数据清理-转换为tidyverse

我很好奇下面的代码是否可以转换为tidyverse代码。我尝试过dplyr:：mutate，但未能使其正常工作

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"

---

df$Gender[df$Gender==“M”]这里有一种方法，使用
dplyr:：case\u when（）
：

最后，还有一个提示：当有很多独特的值需要分组时，使用
case\u when（）
（或嵌套
ifelse（）
s，或子集赋值等）可能会变得相当乏味。避免很多痛苦的一种方法是使用命名向量用字典样式的“查找表”（非正式术语——请参阅一些背景资料）替换每个值。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)

#唯一值
性别值这里有一种方法，使用
dplyr:：case\u when（）
：

最后，还有一个提示：当有很多独特的值需要分组时，使用
case\u when（）
（或嵌套
ifelse（）
s，或子集赋值等）可能会变得相当乏味。避免很多痛苦的一种方法是使用命名向量用字典样式的“查找表”（非正式术语——请参阅一些背景资料）替换每个值。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)

#唯一值
性别价值观
# 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)