将数据框列表的列转换为因子

Question

嗨，我正在手动给我的数据框添加标签，如下所示，我有 800 列要标记，之后我创建了一个数据框的子集（数据的子设置有很多），然后将该数据框应用到 function 进行计算。

所有块的标签都可以不同，这也是为所有块一个一个地创建标签所花费的时间。

data<-data.frame( col1=c(1,1,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,1,1,1,NA,1,1,NA,NA,NA,NA,1,NA,NA,NA,NA,1,NA,1),
                  col2=c(1,1,1,1,1,NA,NA,NA,NA,1,1,1,1,1,NA,NA,NA,1,1,1,NA,1,1,1,1,1,NA,NA,NA,1,1,1,1,1,1,1,NA,NA,NA),
                  col3=c(1,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,NA,NA,NA,1,NA,NA,1,1,1,1,1,NA,NA,1),
                  col4=c(1,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
                  col5=c(1,2,1,1,1,2,1,2,2,1,2,NA,1,1,2,2,2,1,1,1,2,NA,2,1,1,1,2,2,2,NA,1,2,2,1,1,1,2,2,2)
)  

data$col5<-factor(data$col5, levels=c(1,2), labels=c("Local","Overseas"))

df<- data
df$cc1<-1
df2<- subset(df, col5 == 'Local')
df$cc2<-ifelse(df$col5 == 'Local',1,NA)
lst<-list(df$cc1, df$cc2)
ldat<-list("ALL" = df, "Local" =df2)

col_names <- c("col1","col2"...."col4")
    labels <- c("Sales","Ops"...."HR")

make_mutator <- function(x) {
  paste0(
    "factor(", names(faclist)[[x]],
    ",labels=c('",
    paste0(faclist[[x]],
           collapse = "','"
    ), "'))"
  )
}


list_of_fac <- purrr::map_chr(seq_len(length(faclist)),
                              make_mutator)

names(list_of_fac) <- names(faclist)

ldat <- purrr::map(ldat,
                   ~mutate(.,
                           !!!parse_exprs(list_of_fac)))

这非常好并且对我有用....但是如果我将分别为列和标签提供列和标签，则只需要新的解决方案

col_names <- c("col1","col2"...."col4") 标签 <- c("Sales","Ops"....."HR")

那么我该如何更改我的 function 呢....？

Answer 1

除了解析之外，更简单的选择是在使用map遍历list后使用map2 。 使用map2 ，我们根据命名list 'faclist' 传递感兴趣的列和要更改的标签

library(dplyr)
library(purrr)
ldat1 <- map(ldat, ~  {
     .x[names(faclist)] <- map2(.x %>% 
                             dplyr::select(names(faclist)), 
                         faclist, ~ factor(.x, labels= .y))
       .x} )

-输出

str(ldat1[[1]])
#'data.frame':  39 obs. of  7 variables:
# $ col1: Factor w/ 1 level "Sales": 1 1 NA NA NA NA NA NA 1 NA ...
# $ col2: Factor w/ 1 level "OPS": 1 1 1 1 1 NA NA NA NA 1 ...
# $ col3: Factor w/ 1 level "Management": 1 1 NA NA NA NA NA 1 NA NA ...
# $ col4: Factor w/ 1 level "HR": 1 NA NA NA NA NA NA NA NA NA ...
# $ col5: Factor w/ 2 levels "Local","Overseas": 1 2 1 1 1 2 1 2 2 1 ...
# $ cc1 : num  1 1 1 1 1 1 1 1 1 1 ...
# $ cc2 : num  1 NA 1 1 1 NA 1 NA NA 1 ...
str(ldat1[[2]])
#'data.frame':  18 obs. of  6 variables:
# $ col1: Factor w/ 1 level "Sales": 1 NA NA NA NA NA NA NA 1 NA ...
#$ col2: Factor w/ 1 level "OPS": 1 1 1 1 NA 1 1 1 1 1 ...
# $ col3: Factor w/ 1 level "Management": 1 NA NA NA NA NA NA NA NA NA ...
# $ col4: Factor w/ 1 level "HR": 1 NA NA NA NA NA NA NA NA 1 ...
# $ col5: Factor w/ 2 levels "Local","Overseas": 1 1 1 1 1 1 1 1 1 1 ...
# $ cc1 : num  1 1 1 1 1 1 1 1 1 1 ...

---

如果它不是一个list ，而是两个向量，则只需将names(faclist)更改为“col_names”向量，将list “faclist”更改为labels向量

ldat1 <- map(ldat, ~  {
     .x[col_names] <- map2(.x %>% 
                             dplyr::select(col_names), 
                         labels, ~ factor(.x, labels= .y))
       .x} )