R 如何修改所选行的n following值

我想将1后面的3个值替换为1，并将所有剩余的NAs替换为0

我的数据（可复制示例） 我想要什么 到目前为止我在哪里 我正在努力避免在R中出现一个巨大的for循环。我知道这不是正确的方向，但我不知道还能做什么

get_all_the_ones <- function(col) { 

    for (ro in 1:nrow(datadf[col])) {
  
      if (datadf[ro,col] == 1) {
        datadf[seq(ro, ro+3), col] = 1
      }
  
    }

}

---

get_all_the_one这可以通过编写这样的帮助函数轻松完成

my_fun <- function(x){
  require(dplyr)
  cond <- (x == 1) | (lag(x, 1) == 1) | (lag(x, 2) == 1) | (lag(x, 3) == 1)
  new_values <- if_else(cond == TRUE, 1, 0, missing = 0)
  return(new_values)
}

您需要
lag
功能。试试这个：

library(dplyr)
x <- datadf$typeA

a <- (is.na(x) & lag(x,1)==1) |
  (is.na(x) & is.na(lag(x)) & lag(x,2)==1) |
  (is.na(x) & is.na(lag(x)) & is.na(lag(x,2)) & lag(x,3)==1)

a[is.na(a)] <- FALSE

datadf$typeA[a] <- 1
datadf$typeA[is.na(datadf$typeA)] <- 0


x <- datadf$typeB

a <- (is.na(x) & lag(x,1)==1) |
  (is.na(x) & is.na(lag(x)) & lag(x,2)==1) |
  (is.na(x) & is.na(lag(x)) & is.na(lag(x,2)) & lag(x,3)==1)

a[is.na(a)] <- FALSE

datadf$typeB[a] <- 1
datadf$typeB[is.na(datadf$typeB)] <- 0

此代码只替换
NA
值。
非常感谢，它可以完美地工作，只需最少的代码即可理解。这里的诀窍是创建一个条件向量，明白了。谢谢你，你的解决方案对于基R来说非常好。我选择了dplyr，因为它可以更好地扩展我的真实数据帧。我感谢你的努力。
get_all_the_ones <- function(col) { 

    for (ro in 1:nrow(datadf[col])) {
  
      if (datadf[ro,col] == 1) {
        datadf[seq(ro, ro+3), col] = 1
      }
  
    }

}

my_fun <- function(x){
  require(dplyr)
  cond <- (x == 1) | (lag(x, 1) == 1) | (lag(x, 2) == 1) | (lag(x, 3) == 1)
  new_values <- if_else(cond == TRUE, 1, 0, missing = 0)
  return(new_values)
}

library(dplyr)

datadf %>% 
  mutate(across(starts_with("type"), ~my_fun(.)))
#               seq_time typeA typeB
# 1  2020-09-01 04:30:00     0     0
# 2  2020-09-01 04:30:01     0     0
# 3  2020-09-01 04:30:02     0     0
# 4  2020-09-01 04:30:03     0     1
# 5  2020-09-01 04:30:04     0     1
# 6  2020-09-01 04:30:05     1     1
# 7  2020-09-01 04:30:06     1     1
# 8  2020-09-01 04:30:07     1     1
# 9  2020-09-01 04:30:08     1     0
# 10 2020-09-01 04:30:09     0     0
# 11 2020-09-01 04:30:10     0     0
# 12 2020-09-01 04:30:11     0     0

library(dplyr)
x <- datadf$typeA

a <- (is.na(x) & lag(x,1)==1) |
  (is.na(x) & is.na(lag(x)) & lag(x,2)==1) |
  (is.na(x) & is.na(lag(x)) & is.na(lag(x,2)) & lag(x,3)==1)

a[is.na(a)] <- FALSE

datadf$typeA[a] <- 1
datadf$typeA[is.na(datadf$typeA)] <- 0


x <- datadf$typeB

a <- (is.na(x) & lag(x,1)==1) |
  (is.na(x) & is.na(lag(x)) & lag(x,2)==1) |
  (is.na(x) & is.na(lag(x)) & is.na(lag(x,2)) & lag(x,3)==1)

a[is.na(a)] <- FALSE

datadf$typeB[a] <- 1
datadf$typeB[is.na(datadf$typeB)] <- 0

#               seq_time typeA typeB
# 1  2020-09-01 04:30:00     0     0
# 2  2020-09-01 04:30:01     0     0
# 3  2020-09-01 04:30:02     0     0
# 4  2020-09-01 04:30:03     0     1
# 5  2020-09-01 04:30:04     0     1
# 6  2020-09-01 04:30:05     1     1
# 7  2020-09-01 04:30:06     1     1
# 8  2020-09-01 04:30:07     1     1
# 9  2020-09-01 04:30:08     1     0
# 10 2020-09-01 04:30:09     0     0
# 11 2020-09-01 04:30:10     0     0
# 12 2020-09-01 04:30:11     0     0