使用循环或类似函数包装长代码

Question

我想知道是否有任何方法可以包装下面的代码以使其更简短；我正在考虑使用循环或类似的函数来完成此操作。这段代码使用AgeatDeath和Disability生成一个新变量(cat)。如果AgeatDeath介于75.6和77.1之间，并且Disability等于"No Intelectual and Developmental“，则代码将创建值为75.6-77.1的cat变量。谢谢，纳德

IDD <- IDD %>%
      mutate(
        cat = case_when(
          AgeatDeath >= 75.6 &
            AgeatDeath < 77.1  &
            Disability == 'No Intelectual and Developmental Disabilities' ~ '75.6-77.1',
          AgeatDeath >= 74.3 &
            AgeatDeath < 75.6  &
            Disability == 'No Intelectual and Developmental Disabilities' ~ '74.3-75.6',
          AgeatDeath >= 72.5 &
            AgeatDeath < 74.3  &
            Disability == 'No Intelectual and Developmental Disabilities' ~ '72.5-74.3',
          AgeatDeath >= 66.5 &
            AgeatDeath < 72.5  &
            Disability == 'No Intelectual and Developmental Disabilities' ~ '66.6-72.5',
          
          AgeatDeath >= 64.1 &
            AgeatDeath < 71.9  &
            Disability == 'Intellectual disability' ~ '64.1-71.9',
          AgeatDeath >= 62.3 &
            AgeatDeath < 64.1  &
            Disability == 'Intellectual disability' ~ '62.3-64.1',
          AgeatDeath >= 59.4 &
            AgeatDeath < 62.3  &
            Disability == 'Intellectual disability' ~ '59.4-62.3',
          AgeatDeath >= 50.4 &
            AgeatDeath < 59.4  &
            Disability == 'Intellectual disability' ~ '50.4-59.4',
          
          AgeatDeath >= 56.47 &
            AgeatDeath < 59.1  &
            Disability == 'Down syndrome' ~ '56.47-59',
          AgeatDeath >= 55.59 &
            AgeatDeath < 56.47  &
            Disability == 'Down syndrome' ~ '55.59-56.47',
          AgeatDeath >= 54.42 &
            AgeatDeath < 55.59  &
            Disability == 'Down syndrome' ~ '54.42-55.59',
          AgeatDeath >= 50.92 &
            AgeatDeath < 54.42  &
            Disability == 'Down syndrome' ~ '50.92-54.42',
          
          AgeatDeath >= 53.3 &
            AgeatDeath < 58.2  &
            Disability == 'Cerebral palsy' ~ '53.3-58.2',
          AgeatDeath >= 50.6 &
            AgeatDeath < 53.3  &
            Disability == 'Cerebral palsy' ~ '50.6-53.3',
          AgeatDeath >= 48.9 &
            AgeatDeath < 50.6  &
            Disability == 'Cerebral palsy' ~ '48.9-50.6',
          AgeatDeath >= 41.38 &
            AgeatDeath < 48.9  &
            Disability == 'Cerebral palsy' ~ '41.4-48.9',
          
          AgeatDeath >= 44.2 &
            AgeatDeath < 51.1  &
            Disability == 'Other rare developmental disabilities' ~ '44.2-51',
          AgeatDeath >= 41.6 &
            AgeatDeath < 44.2  &
            Disability == 'Other rare developmental disabilities' ~ '41.6-44.2',
          AgeatDeath >= 30.6 &
            AgeatDeath < 38.4  &
            Disability == 'Other rare developmental disabilities' ~ '30.6-38.4',
          AgeatDeath >= 38.4 &
            AgeatDeath < 41.6  &
            Disability == 'Other rare developmental disabilities' ~ '38.4-41.6'
        )
      )

Answer 1

一些子集和函数cut()可以发挥很大的作用。我将演示的内容不涉及dplyr。

首先创建一个emtpy新变量。我们将使用代码的其余部分在几行代码中进行填充。

IDD$cat <- NA_character

接下来，使用Disability的值和相应的切割点创建一个列表。我们将遍历这个列表。

L <- list(
`No Intelectual and Developmental Disabilities` = c(66.6, 72.5, 74.3, 75.6, 77.1),
`Intellectual disability` = c(50.4, 59.4, 62.3, 64.1, 71.9)
)

你可以填完剩下的部分。现在，我们将使用一个循环来根据Disability的每个值进行子集，使用cut()将这些值划分为类别并重命名这些类别。

for (d in names(L)) {
   IDD$cat[IDD$Disability == d] <- as.character(
                                      cut(IDD$Ageatdeath, 
                                        breaks = L[[d]], 
                                        labels = paste(L[[d]][-4], L[[d]][-1], sep = "-"),
                                        include.lowest = TRUE,
                                        right = FALSE))
}

cut()根据我们提供给L的断点拆分Ageatdeath。我们根据断点给它加标签。right = FALSE使得每个类别都包括下界并排除上界，并且include.lowest = TRUE确保如果任何值在上界，它们都会被包括在最高类别中。我们使用as.character()来确保它是一个字符向量，而不是一个因子。

Answer 2

无论您采用哪种方法，您仍然需要将阈值和条件存储在某个地方。现在，这些代码已经写入到您的代码中，但是可以将它们移动到表中。

考虑设置一张表

order | min_age | max_age | disability
------+--------+---------+------------
1     |75.6    | 77.1    | 'No Intelectual and Developmental Disabilities'
2     |74.3    | 75.6    | 'No Intelectual and Developmental Disabilities'
etc.
...

然后，您可以使用该表来设置条件。遵循this问题中的parse_exprs方法：

# loading of condition table
# other setup
# etc.

# ensure conditions are in the preferred order
twc = table_w_conditions %>%
  arrange(order)

# make text strings of conditions
conditions = paste("AgeatDeath >=", twc$min_age,
                  "& AgeatDeath <", twc$max_age,
                  "& Disability ==", twc&disability,
                  " ~ '", twc$min_age, "-", twc$max_age, "'")

# mutate treating text strings as code
IDD <- IDD %>%
  mutate(
        cat = case_when(!!!parse_exprs(conditions))
  )

如果您采用这种方法，我建议您在使用conditions之前检查它是否包含正确条件文本的文本字符串列表。