数据清理 - 转换为 tidyverse

Question

我很好奇以下代码是否可以转换为 tidyverse 代码。我试过 dplyr::mutate 但没能正常工作。

df$Gender[df$Gender == "M"] <- "Man"
df$Gender[df$Gender == "Male"] <- "Man"
df$Gender[df$Gender == "F"] <- "Woman"
df$Gender[df$Gender == "Female"] <- "Woman"
df$Gender[df$Gender == "M & F"] <- "Man and Woman"
df$Gender[df$Gender == "Male & Female"] <- "Man and Woman"

Answer 1

这是一种方法，dplyr::case_when():

df$Gender <- dplyr::case_when(
  df$Gender %in% c("M", "Male") ~ "Man", 
  df$Gender %in% c("F", "Female") ~ "Woman",
  df$Gender %in% c("M & F", "Male & Female") ~ "Man and Woman",
  TRUE ~ NA_character_)

或者，如果您想使用典型的 dplyr::/magrittr:: pipe-chain 方法：

df <- df %>% mutate(Gender = case_when(
  Gender %in% c("M", "Male") ~ "Man", 
  Gender %in% c("F", "Female") ~ "Woman",
  Gender %in% c("M & F", "Male & Female") ~ "Man and Woman",
  TRUE ~ NA_character_))

最后，提示：当需要对大量唯一值进行分组时，使用 case_when()（或嵌套 ifelse() 或子集赋值等）会变得非常乏味.避免大部分痛苦的一种方法是使用命名向量将每个值替换为 dictionary-style "lookup table"（非正式术语——请参阅 wiki on "associative array" 了解一些背景知识）。根据我的经验，这通常感觉最干净：

# the unique values 
gender_values <- c("M","Man","Male","F","Woman","Female","MF","male-female")

# associate unique values with our new labels: "m", "f", and "b"
gender_lkup <- setNames(c("m","m","m","f","f","f","b","b"), gender_values)

# suppose this is a column of a df 
raw_column <- sample(gender_values, 10, replace=TRUE)

# create a clean one with `gender_lkup` 
clean_column <- gender_lkup[raw_column]

# inspect the two vectors side-by-side
data.frame(original=raw_column, cleaned=clean_column)

数据清理 - 转换为 tidyverse

data cleaning - conversion to tidyverse

r

tidyverse

data-munging