一种基于基本R的简单方法：
```r
na_locf_max <- function(x, nmax){
  # 将向量拆分为数字和NA的序列
  s <- split(x, cumsum(!is.na(x)))
  # 将第一个nmax个值分配给第一个值，并修剪以匹配长度
  l <- mapply(\(x, y) {
    x[1:nmax+1] <- x[1]
    length(x) <- y
    x
  }, s, lengths(s))
  # 格式化为向量格式
  unlist(l, use.names = FALSE)
}
na_locf_max(x, nmax = 2)
# [1] 58 58 58 NA NA 13 13 13 NA 12 23 23 12

<details>
<summary>英文:</summary>
One crude way in base R:
```r
na_locf_max &lt;- function(x, nmax){
  # Split the vector in sequence of numbers and NAs
  s &lt;- split(x, cumsum(!is.na(x)))
  # Assign the first nmax value to the first value, and trim to match length
  l &lt;- mapply(\(x, y) {
    x[1:nmax+1] &lt;- x[1]
    length(x) &lt;- y
    x
  }, s, lengths(s))
  #Format to vector format
  unlist(l, use.names = FALSE)
}
na_locf_max(x, nmax = 2)
# [1] 58 58 58 NA NA 13 13 13 NA 12 23 23 12

library(data.table)
dt[, .(V1, grp = rleid(V1), V1shift = shift(V1, 1)),][
   , .(V1, ifelse(is.na(V1shift), shift(V1shift, 1), V1shift)), by = grp][
   , .(V1, res = ifelse(is.na(V1), V2, V1)),]
    V1 res
 1: 58  58
 2: NA  58
 3: NA  58
 4: NA  NA
 5: NA  NA
 6: 13  13
 7: NA  13
 8: NA  13
 9: NA  NA
10: 12  12
11: 23  23
12: NA  23
13: 12  12

dt <- structure(list(V1 = c(58, NA, NA, NA, NA, 13, NA, NA, NA, 12, 
23, NA, 12)), row.names = c(NA, -13L), class = c("data.table", 
"data.frame"))

library(data.table)
dt = data.table(V1 = c(58, NA, NA, NA, NA, 13, NA, NA, NA, 12, 23, NA, 12))
dt[, V2 := fifelse(is.na(V1) & rowid(rleid(V1)) <= 2, nafill(V1, "locf"), V1)]