如何填充R中data.frame的缺失值？

Question

I have multiple columns that has missing values . I want to use the mean of the same day across all years while filling the missing data for each column . for example, DF is my fake data where I see missing values for the two columns (A & X)

library(lubridate)
library(tidyverse)
library(naniar)

set.seed(123)

DF <- data.frame(Date = seq(as.Date("1985-01-01"), to = as.Date("1987-12-31"), by = "day"),
                 A = sample(1:10,1095, replace = T), X = sample(5:15,1095, replace = T)) %>% 
                replace_with_na(replace = list(A = 2, X = 5))

To fill in Column A , i use the following code

Fill_DF_A <- DF %>% 
          mutate(Year = year(Date), Month = month(Date), Day = day(Date)) %>% 
          group_by(Year, Day) %>% 
  mutate(A = ifelse(is.na(A), mean(A, na.rm=TRUE), A))

I have many columns in my data.frame and I would like to generalize this for all the columns to fill in the missing value?

Answer 1

We can use na.aggregate from zoo

library(dplyr)
library(zoo)
DF %>% 
  mutate(Year = year(Date), Month = month(Date), Day = day(Date)) %>% 
      group_by(Year, Day)  %>%
     mutate(across(A:X, na.aggregate))

---

Or if we prefer to use conditional statements

DF %>% 
  mutate(Year = year(Date), Month = month(Date), Day = day(Date)) %>% 
  group_by(Year, Day)  %>%
  mutate(across(A:X, ~ case_when(is.na(.) 
                 ~ mean(., na.rm = TRUE), TRUE ~ as.numeric(.))))