用正确的值替换NA值

Question

假设我们具有以下数据框：

ID <- c(1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 6, 6, 6)
age <- c(25, 25, 25, 22, 22, 56, 56, 56, 80, 33, 33, 90, 90, 90, 5, 5, 5)
gender <- c("m", "m", NA, "f", "f", "m", NA, "m", "m", "m", NA, NA, NA, "m", NA, NA, NA)
company <- c("c1", "c2", "c2", "c3", "c3", "c1", "c1", "c1", "c1", "c5", "c5", "c3", "c4", "c5", "c3", "c1", "c1")
income <- c(1000, 1000, 1000, 500, 1700, 200, 200, 250, 500, 700, 700, 300, 350, 300, 500, 1700, 200)

df <- data.frame(ID, age, gender, company, income)

在此数据中，我们有6个唯一的IDs，如果您查看gender变量，有时会包含NA

我想用正确的性别类别替换NAs。另外，如果ID包含所有NA的性别，请保持原样。

预期结果将是：

Answer 1

使用tidyverse库，您可以这样做

library(tidyverse)
# for each ID get the gender
df_gender_ref <- df %>% filter(!is.na(gender)) %>% select(ID,gender) %>% unique() 
# add the new gender column to the original dataframe
df %>% select(-gender) %>% left_join(df_gender_ref) 

Answer 2

使用df$gender <- with(df, ave(gender, ID, FUN = function(x) na.omit(x)[1])) ID age gender company income 1 1 25 m c1 1000 2 1 25 m c2 1000 3 1 25 m c2 1000 4 2 22 f c3 500 5 2 22 f c3 1700 6 3 56 m c1 200 7 3 56 m c1 200 8 3 56 m c1 250 9 3 80 m c1 500 10 4 33 m c5 700 11 4 33 m c5 700 12 5 90 m c3 300 13 5 90 m c4 350 14 5 90 m c5 300 15 6 5 <NA> c3 500 16 6 5 <NA> c1 1700 17 6 5 <NA> c1 200 -

dplyr

使用tidyr和df %>% group_by(ID) %>% mutate(gender = na.omit(gender)[1]) df %>% group_by(ID) %>% fill(gender, .direction = "up") %>% fill(gender, .direction = "down") 的某些方法-

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