R语言实战之如何对数据进行缺失值处理

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VIP免费原创最新推荐文章于 2026-06-27 09:09:14 发布 2.1k 阅读 AI 写同款· GEO 优化 ›

别再只会用na.omit了！R语言数据缺失值处理的3种进阶实战（附随机森林与mice代码）本文深入探讨R语言中缺失值处理的三种高阶策略，包括随机森林填补和多重插补技术。通过实战案例和代码示例，帮助数据分析师超越简单的na.omit方法，有效处理非随机缺失数据，提升模型的可靠性和准确性。特别适合处理临床数据和复杂缺失模式场景。阅读详情

R语言实战之如何对数据进行缺失值处理

以下是对于数据中含有部分缺失值的处理方式，代码十分详尽基础：

下面展示一些 基础代码。

manager <- c(1,2,3,4,5)
date <- c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
country <- c("US","US","UK","UK","UK")
gender <- c("M","F","F","M","F")
age <- c(32,45,25,39,99)
q1 <- c(5,3,3,3,2)
q2 <- c(4,5,5,3,2)
q3 <- c(5,2,5,4,1)
q4 <- c(5,5,5,NA,2)
q5 <- c(5,5,2,NA,1)
leadership <- data.frame(manager, date, country, gender, age,
                         q1,q2,q3,q4,q5,stringsAsFactors = FALSE)
#stringsAsFactors = FALSE遇到字符型的数值的时候，不将其转换成变量
leadership

leadership$age[leadership$age == 99] <- NA
#将99岁的数据列为缺失值
leadership <- within(leadership,{
  agecat <- NA
  agecat[age > 75] <- "elder"
  agecat[age >= 55 & age <=75] <- "Middle Aged"
  agecat[age < 55] <- "Young"})

leadership

library("plyr")
#fix(leadership)
#打开数据编辑器进行修改，当然也可直接修改，示例如下：
leadership <- rename(leadership, c(manager = "Manager ID", date = "Testdate"))
names(leadership)[6:10] <- c("item1","item2","item3","item4","item5")
leadership
is.na(leadership)
#检测是否有缺失值

x <- c(1,2,NA,3)
y <- sum(x,na.rm = "true")
y
#na.rm移除缺失的值为真
#na.omit忽略缺失值
leadership
newdata <- na.omit(leadership)
newdata

> manager <- c(1,2,3,4,5)
> date <- c("10/24/08","10/28/08","10/1/08","10/12/08","5/1/09")
> country <- c("US","US","UK","UK","UK")
> gender <- c("M","F","F","M","F")
> age <- c(32,45,25,39,99)
> q1 <- c(5,3,3,3,2)
> q2 <- c(4,5,5,3,2)
> q3 <- c(5,2,5,4,1)
> q4 <- c(5,5,5,<