将具有多个种族类别反应的细胞重新编码为“多种族”反应？

Question

我正在清理一些调查数据，这些数据似乎允许受访者选择多个种族类别。我想知道如何将这些重新编码为“多种族”响应以进行分析。

现在我一直在做相当费力的手工编码，但还没有成功。这是我尝试使用 recode 将具有多个条目的每个响应转换为一个数字，然后可以使用 case_when 对其进行重新编码。

rawdat$race <- recode(rawdat$race, "White, non-Hispanic,Asian" = 1, 
  "White, non-Hispanic,American Indian or Alaska Native" = 2,
  "White, non-Hispanic,Black or African American,Asian" = 3, 
  "Black or African American,American Indian or Alaska Native" = 4, 
  "White, non-Hispanic,Hispanic" = 5,
  "Asian,Native Hawaiian or Pacific Islander" = 6,
  "White, non-Hispanic,Black or African American" = 7,
  "Black or African American,American Indian or Alaska Native,Asian,Hispanic" = 8,
  "White, non-Hispanic,Black or African American,American Indian or Alaska Native,Asian,Native Hawaiian or Pacific Islander,Hispanic" = 9,
  "Black or African American,Hispanic" = 10,
  "Black or African American,Asian" = 11, 
  "White, non-Hispanic,Native Hawaiian or Pacific Islander" =12,
  "White, non-Hispanic,Black or African American,American Indian or Alaska Native,Asian,Hispanic",
  "American Indian or Alaska Native,Hispanic" = 13)

这种方法有很多问题（我只是尝试过，因为我认为它可以作为强力的短期修复 - 它没有），我更喜欢初始化一个向量包含针对此问题呈现给受访者的每个可能值，然后将包含多个这些值的任何单元格重新编码为值“多种族”，但据我所知，recode() 函数不会接受这样的向量作为论据。关于如何完成后一种方法的任何想法？

Answer 1

看起来您要重新编码为“多种族”的单元格包含逗号 - 对吗？如果是这样，您只需要用逗号标识单元格。

library(tidyverse)
race <- c("White", "White, Asian", "Black or African American", "White", "White, Black or African American")
df <- as.data.frame(race)
df$multiracial <- ifelse(grepl(",", df$race), "Multiracial", "Not multiracial")
df$race <- ifelse(df$multiracial == "Multiracial", "Multiracial", df$race)
head(df$race)
#> [1] "White"                     "Multiracial"              
#> [3] "Black or African American" "White"                    
#> [5] "Multiracial"

编辑

创建了一个单独的西班牙裔/非西班牙裔专栏。这可能对您的原始数据不那么干净，这取决于每个选项之间是否有一致的空格/逗号。

library(tidyverse)
library(stringr)
race <- c("White, non-Hispanic", 
          "White, Asian", 
          "Black or African American", 
          "White, Hispanic, Asian", 
          "White, Black or African American",
          "White, non-Hispanic",
          "Black or African American, Hispanic")
df <- as.data.frame(race)

# original
df$original <- df$race

# create separate Hispanic/non-Hispanic column
df$hispanic <- ifelse(grepl("non-Hispanic",df$race),"non-Hispanic", 
                      ifelse(grepl("Hispanic",df$race),"Hispanic", "Unknown"))

# remove Hispanic/non-Hispanic
df$race <- str_remove(df$race, ", non-Hispanic")
df$race <- str_remove(df$race, ", Hispanic")

# recode as multiracial
df$multiracial <- ifelse(grepl(",", df$race), "Multiracial", "Not multiracial")
df$race <- ifelse(df$multiracial == "Multiracial", "Multiracial", df$race)
head(df)
#>                        race                         original     hispanic
#> 1                     White              White, non-Hispanic non-Hispanic
#> 2               Multiracial                     White, Asian      Unknown
#> 3 Black or African American        Black or African American      Unknown
#> 4               Multiracial           White, Hispanic, Asian     Hispanic
#> 5               Multiracial White, Black or African American      Unknown
#> 6                     White              White, non-Hispanic non-Hispanic
#>       multiracial
#> 1 Not multiracial
#> 2     Multiracial
#> 3 Not multiracial
#> 4     Multiracial
#> 5     Multiracial
#> 6 Not multiracial