下面是一个tidyverse解决方案：

library(tidyverse)

df <- df %>% mutate(dates = as.Date(dates))  # type cast from Q sample

ddd <- filter(df, !is.na(x)) %>% pull(dates) # vector of dates for which x'es are available

useDatediff <- function(d){       # find nearest date for which value is present
  aftr <- min(ddd[ddd >= d] - d)  # and return the days difference (+) after or (-) before
  bfr <- min(d - ddd[ddd <= d])
    if (aftr > 30 && bfr > 30) return(0) # after left_join x will be NA
    if (bfr <= aftr) { # bfr is prevailing
      -bfr 
    } else {
      aftr
    } 
}

df %>% 
  rowwise() %>% 
  mutate(useDatediff = useDatediff(dates)
  ) %>% 
  mutate(useDate = dates + useDatediff) %>% 
  left_join(df, by = c("useDate" = "dates")) %>% 
  transmute(dates, 
            x = coalesce(x.x, x.y))

测试结果：

# A tibble: 11 × 2
# Rowwise: 
   dates          x
   <date>     <dbl>
 1 2023-09-01    10
 2 2023-09-02    10
 3 2023-09-05    20
 4 2023-09-06    20
 5 2023-09-10    30
 6 2023-09-11    30
 7 2023-09-14    30
 8 2023-09-16    30
 9 2023-09-20    40
10 2023-09-27    40
11 2023-09-28    40