r - 一个家庭长辈最高受教育年限如何计算

Question

假设我有这样的数据框:

   family relationship meanings              edu
 1      1 A            respondent             12
 2      1 B            respondent's spouse    18
 3      1 C            A's father             10
 4      1 D            A's mother              9
 5      1 E1           A's first son          15
 6      1 F1           E1's spouse            14
 7      1 G11          E1's first son          3
 8      1 G12          E1's second son         1
 9      1 E2           A's second son         13
10      2 A            respondent             21
11      2 B            respondent's spouse    16
12      2 C            A's father             12
13      2 D            A's mother             16
14      2 E1           A's first son          18
15      2 F1           E1's spouse            15
16      2 E2           A's second son         17
17      2 E3           A's third son          16

family表示家庭号码。 relationship表示一个家庭的关系。 meanings表示第二列的含义， relationship .



我想计算一个家庭中父代的最大受教育年限。
我们不需要配偶的信息。

预期结果如下:

   family id      edu fedu 
 1      1 A        12 10   
 2      1 C        10 NA   
 3      1 E1       15 18   
 4      1 E2       13 18   
 5      1 G11       3 15   
 6      1 G12       1 15   
 7      2 A        21 16   
 8      2 C        12 NA   
 9      2 E1       18 21   
10      2 E2       17 21   
11      2 E3       16 21

这是数据:

 d = structure(list(family = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2), relationship = c("A", "B", "C", "D", "E1", "F1", "G11", "G12", "E2", "A", "B", "C", "D", "E1", "F1", "E2", "E3"), meanings = c("respondent", "respondent's spouse", "A's father","A's mother", "A's first son", "E1's spouse", "E1's first son","E1's second son", "A's second son", "respondent", "respondent's spouse","A's father", "A's mother", "A's first son", "E1's spouse", "A's second son","A's third son"), edu = c(12, 18, 10, 9, 15, 14, 3, 1, 13, 21,16, 12, 16, 18, 15, 17, 16)), row.names = c(NA, -17L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

这是我尝试过的。我认为有必要创建一个生成变量。查看您问题中的示例图片，C和 D是第一代。 A和 B是第2代。 E和 F是第三代，和G是第4代。第一个mutate()与 case_when()创建了生成变量。然后，我通过 family 定义了组和 generation .对于每组，我确定了最长教育时间(即 max_ed_duration)。因为你说你不需要配偶的信息，所以我在 meanings 中删除了包含母亲或配偶的行。 .然后，我再次使用 family 定义组.对于每个家庭，如果 generation为 1，将 NA 分配给 fedu .否则，分配 max_ed_duration值从上一代到fedu .最后我按family整理了数据和 relationship .

library(dplyr)

mutate(mydf, generation = case_when(relationship %in% c("C", "D") ~ 1,
                                    relationship %in% c("A", "B") ~ 2,
                                    grepl(x = relationship, pattern = "^E|F") ~ 3,
                                    grepl(x = relationship, pattern = "^G") ~ 4)) %>% 
  group_by(family, generation) %>% 
  mutate(max_ed_duration = max(edu)) %>% 
  filter(!grepl(x = meanings, pattern = "mother|spouse")) %>% 
  group_by(family) %>%
  mutate(fedu = if_else(generation == 1,
                        NA_real_,
                        max_ed_duration[match(x = generation - 1, table = generation)])) %>% 
  arrange(family, relationship)

#   family relationship meanings          edu generation max_ed_duration  fedu
#    <dbl> <chr>        <chr>           <dbl>      <dbl>           <dbl> <dbl>
# 1      1 A            respondent         12          2              18    10
# 2      1 C            A's father         10          1              10    NA
# 3      1 E1           A's first son      15          3              15    18
# 4      1 E2           A's second son     13          3              15    18
# 5      1 G11          E1's first son      3          4               3    15
# 6      1 G12          E1's second son     1          4               3    15
# 7      2 A            respondent         21          2              21    16
# 8      2 C            A's father         12          1              16    NA
# 9      2 E1           A's first son      18          3              18    21
#10      2 E2           A's second son     17          3              18    21
#11      2 E3           A's third son      16          3              18    21

数据

mydf <- structure(list(family = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 2, 2, 2), relationship = c("A", "B", "C", "D", "E1", "F1", 
"G11", "G12", "E2", "A", "B", "C", "D", "E1", "F1", "E2", "E3"
), meanings = c("respondent", "respondent's spouse", "A's father", 
"A's mother", "A's first son", "E1's spouse", "E1's first son", 
"E1's second son", "A's second son", "respondent", "respondent's spouse", 
"A's father", "A's mother", "A's first son", "E1's spouse", "A's second son", 
"A's third son"), edu = c(12, 18, 10, 9, 15, 14, 3, 1, 13, 21, 
16, 12, 16, 18, 15, 17, 16)), class = "data.frame", row.names = c(NA, 
-17L))