    First.Age.In.Range = as.numeric(str_extract(Age.Range, "^[0-9]+"))
    New.Age.Range = case_when(
      First.Age.In.Range < 30 ~ "05-29 years",
      First.Age.In.Range < 40 ~ "30-39 years",
      First.Age.In.Range < 50 ~ "40-49 years",
      First.Age.In.Range < 60 ~ "50-59 years",    
      ## not sure how high you need to go 
      ## catch-all for the last category
      TRUE ~ "90-99 years"
    )
  ) %>%
  group_by(ID, New.Age.Range, Population, Category) %>%
  summarize(across(starts_with("X"), sum))

Answer 2

這是一個使用tidyr 、 stringr和dplyr包的解決方案。 它類似於 Gregor Thomas 提供的內容。 在我們等待添加編輯時，它還讓其他人有機會與可重現的示例進行交互。

df <- structure(list(ID = c(1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3), Age.Range = c("05-09 years", 
"10-14 years", "30-34 years", "40-44 years", "05-09 years", "25-29 years", 
"10-14 years", "15-19 years", "20-24 years", "30-34 years", "35-39 years"
), Population = c(10L, 20L, 10L, 15L, 15L, 10L, 15L, 10L, 15L, 
20L, 10L), X1 = c(1L, 0L, 0L, 2L, 1L, 0L, 0L, 1L, 0L, 0L, 0L), 
    X2 = c(0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L), X3 = c(0L, 
    1L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 1L, 0L), X4 = c(1L, 0L, 0L, 
    1L, 2L, 0L, 0L, 1L, 3L, 0L, 0L), Category = c("a", "a", "a", 
    "a", "b", "b", "a", "a", "a", "a", "a")), class = "data.frame", row.names = c(NA, 
-11L))


library(stringr)
library(dplyr)
library(tidyr)

df %>% 
  group_by(ID) %>% 
  separate(col = Age.Range, into = c("Age_1", "Age_2"), sep = "-") %>%
  # You will have to add ifelse statements if you have ages that are >49 in your dataset. 
  mutate(
    Age_2 = str_remove(Age_2, " years"),
    Age_1 = ifelse(Age_2 <= 29, "05-29 years", Age_1),
    Age_1 = ifelse(Age_2 > 29 & Age_2 <= 39, "30-39 years", Age_1),
    Age_1 = ifelse(Age_2 > 39 & Age_2 <= 49, "40-49 years", Age_1)
  ) %>%
  rename(Age.Range = Age_1) %>% 
  group_by(ID, Category, Age.Range) %>% 
  summarise(across(
    .cols = Population:X4, sum
  )) %>% 
  select(ID, Age.Range, Population, X1, X2, X3, X4, Category)


#> # A tibble: 6 x 8
#> # Groups:   ID, Category [3]
#>      ID Age.Range   Population    X1    X2    X3    X4 Category
#>   <dbl> <chr>            <int> <int> <int> <int> <int> <chr>   
#> 1     1 05-29 years         30     1     0     1     1 a       
#> 2     1 30-39 years         10     0     0     1     0 a       
#> 3     1 40-49 years         15     2     0     0     1 a       
#> 4     2 05-29 years         25     1     1     0     2 b       
#> 5     3 05-29 years         40     1     1     3     4 a       
#> 6     3 30-39 years         30     0     1     1     0 a

^{由reprex package (v0.3.0) 創建於 2020-11-15}