我想将字符串值分配给R中的数字[关闭]

Question

zom$country.code 是 int。
zom$Country.Code

r <-c(India,Australia,Brazil,Canada,Indonesia,NewZealand,Phillipines,Qatar,Singapore,southAfrica,SriLanka,Turkey,UAE,UnitedKingdom,UnitedStates)

我想要这样的输出：

zom$Country.Code <- c(India,Australia,Brazil,Canada,Indonesia,NewZealand,Phillipines,Qatar,Singapore,southAfrica,SriLanka,Turkey,UAE,UnitedKingdom,UnitedStates)

如何在 R 中解决这个问题。

Answer 1

factor() 函数可用于将数字向量与一组标签相关联。例如：

x <- c(1,1,1,2,3,3,2,3,4,4)

theLabels <- c("India","Canada","United States","Mexico")

y <- factor(x,1:4,theLabels)
y

产生以下输出：

> y <- factor(x,1:4,theLabels)
> y
 [1] India         India         India         Canada        United States
 [6] United States Canada        United States Mexico        Mexico       

级别：印度 加拿大 美国 墨西哥

为了证明此答案适用于 OP 第五次编辑中提供的数据：

r <-c("India","Australia","Brazil","Canada","Indonesia","NewZealand",
      "Phillipines","Qatar","Singapore","southAfrica","SriLanka","Turkey","UAE","UnitedKingdom","UnitedStates")
zom<- data.frame(Country.Code=c(1,14,30,37,94,148,162,166,184,189,191,208,214,215,216))

zom$Country.Code <- factor(zom$Country.Code,
                           levels = c(1,14,30,37,94,148,162,166,184,189,191,208,214,215,216),
                           labels = r)

zom$Country.Code

...和输出：

> zom$Country.Code
 [1] India         Australia     Brazil        Canada        Indonesia     NewZealand    Phillipines   Qatar        
 [9] Singapore     southAfrica   SriLanka      Turkey        UAE           UnitedKingdom UnitedStates 
15 Levels: India Australia Brazil Canada Indonesia NewZealand Phillipines Qatar Singapore southAfrica SriLanka Turkey ... UnitedStates

注意：一旦将原始代码转换为因子，基础代码就会丢失，因为因子的副作用是因子级别变为从 1 到唯一标签数量的有序列表与因素有关。

factor() 方法的替代方法是创建国家名称和代码的查找表，并将其与原始数据合并。这种方法保留了Country.Code 的原始值。

为了说明，我们将创建一个包含来自 OP 的多行 Country.Code 的数据框，并通过 dplyr::inner_join() 将其与查找表合并。然后我们将生成Country.Name 和Country.Code 的交叉表来说明连接过程的准确性。

library(dplyr)
# first, build a data frame containg multiple rows with same country code
zom<- data.frame(Country.Code=c(1,14,30,37,94,148,162,166,184,189,191,208,214,215,216,
                                1,14,30,37,94,148,162,166,184,189,191,208,214,215,216,
                                1,14,30,37,94,148,162,166,184,189,191,208,214,215,216))
# second, create lookup table of codes and names, one row per country
countryNames <- data.frame(Country.Code=c(1,14,30,37,94,148,162,166,184,189,191,208,214,215,216),
                           Country.Name= c("India","Australia","Brazil","Canada","Indonesia","NewZealand",
                                           "Phillipines","Qatar","Singapore","southAfrica","SriLanka","Turkey","UAE","UnitedKingdom","UnitedStates"),
     stringsAsFactors=FALSE)

# use dplyr::inner_join() to join country names 
mergedData <- zom %>% inner_join(countryNames)
table(mergedData$Country.Name,mergedData$Country.Code)

...和输出：

> table(mergedData$Country.Name,mergedData$Country.Code)

                1 14 30 37 94 148 162 166 184 189 191 208 214 215 216
  Australia     0  3  0  0  0   0   0   0   0   0   0   0   0   0   0
  Brazil        0  0  3  0  0   0   0   0   0   0   0   0   0   0   0
  Canada        0  0  0  3  0   0   0   0   0   0   0   0   0   0   0
  India         3  0  0  0  0   0   0   0   0   0   0   0   0   0   0
  Indonesia     0  0  0  0  3   0   0   0   0   0   0   0   0   0   0
  NewZealand    0  0  0  0  0   3   0   0   0   0   0   0   0   0   0
  Phillipines   0  0  0  0  0   0   3   0   0   0   0   0   0   0   0
  Qatar         0  0  0  0  0   0   0   3   0   0   0   0   0   0   0
  Singapore     0  0  0  0  0   0   0   0   3   0   0   0   0   0   0
  southAfrica   0  0  0  0  0   0   0   0   0   3   0   0   0   0   0
  SriLanka      0  0  0  0  0   0   0   0   0   0   3   0   0   0   0
  Turkey        0  0  0  0  0   0   0   0   0   0   0   3   0   0   0
  UAE           0  0  0  0  0   0   0   0   0   0   0   0   3   0   0
  UnitedKingdom 0  0  0  0  0   0   0   0   0   0   0   0   0   3   0
  UnitedStates  0  0  0  0  0   0   0   0   0   0   0   0   0   0   3
>