创建在面板数据组中的条件上重新启动的顺序计数器 [英] Create sequential counter that restarts on a condition within panel data groups
问题描述
df< - data.frame（country = rep c（A，B），每个= 5），year = rep（2000：2004，times = 2），event = c（0,0,1,0,0,1,0,0,1 ，0），stringsAsFactors = FALSE）
我想要做的是创建一个计数器在每个国家的一系列观察结果中键入 df $ event 。当我们开始观察每个国家时，时钟从1开始;每年通过增加1;并且每当 df $ event == 1 重新启动。所需的输出是这样的：
国家年事件时钟
1 A 2000 0 1
2 A 2001 0 2
3 A 2002 1 1
4 A 2003 0 2
5 A 2004 0 3
6 B 2000 1 1
7 B 2001 0 2
8 B 2002 0 3
9 B 2003 1 1
10 B 2004 0 2
我尝试使用 getanID 从 splitstackshape 和的一些变体，如果和 ifelse 但到目前为止还没有得到所需的结果。
我已经在我需要这样做的脚本中使用 dplyr ，所以我更喜欢使用它或基于R的解决方案，但是我会感谢任何有用的东西。我的数据集不是很大，所以速度并不重要，但是效率总是很高。
c> dplyr 将是：
df％>％
group_by ，idx = cumsum（event == 1L））％>％
mutate（counter = row_number（））％>％
ungroup％>％
select（-idx）
#Source：本地数据框架[10 x 4]
＃
＃国家年度活动柜台
＃1 A 2000 0 1
＃2 A 2001 0 2
＃3 A 2002 1 1
＃4 A 2003 0 2
＃5 A 2004 0 3
＃6 B 2000 1 1
＃7 B 2001 0 2
＃8 B 2002 0 3
＃9 B 2003 1 1
＃10 B 2004 0 2
或使用 data.table ：
library（data.table）
setDT（df）[，counter：= seq_len（.N），by = list（country，cumsum（ev ent == 1L））]
编辑： code> group_by（country，idx = cumsum（event == 1L））用于按国家分组和新的分组索引idx。 event == 1L part创建一个逻辑索引，告诉我们列event是否为整数1（ TRUE / FALSE ）。然后，$ code> cumsum（...）从前2行开始为0，接下来3为2，接下来3为2，依此类推。我们使用这个新列（+国家/地区）根据需要对数据进行分组。如果您将最后一个删除到dplyr代码中的管道部件，可以查看。
I have a panel data set for which I would like to create a counter that increases with each step in the panel but restarts whenever some condition occurs. In my case, I'm using country-year data and want to count the passage of years between an event. Here's a toy data set with the key features of my real one:
df <- data.frame(country = rep(c("A","B"), each=5), year=rep(2000:2004, times=2), event=c(0,0,1,0,0,1,0,0,1,0), stringsAsFactors=FALSE)
What I'm looking to do is to create a counter that is keyed to df$event within each country's series of observations. The clock starts at 1 when we start observing each country; it increases by 1 with the passage of each year; and it restarts at 1 whenever df$event==1. The desired output is this:
country year event clock
1 A 2000 0 1
2 A 2001 0 2
3 A 2002 1 1
4 A 2003 0 2
5 A 2004 0 3
6 B 2000 1 1
7 B 2001 0 2
8 B 2002 0 3
9 B 2003 1 1
10 B 2004 0 2
I have tried using getanID from splitstackshape and a few variations of if and ifelse but have failed so far to get the desired result.
I'm already using dplyr in the scripts where I need to do this, so I would prefer a solution that uses it or base R, but I would be grateful for anything that works. My data sets are not massive, so speed is not critical, but efficiency is always a plus.
With dplyr that would be:
df %>%
group_by(country, idx = cumsum(event == 1L)) %>%
mutate(counter = row_number()) %>%
ungroup %>%
select(-idx)
#Source: local data frame [10 x 4]
#
# country year event counter
#1 A 2000 0 1
#2 A 2001 0 2
#3 A 2002 1 1
#4 A 2003 0 2
#5 A 2004 0 3
#6 B 2000 1 1
#7 B 2001 0 2
#8 B 2002 0 3
#9 B 2003 1 1
#10 B 2004 0 2
Or using data.table:
library(data.table)
setDT(df)[, counter := seq_len(.N), by = list(country, cumsum(event == 1L))]
Edit: group_by(country, idx = cumsum(event == 1L)) is used to group by country and a new grouping index "idx". The event == 1L part creates a logical index telling us whether the column "event" is an integer 1 or not (TRUE/FALSE). Then, cumsum(...) sums up starting from 0 for the first 2 rows, 1 for the next 3, 2 for the next 3 and so on. We use this new column (+ country) to group the data as needed. You can check it out if you remove the last to pipe-parts in the dplyr code.
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