我有一组工作代码，使用下面的 for 循环来处理数据帧，但如果可能的话，需要在没有 for 循环的情况下对其进行优化。 我已经搜索了一段时间才能找到这样的东西，但一定不知道正确的搜索词。感谢您的任何帮助。
数据帧示例(底部的较长版本)具有日期时间列和瓶子列。瓶子列从某个数字(下面的 1)开始，并在添加样品时重复并切换到 2，依此类推，直到瓶子 7(在本例中)，然后从 1 重新开始并转到 14(在本例中)并一遍又一遍。(注意，真实文件中每瓶有2次以上)
datetime bottle
6/9/2016 0:00 1
6/9/2016 0:15 1
6/9/2016 0:30 1
6/9/2016 0:45 1
6/9/2016 1:00 2
6/9/2016 1:15 2
6/9/2016 1:30 2
6/9/2016 1:45 3
6/9/2016 2:00 3
6/9/2016 2:15 4
6/9/2016 2:30 4
6/9/2016 2:45 5
6/9/2016 3:00 5
6/9/2016 3:15 6
6/9/2016 3:30 6
6/9/2016 3:45 7
6/9/2016 4:00 7
6/9/2016 4:15 7
6/9/2016 4:30 1
6/9/2016 4:45 1
6/9/2016 5:00 1
6/9/2016 5:15 2
6/9/2016 5:30 2
6/9/2016 5:45 2
6/9/2016 6:00 3
6/9/2016 6:15 3
6/9/2016 6:30 3
我需要创建一个包含瓶子开始和结束时间的新数据帧。请注意，每个瓶子序列都是重复的。
bottle begin end
1 6/9/2016 0:00 6/9/2016 0:45
2 6/9/2016 1:00 6/9/2016 1:30
3 6/9/2016 1:45 6/9/2016 2:00
4 6/9/2016 2:15 6/9/2016 2:30
5 6/9/2016 2:45 6/9/2016 3:00
6 6/9/2016 3:15 6/9/2016 3:30
7 6/9/2016 3:45 6/9/2016 4:15
1 6/9/2016 4:30 6/9/2016 5:00
2 6/9/2016 5:15 6/9/2016 5:45
3 6/9/2016 6:00 6/9/2016 6:30
到目前为止，我所做的是下面的注释代码。这运行良好，但在整个数据帧上需要很长时间。
#create id number for each bottle using data.table
setDT(t2s_bottle_timing.df)[, id := .GRP, by = t2s_bottle]
#declare/set variables
x1 <- 1
x2 <- 1
x3 <- 1
i <- 1
N <- length(t2s_bottle_timing.df$t2s_bottle)
#renumber id column to have unique id for each bottle run
for (i in 2:(N-1)) {
x1 <- t2s_bottle_timing.df[(i) , 2] #load bottle numbers
x2 <- t2s_bottle_timing.df[(i+1) , 2] #load bottle numbers
if (x2 == x1) { t2s_bottle_timing.df[(i),3] <- x3 } #set id number
if (x2 != x1) { x3 <- x3 +1} #increment id number
t2s_bottle_timing.df[(i+1),3] <- x3 #load new id number into table
}
# get rid of unused stuff
rm(x1, x2, i, N, x3)
# summerise the raw dataframe to produce the bottle, begin, end dataframe
t2s_timing_output.df <- t2s_bottle_timing.df %>% group_by( id ,t2s_bottle )
%>% #group_by(id,bottle)
summarize(
begin = min(datetime),
end = max(datetime) )
所以这行得通，但我渴望学习另一种方法和更有效的方法。
t2s_bottle_timing.df <- structure(list(datetime = structure(c(1465514100, 1465515000,
1465515900, 1465516800, 1465517700, 1465518600, 1465519500, 1465520400,
1465521300, 1465522200, 1465523100, 1465524000, 1465524900, 1465525800,
1465526700, 1465527600, 1465528500, 1465529400, 1465530300, 1465531200,
1465532100, 1465533000, 1465533900, 1465534800, 1465535700, 1465536600,
1465537500, 1465538400, 1465539300, 1465540200, 1465541100, 1465542000,
1465542900, 1465543800, 1465544700, 1465545600, 1465546500, 1465547400,
1465548300, 1465549200, 1465550100, 1465551000, 1465551900, 1465552800,
1465553700, 1465554600, 1465555500, 1465556400, 1465557300, 1465558200,
1465559100, 1465560000, 1465560900, 1465561800, 1465562700, 1465563600,
1465564500, 1465565400, 1465566300, 1465567200, 1465568100, 1465569000,
1465569900, 1465570800, 1465571700, 1465572600, 1465573500, 1465574400,
1465575300, 1465576200, 1465577100, 1465578000, 1465578900, 1465579800,
1465580700, 1465581600, 1465582500, 1465583400, 1465584300, 1465585200,
1465586100, 1465587000, 1465587900, 1465588800, 1465589700, 1465590600,
1465591500), tzone = "UTC", class = c("POSIXct", "POSIXt")),
t2s_bottle = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L)), .Names = c("datetime", "t2s_bottle"), row.names = c(NA,
-87L), spec = structure(list(cols = structure(list(datetime = structure(list(), class = c("collector_character",
"collector")), t2s_bottle = structure(list(), class = c("collector_integer",
"collector"))), .Names = c("datetime", "t2s_bottle")), default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"), class = c("tbl_df",
"tbl", "data.frame"))
你的例子让我有点困惑，但如果你想要创建一个索引，也许cumsum逻辑可能会有所帮助：
t2s_bottle_timing.df %>%
mutate(index = cumsum(t2s_bottle != dplyr::lag(t2s_bottle, default = 0))) %>%
group_by(index, t2s_bottle) %>%
summarise(begin = min(datetime), end = max(datetime))
index t2s_bottle begin end
<int> <int> <dttm> <dttm>
1 1 1 2016-06-09 23:15:00 2016-06-10 00:15:00
2 2 2 2016-06-10 00:30:00 2016-06-10 02:15:00
3 3 3 2016-06-10 02:30:00 2016-06-10 04:30:00
4 4 4 2016-06-10 04:45:00 2016-06-10 06:00:00
5 5 5 2016-06-10 06:15:00 2016-06-10 07:45:00
6 6 6 2016-06-10 08:00:00 2016-06-10 09:00:00
7 7 7 2016-06-10 09:15:00 2016-06-10 10:15:00
8 8 1 2016-06-10 10:30:00 2016-06-10 11:15:00
9 9 2 2016-06-10 11:30:00 2016-06-10 13:00:00
10 10 3 2016-06-10 13:15:00 2016-06-10 13:30:00
11 11 4 2016-06-10 13:45:00 2016-06-10 15:00:00
12 12 5 2016-06-10 15:15:00 2016-06-10 15:45:00
13 13 6 2016-06-10 16:00:00 2016-06-10 17:15:00
14 14 7 2016-06-10 17:30:00 2016-06-10 18:45:00
15 15 8 2016-06-10 19:00:00 2016-06-10 20:45:00