R语言数据清洗需求：多数据点时统一采用second数据点值

原始数据集 #

df <- data.frame(id = c(1,1,1, 2,2,2, 3,3,  4,4, 5),
                 text = c("female","male","male", "female","female","female", "male","female","male", "female", "female"),
                 time = c("first","second","third", "first","second","third", "first","second","second", "third", "first"))

解决方案 #

方案1：使用dplyr（tidyverse工具链）

library(dplyr)

df_cleaned <- df %>%
  group_by(id) %>%
  mutate(
    # 提取当前id下second采集点的gender值
    second_gender = first(text[time == "second"]),
    # 存在second记录则替换全组text，否则保留原数据
    text = ifelse(!is.na(second_gender), second_gender, text)
  ) %>%
  select(-second_gender) %>%
  ungroup()

# 输出结果
df_cleaned

方案2：Base R实现（无需额外安装包）

# 按id拆分数据集
split_groups <- split(df, df$id)

# 逐个处理每个id的分组
cleaned_groups <- lapply(split_groups, function(group) {
  second_gender <- group$text[group$time == "second"]
  # 若存在second采集记录，替换当前id所有text
  if (length(second_gender) > 0) {
    group$text <- second_gender[1]
  }
  return(group)
})

# 合并分组并重置行号
df_cleaned <- do.call(rbind, cleaned_groups)
rownames(df_cleaned) <- NULL

# 输出结果
df_cleaned

验证结果 #

两种方法都会得到符合预期的清洗后数据：

id   text   time
1   1   male  first
2   1   male second
3   1   male  third
4   2 female  first
5   2 female second
6   2 female  third
7   3 female  first
8   3 female second
9   4   male second
10  4   male  third
11  5 female  first

内容的提问来源于stack exchange，提问作者amisos55