选择标记 R 周围的行 [英] Select rows around a marker R

问题描述

我正在尝试在相对较大的数据框中选择标记前后的 100 行.标记很稀疏，出于某种原因，我无法弄清楚或找到解决方案 - 这似乎不应该那么难，所以我可能遗漏了一些明显的东西.

I'm trying to select 100 rows before and after a marker in a relatively large dataframe. The markers are sparse and for some reason I haven't been able to figure it out or find a solution - this doesn't seem like it should be that hard, so I'm probably missing something obvious.

这是一个非常小的简单示例，说明数据的样子:

Here's a very small simple example of what the data looks like:

timestamp talking_yn transition_yn
0.01      n          n
0.02      n          n
0.03      n          n
0.04      n          n
0.05      n          n
0.06      n          n
0.07      n          n
0.08      n          n
0.09      n          n
0.10      n          n
0.11      y          y
0.12      y          n
0.13      y          n
0.14      y          n
0.15      y          n
0.16      y          n
0.17      y          n
0.18      y          n

我尝试使用来自各种答案的不同方法(zoo 或 dplyr 中的 lag)，但它们都专注于选择一行或仅子集那些带有标记的行.对于虚拟示例数据，我将如何选择 transition == 'y' 行之前和之后的 5 行?

I've tried using different methods from a variety of answers (lag from zoo or dplyr), but they all focus on selecting one row or subsetting only those rows with the marker. For the dummy example data, how would I select the 5 rows before and after the transition == 'y' row?

推荐答案

我有一个快速功能:

#' Lead/Lag a logical
#'
#' @param lgl logical vector
#' @param bef integer, number of elements to lead by
#' @param aft integer, number of elements to lag by
#' @return logical, same length as 'lgl'
#' @export
leadlag <- function(lgl, bef = 1, aft = 1) {
  n <- length(lgl)
  bef <- min(n, max(0, bef))
  aft <- min(n, max(0, aft))
  befx <- if (bef > 0) sapply(seq_len(bef), function(b) c(tail(lgl, n = -b), rep(FALSE, b)))
  aftx <- if (aft > 0) sapply(seq_len(aft), function(a) c(rep(FALSE, a), head(lgl, n = -a)))
  rowSums(cbind(befx, lgl, aftx), na.rm = TRUE) > 0
}

dat[leadlag(dat$transition_yn == 'y', 2, 4),]
#    timestamp talking_yn transition_yn
# 9       0.09          n             n
# 10      0.10          n             n
# 11      0.11          y             y
# 12      0.12          y             n
# 13      0.13          y             n
# 14      0.14          y             n
# 15      0.15          y             n

<小时>

数据

dat <- read.table(header=TRUE, stringsAsFactor=FALSE, text="
timestamp talking_yn transition_yn
0.01      n          n
0.02      n          n
0.03      n          n
0.04      n          n
0.05      n          n
0.06      n          n
0.07      n          n
0.08      n          n
0.09      n          n
0.10      n          n
0.11      y          y
0.12      y          n
0.13      y          n
0.14      y          n
0.15      y          n
0.16      y          n
0.17      y          n
0.18      y          n")

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