Question

我正在尝试创建一个如下所示的函数：

region <- function(State){
region_vector <- 
    ifelse(State %in% c("CT", "DE", "DC", "MD", "ME", "MD", "MA", "NH","NJ", "NY", "PA", "RI", "VT"), "Northeast",
    ifelse(State %in% c("IN", "KY", "MI", "OH", "PA", "WV") "East Central", 
    ifelse(State %in% c("IN", "KY", "MI", "OH", "PA", "WV") "East Central", 
    ifelse(State %in% c("CO", "IL", "IA", "KS", "MN", "MO", "MT", "NE", "ND", "SD", "WI", "WY") "West Central",  
    ifelse(State %in% c("AL", "FL", "GA", "MS", "NC", "SC", "TN", "VA") "Southeast",
    ifelse(State %in% c("AR", "LA", "NM", "OK", "TC") "Southwest", 
    ifelse(State %in% c("AK", "AZ", "CA", "HI", "ID", "NV", "OR", "UT", "WA") "Pacific",
           "NA" )))))))
return(region_vector)  }

我想将区域分配给我的数据中的列 c ("CA", "NY", "CO"...)我上面编写的代码有什么问题？错误消息为unexpected string constant in c (...)

Answer 1

只是为了告诉你其他方法，以及如何使用slooow ifelse，例如：

# Lookup list
l <- list(
  "Northeast" = c("CT", "DE", "DC", "MD", "ME", "MA", "NH","NJ", "NY", "RI", "VT"),
  "East central" = c("IN", "KY", "MI", "OH", "PA", "WV"),
  "West central" = c("CO", "IL", "IA", "KS", "MN", "MO", "MT", "NE", "ND", "SD", "WI", "WY"),
  "Southeast" = c("AL", "FL", "GA", "MS", "NC", "SC", "TN", "VA"),
  "Southwest" = c("AR", "LA", "NM", "OK", "TC"),
  "Pacific" = c("AK", "AZ", "CA", "HI", "ID", "NV", "OR", "UT", "WA")
)
# long list
L <- unlist(l)
names(L) <- rep(names(l), times = lapply(l, length))
# data.frame
df <- data.frame(
  CODE = unlist(l),
  LABEL = rep(names(l), times = lapply(l, length)),
  stringsAsFactors = FALSE, row.names = NULL
)

# Test data
set.seed(123)
test <- data.frame(CODE = sample(x = unlist(l), size = 1e4, replace = TRUE), stringsAsFactors = FALSE)


# Fun to recode with match
match_recode <- function(var, dico) {
  names(dico)[match(x = var, table = dico)]
}

# With ifelse
region <- function(State){
  region_vector <- 
    ifelse(State %in% c("CT", "DE", "DC", "MD", "ME", "MA", "NH","NJ", "NY", "RI", "VT"), "Northeast",
           ifelse(State %in% c("IN", "KY", "MI", "OH", "PA", "WV"), "East Central", 
                  ifelse(State %in% c("IN", "KY", "MI", "OH", "PA", "WV"), "East Central", 
                         ifelse(State %in% c("CO", "IL", "IA", "KS", "MN", "MO", "MT", "NE", "ND", "SD", "WI", "WY"), "West Central",  
                                ifelse(State %in% c("AL", "FL", "GA", "MS", "NC", "SC", "TN", "VA"), "Southeast",
                                       ifelse(State %in% c("AR", "LA", "NM", "OK", "TC"), "Southwest", 
                                              ifelse(State %in% c("AK", "AZ", "CA", "HI", "ID", "NV", "OR", "UT", "WA"), "Pacific",
                                                     "NA" )))))))
  return(region_vector)  
}

# With data.table
dt_recode <- function(var, dico) {
  dt <- data.table(CODE = var)
  setkey(dt, CODE)
  dt <- dt[dico]
  return(dt$LABEL)
}

测试结果

table(match_recode(test$CODE, dico = L))
# East central    Northeast      Pacific    Southeast    Southwest West central 
#         1211         2132         1711         1554          998         2394 
table(region(test$CODE))
# East central    Northeast      Pacific    Southeast    Southwest West central 
#         1211         2132         1711         1554          998         2394 
library("data.table")
table(dt_recode(test$CODE, dico = df))
# East central    Northeast      Pacific    Southeast    Southwest West central 
#         1211         2132         1711         1554          998         2394 

# All the same

基准：

library("microbenchmark")
microbenchmark(match_recode(test$CODE, dico = L), 
               region(test$CODE),
               dt_recode(test$CODE, dico = df), 
               times = 100L)
# Unit: microseconds
#                              expr       min        lq       mean     median         uq       max neval
# match_recode(test$CODE, dico = L)   266.845   271.549   344.7044   288.2265   298.7035  1138.792   100
# region(test$CODE)                 23454.496 24250.325 26391.6468 24637.9750 25257.4050 49958.884   100
# dt_recode(test$CODE, dico = df)    1133.233  1184.977  1355.1031  1364.3705  1445.8345  2116.794   100

match比ifelse快得多！

使用data.table

可能有更好的方法

PS：在您的ifelse中，MD在东北部出现两次，PA在东北部和东部中部

多个Elseif声明

1 个答案: