# 创建示例数据
data <- data.frame(score = c(85, 72, 90, 65, 78, 92, 55, 88))

# 将分数重新编码为等级
data$grade <- ifelse(data$score >= 90, "A",
                    ifelse(data$score >= 80, "B",
                          ifelse(data$score >= 70, "C",
                                ifelse(data$score >= 60, "D", "F"))))

# 将连续变量分箱
data$grade_cut <- cut(data$score,
                     breaks = c(0, 60, 70, 80, 90, 100),
                     labels = c("F", "D", "C", "B", "A"),
                     right = FALSE)

library(dplyr)

# 创建示例数据
data <- data.frame(gender = c("M", "F", "F", "M", "Other"))

# 重新编码性别变量
data$gender_recoded <- recode(data$gender,
                             "M" = "Male",
                             "F" = "Female",
                             .default = "Other")

data$grade_case <- case_when(
  data$score >= 90 ~ "A",
  data$score >= 80 ~ "B",
  data$score >= 70 ~ "C",
  data$score >= 60 ~ "D",
  TRUE ~ "F"
)

# 创建包含多个需要重新编码的列的数据框
data <- data.frame(
  var1 = c(1, 2, 3, 4, 5),
  var2 = c(5, 4, 3, 2, 1),
  var3 = c(3, 3, 3, 3, 3)
)

# 定义重新编码的函数
recode_fn <- function(x) {
  ifelse(x > 3, "High", "Low")
}

# 在循环中重新编码
for (col in c("var1", "var2", "var3")) {
  data[[paste0(col, "_recoded")]] <- recode_fn(data[[col]])
}

# 使用lapply在多个列上应用重新编码
data_recoded <- as.data.frame(lapply(data, function(x) {
  ifelse(x > 3, "High", "Low")
}))
colnames(data_recoded) <- paste0(colnames(data), "_recoded")

data$grade <- factor(data$grade, levels = c("F", "D", "C", "B", "A"), ordered = TRUE)

data$grade <- ifelse(is.na(data$score), "Missing",
                    ifelse(data$score >= 90, "A", "B"))

# 更高效的方式
data$grade <- cut(data$score,
                 breaks = c(0, 60, 70, 80, 90, 100),
                 labels = c("F", "D", "C", "B", "A"))