字符串的 R 切函数

Question

我想知道R中是否有任何类似于cut函数但适用于字符串的函数？

我正在处理的代码是将有关美国各州的数据分配给一个名为 Region 的分类变量：有 4 个区域，Northeast、Midwest、South、West。存储每个数据的数据框将州名称存储在一个名为“state”的变量中，并以它的缩写形式：例如，“NY”代表纽约，“MS”代表密西西比州。需要将区域变量添加到数据框中，我目前正在执行以下操作（这是为了家庭作业，所以我想表明我已经有了一个解决方案，并且正在寻找一个可能更好的解决方案）：

create.region <- function(state) {
northeast <- c("CT", "ME", "MA", "NH", "RI", "VT", "NJ", "NY", "PA")
midwest <- c("IL", "IN", "MI", "OH", "WI", "IA", "KS", "MN", "MO", "NE", "ND", "SD")
south <- c("DE", "DC", "FL", "GA", "MD", "NC", "SC", "VA", "WV", "AL", "KY", "TN", "MS", "AR", "LA", "OK", "TX")
west <- c("AZ", "CO", "ID", "MT", "NV", "NM", "UT", "WY", "AK", "CA", "HI", "OR", "WA")
region <- ifelse(state %in% northeast, "Northeast", 
          ifelse(state %in% midwest, "Midwest",
          ifelse(state %in% south, "South",
          ifelse(state %in% west, "West", NA ))))
return(region)
}
birth_data <- within(birth_data, region <- create.region(state))

我对 R 的了解还不是很多，而且我很关心我的代码的效率。过去我发现 cut 函数是一种以这种方式对数字数据进行分类的更简洁有效的方法，但它显然不适用于字符向量。是否有任何类似于 cut 的函数允许字符分配规则，而不仅仅是数字规则？

Answer 1

最简单的方法是通过向量映射名称。

首先，我们准备地图：

all_states = c('northeast', 'midwest', 'south', 'west')

states_for_region = function (region) {
    states = get(region)
    setNames(rep(region, length(states)), states)
}

states_map = unlist(lapply(all_states, states_for_region))

我们也可以为每个区域手动构建states_map，然后连接结果。但上面的重复性较少。

然后，我们进行实际的映射，现在只需要一行。

region = states_map[state]

为了提高效率，最好在函数的外部准备地图。否则，无论何时调用该函数，它都会重新生成。

Answer 2

开箱即用的 R 包含变量 state.abb 和 state.region。前者是所有状态缩写的字符向量，后者是包含相应区域的相同长度的4级因子；因此，要获得 MS 的区域，请说：

state.region[state.abb == "MS"]
## [1] South
## Levels: Northeast South North Central West

如果您想要一个不同的分类，定义您自己的 state.region 替代品然后使用上面的代码会很容易。

顺便说一句，请注意state.name 也存在，它与上述两个变量的长度相同，并给出了完整的状态名称。

Answer 3

您还可以将levels<- 函数与映射列表一起使用。

这是一个例子：

## Create your mapping....
## Overkill in this example as @Grothendieck has pointed out,
##   but still applicable in a general scenario

myLevs <- list(
  Northeast = c("CT", "ME", "MA", "NH", "RI", "VT", "NJ", "NY", "PA"), 
  Midwest = c("IL", "IN", "MI", "OH", "WI", "IA", "KS", "MN", "MO", "NE", "ND", "SD"), 
  South = c("DE", "DC", "FL", "GA", "MD", "NC", "SC", "VA", "WV", "AL", "KY", "TN", "MS", "AR", "LA", "OK", "TX"), 
  West = c("AZ", "CO", "ID", "MT", "NV", "NM", "UT", "WY", "AK", "CA", "HI", "OR", "WA"))

现在，创建一个样本向量：

set.seed(1)
x <- sample(state.abb, 10)

factor 向量，并更改其levels。这可以分两步完成（y <- factor(x); levels(y) <- myLevs) 或在一个看起来很神秘的步骤中完成：

y <- `levels<-`(factor(x), myLevs)

这是输出：

x
#  [1] "IN" "ME" "NV" "TX" "GA" "SD" "TN" "NH" "NE" "AZ"
y
#  [1] Midwest   Northeast West      South     South     Midwest   South    
#  [8] Northeast Midwest   West     
# Levels: Northeast Midwest South West