Question

对于面板数据集（GSOEP），我需要创建一个时间计数器，该计时器在每个人的特定年份的虚拟编码1的事件之后给出delta t。例如。对于一个随机范围的年份，例如1990-2006，有一个个体的观察结果，其中一个单独的变量表明一年中的某个事件，例如计数器需要在下一年开始，应该以下一个人（id）结束，并且在该个人的事件发生之前需要为零。

目前数据如下：

df <- data.frame(id= rep(c("1","2","3"), each=6), year=rep(1998:2003, times=3), event=c(0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0), stringsAsFactors=FALSE)

   id year event
1   1 1998     0
2   1 1999     0
3   1 2000     1
4   1 2001     0
5   1 2002     0
6   1 2003     0
7   2 1998     0
8   2 1999     0
9   2 2000     0
10  2 2001     0
11  2 2002     1
12  2 2003     0
13  3 1998     0
14  3 1999     1
15  3 2000     0
16  3 2001     0
17  3 2002     0
18  3 2003     0

需要的是：

df <- data.frame(id= rep(c("1","2","3"), each=6), year=rep(1998:2003, times=3), event=c(0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0),delta=c(0,0,0,1,2,3,0,0,0,0,0,1,0,0,1,2,3,4), stringsAsFactors=FALSE)

   id year event delta
1   1 1998     0     0
2   1 1999     0     0
3   1 2000     1     0
4   1 2001     0     1
5   1 2002     0     2
6   1 2003     0     3
7   2 1998     0     0
8   2 1999     0     0
9   2 2000     0     0
10  2 2001     0     0
11  2 2002     1     0
12  2 2003     0     1
13  3 1998     0     0
14  3 1999     1     0
15  3 2000     0     1
16  3 2001     0     2
17  3 2002     0     3
18  3 2003     0     4

我怎样才能做到这一点？我得到的最接近的是：Create sequential counter that restarts on a condition within panel data groups

但是我不知道如何修改它以便它只在事件发生一次之后开始并在事件之前放置零。还有一些人没有事件，计数器需要给出零。每个人的年数（观察数）是不同的，因此从1984年到1999年，其他人的年龄范围是一致的。而在1995年至2015年期间，其他人的年数是这样的。

你会非常帮助我，我想提前感谢你的时间和精力。

最诚挚的问候，

朱利

Answer 1

您可以使用cumsum(cummax(event))和1...N来关闭 - 从event==1开始生成ifelse(...)。我将其打包在> 0中，以从library(tidyverse) df %>% group_by(id) %>% mutate(delta = ifelse(cumsum(cummax(event)) > 0, cumsum(cummax(event)) - 1, 0)) %>% ungroup() # A tibble: 18 x 4 # id year event delta # <chr> <int> <dbl> <dbl> # 1 1 1998 0. 0. # 2 1 1999 0. 0. # 3 1 2000 1. 0. # 4 1 2001 0. 1. # 5 1 2002 0. 2. # 6 1 2003 0. 3. # 7 2 1998 0. 0. # 8 2 1999 0. 0. # 9 2 2000 0. 0. # 10 2 2001 0. 0. # 11 2 2002 1. 0. # 12 2 2003 0. 1. # 13 3 1998 0. 0. # 14 3 1999 1. 0. # 15 3 2000 0. 1. # 16 3 2001 0. 2. # 17 3 2002 0. 3. # 18 3 2003 0. 4.的值中减去1。

table trucks
  id,created_at, ....

Answer 2

也许不是最优雅的版本，但如果您的数据集不是太大，以下几行可能就是一个开头。

library(data.table)
df <- data.frame(id= rep(c("1","2","3"), each=6), year=rep(1998:2003, times=3), event=c(0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0), stringsAsFactors=FALSE)
DT <- as.data.table(df)

get_delta <- function(x) {
  if (all(x == 0)) {
    return(x)
  } else {
    event_position <- which(x == 1)
    x[event_position] <- 0
    if (event_position == length(x)) {
     return(x) 
    } else {
     x[(event_position+1):length(x)] <- seq(length(x)-event_position)
     return(x)
    }
  }
}


DT[, delta:= get_delta(event), by = c("id")]
DT
# id year event delta
# 1:  1 1998     0     0
# 2:  1 1999     0     0
# 3:  1 2000     1     0
# 4:  1 2001     0     1
# 5:  1 2002     0     2
# 6:  1 2003     0     3
# 7:  2 1998     0     0
# 8:  2 1999     0     0
# 9:  2 2000     0     0
# 10:  2 2001     0     0
# 11:  2 2002     1     0
# 12:  2 2003     0     1
# 13:  3 1998     0     0
# 14:  3 1999     1     0
# 15:  3 2000     0     1
# 16:  3 2001     0     2
# 17:  3 2002     0     3
# 18:  3 2003     0     4

n_rows <- 1e6
DT_large <- data.table(id= as.character(rep(c(1:n_rows), each=6))
                       ,year=rep(1998:2003, n_rows), 
                       event = as.vector(sapply(1:n_rows, function(x) {
                         x <- rep(0, 6)
                         x[sample(6, 1)] <- 1  
                         x
                       }))
                       ,stringsAsFactors=FALSE)

system.time(DT_large[, delta:= get_delta(event), by = c("id")])
# User      System     elapsed 
# 9.30        0.02        9.35

#some benchmarking...
library(tidyverse)
library(data.table)
library(microbenchmark)

df <- data.frame(id= rep(c("1","2","3"), each=6), year=rep(1998:2003, times=3), event=c(0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0), stringsAsFactors=FALSE)

CPak_approach <- function() {
  df %>%
    group_by(id) %>%
    mutate(delta = ifelse(cumsum(cummax(event)) > 0, cumsum(cummax(event)) - 1, 0)) %>%
    ungroup()  
}

manuelbickel_approach <- function(x) {
  DT <- as.data.table(df)
  get_delta <- function(x) {
    if (all(x == 0)) {
      return(x)
    } else {
      event_position <- which(x == 1)
      x[event_position] <- 0
      if (event_position == length(x)) {
        return(x) 
      } else {
        x[(event_position+1):length(x)] <- seq(length(x)-event_position)
        return(x)
      }
    }
  }
  DT[, delta:= get_delta(event), by = c("id")]
}


microbenchmark(
  (dplyr_approach()),
  (manuelbickel_approach())
)

# Unit: microseconds
#       expr                      min        lq     mean   median       uq       max neval
# (dplyr_approach())         3731.146 3872.6625 4098.923 3985.363 4194.183  6441.475   100
# (manuelbickel_approach())   803.705  829.5605 1148.891 1014.105 1049.829 13993.372   100

创建顺序计数器，从事件前的事件和零开始，为面板

2 个答案: