如何重新编码序数变量?

How to recode ordinal variable?

我正在使用来自世界价值观调查的调查数据,我使用下面的代码将我的变量从数字变量更改为有序变量

renameddata$Education= ordered(renameddata$Education, levels =c(-2,-1,840001,840002,840003,
                                         840004,840005,840006,840007,
                                         840008,840009),
        labels = c("NA","NA","LessHighSchool","SomeHighSchool",
                   "GED","SomeCollege","Associates","Bachelors",
                   "Masters","Professional","Doctorate"))

但是,现在我想重新编码教育变量,以便 LessHighSchoolSomeHighSchool 合二为一,例如 "NO GED",这样 SomeCollegeAssociatesBachelors 变成 "Undergraduate" 等等

这个怎么样:

library(dplyr)
renameddat <- renameddat %>% mutate(Education = 
        case_when(
          Education %in% c(840001,840002) ~ "No GED", 
          Education == 840003 ~ "GED", 
          Education %in% c(840004,840005,840006) ~ "Undergraduate", 
          Education %in% c(840007,840008,840009) ~ "Graduate", 
      TRUE ~ NA_character_), 
Education=factor(Education, 
                 levels=c("No GED", "GED", "Undergraduate", "Graduate")))

或者,如果您想重新编码创建的因子变量,您可以使用 forcats 包中的 fct_collapse

输入:

renameddata <- data.frame(Education = c(-2, -1, 840001, 840002, 840003, 840004, 840005, 840006, 840007, 840008, 840009))

renameddata$Education = ordered(renameddata$Education,
                                levels = c(-2, -1, 840001, 840002, 840003, 840004, 840005, 840006, 840007, 840008, 840009),
                                labels = c("NA", "NA", "LessHighSchool", "SomeHighSchool", "GED", "SomeCollege", "Associates", "Bachelors", "Masters", "Professional", "Doctorate"))

重新编码:

library(forcats)
renameddata$Education <- fct_collapse(renameddata$Education,
                                      "NO GED" = c("LessHighSchool", "SomeHighSchool"),
                                      "Undergraduate" = c("SomeCollege", "Associates", "Bachelors"))

给出:

       Education
1             NA
2             NA
3         NO GED
4         NO GED
5            GED
6  Undergraduate
7  Undergraduate
8  Undergraduate
9        Masters
10  Professional
11     Doctorate