考虑以下数据框
time <-c('2016-04-13 23:07:45','2016-04-13 23:07:50','2016-04-13 23:08:45','2016-04-13 23:08:45'
,'2016-04-13 23:08:45','2016-04-13 23:07:50','2016-04-13 23:07:51')
group <-c('A','A','A','B','B','B','B')
value<- c(5,10,2,2,NA,1,4)
df<-data.frame(time,group,value)
> df
time group value
1 2016-04-13 23:07:45 A 5
2 2016-04-13 23:07:50 A 10
3 2016-04-13 23:08:45 A 2
4 2016-04-13 23:08:45 B 2
5 2016-04-13 23:08:45 B NA
6 2016-04-13 23:07:50 B 1
7 2016-04-13 23:07:51 B 4
我想在5 seconds level - group level 重新取样此数据框，并计算每个value的总和 time-interval - group value。
间隔应在左侧关闭，在右侧打开。例如，第一行输出应为
2016-04-13 23:07:45 A 5因为前5秒间隔为[2016-04-13 23:07:45, 2016-04-13 23:07:50[
如何在dplyr或data.table中执行此操作？我是否需要导入lubridate作为时间戳？
答案 0 :(得分：6)
使用data.table的最新版本（1.9.8 +）：
library(data.table)
# convert to data.table, fix time, add future time
setDT(df)
df[, time := as.POSIXct(time)][, time.5s := time + 5]
# use non-equi join to filter on the required intervals and sum
df[, newval := df[df, on = .(group, time < time.5s, time >= time),
sum(value, na.rm = T), by = .EACHI]$V1]
df
# time group value time.5s newval
#1: 2016-04-13 23:07:45 A 5 2016-04-13 23:07:50 5
#2: 2016-04-13 23:07:50 A 10 2016-04-13 23:07:55 10
#3: 2016-04-13 23:08:45 A 2 2016-04-13 23:08:50 2
#4: 2016-04-13 23:08:45 B 2 2016-04-13 23:08:50 2
#5: 2016-04-13 23:08:45 B NA 2016-04-13 23:08:50 2
#6: 2016-04-13 23:07:50 B 1 2016-04-13 23:07:55 5
#7: 2016-04-13 23:07:51 B 4 2016-04-13 23:07:56 4
答案 1 :(得分：2)
最好的想法是我来data.table：
library(data.table)
setDT(df)
df[, result:={lv=df$group==group; dt=difftime( df$time, time, units="sec"); print(dt); sum(df$value[lv & dt >= 0 & dt < 5],na.rm=TRUE)},by=1:nrow(df)]
输出：
time group value result
1: 2016-04-13 23:07:45 A 5 5
2: 2016-04-13 23:07:50 A 10 10
3: 2016-04-13 23:08:45 A 2 2
4: 2016-04-13 23:08:45 B 2 2
5: 2016-04-13 23:08:45 B NA 2
6: 2016-04-13 23:07:50 B 1 5
7: 2016-04-13 23:07:51 B 4 4
j部分详情：
lv=df$group==group # Create a logical vector to filter at end
dt=abs( difftime( df$time, time, units="sec")) # compute the time difference in seconds between current row and all others
sum(df$value[lv & dt >= 0 & dt < 5]) # Sum the values where in same group and the difference in seconds is between 0 and 5 secs, 0 included, 5 excluded
result:={}允许我们将结果创建为函数调用。 by=1:nrow(df)使其逐行运行。
并过滤结果只得到起点：
> df[,.SD[!duplicated(result)],by=group]
group time value result
1: A 2016-04-13 23:07:45 5 5
2: A 2016-04-13 23:07:50 10 10
3: A 2016-04-13 23:08:45 2 2
4: B 2016-04-13 23:08:45 2 2
5: B 2016-04-13 23:07:50 1 5
6: B 2016-04-13 23:07:51 4 4
答案 2 :(得分：2)
如果您愿意为每个组分别设置单独的数据对象，则可以使用xts来解决您的问题，而不是每组对象data.table。 xts period.apply将自动处理您在左侧关闭但在右侧打开的时间间隔（这对于将金融蜱数据汇总到条形频率非常有用。对于连续条形，您不会在间隔边缘上重复计算蜱数/间隔）：
time <-c('2016-04-13 23:07:45','2016-04-13 23:07:55','2016-04-13 23:08:45','2016-04-13 23:08:45'
,'2016-04-13 23:08:45','2016-04-13 23:07:50','2016-04-13 23:07:51')
group <-c('A','A','A','B','B','B','B')
value<- c(5,10,2,2,NA,1,4)
df=data.frame(time,group,value)
library(quantmod)
library(lubridate)
df$time = ymd_hms(df$time)
# In this example, model group B object: (You can easily generalise this with a loop or lapply over multiple groups)
df_grp <- df[df$group == "B", ]
x.df_grp <- xts(df_grp$value, order.by = df_grp$time)
ep <- endpoints(x.df_grp, on = "seconds", k = 5)
# You can replace sum by any useful function. Pass in extra arguments to period.apply that correspond to FUN, here na.rm = T, to avoid having sum returning NA in your group B row:
x.df_grp_5sec <- period.apply(x.df_grp, ep, FUN = sum, na.rm = TRUE)
# Align timestamps to end of each 5 sec interval by default (helps avoid lookforward bias when merging time series data on different time frequencies):
x.df_grp_5sec <- align.time(x.df_grp_5sec, 5)
# Now record timestamps at start of each 5 sec interval:
.index(x.df_grp_5sec) <- .index(x.df_grp_5sec) - 5
#result:
> x.df_grp_5sec
[,1]
2016-04-13 23:07:50 5
2016-04-13 23:08:45 2
答案 3 :(得分：2)
这个怎么样：
library(dplyr)
Group5 <- function(myDf) {
myDf$time <- ymd_hms(myDf$time)
myDf$timeGroup <- floor_date(myDf$time, unit = "5 seconds")
summarise(myDf %>% group_by(group, timeGroup), sum(value, na.rm = TRUE))
}
Group5(df)
Source: local data frame [5 x 3]
Groups: group [?]
group timeGroup `sum(value, na.rm = TRUE)`
<fctr> <dttm> <dbl>
1 A 2016-04-13 23:07:45 5
2 A 2016-04-13 23:07:50 10
3 A 2016-04-13 23:08:45 2
4 B 2016-04-13 23:07:50 5
5 B 2016-04-13 23:08:45 2
利用floor_date中的ymd_hms和lubridate将每个日期时间放入正确的群组时间。
这是一个更具异国情调的例子：
set.seed(500)
time <- ymd_hms('2016-04-13 23:07:45') + sample(-10^3:10^3, 10^5, replace=TRUE)
group <- rep(LETTERS[1:20], each = 5000)
value <- rep(NA, 10^5)
value[sample(10^5, 95000)] <- sample(100, 95000, replace=TRUE)
df2 <- data.frame(time,group,value)
head(df2)
time group value
1 2016-04-13 23:18:53 A 53
2 2016-04-13 23:15:15 A NA
3 2016-04-13 23:23:36 A 40
4 2016-04-13 23:06:40 A 23
5 2016-04-13 23:18:10 A 74
6 2016-04-13 22:57:56 A 65
打电话给我们：
Group5(df2)
Source: local data frame [8,020 x 3]
Groups: group [?]
group timeGroup `sum(value, na.rm = TRUE)`
<fctr> <dttm> <int>
1 A 2016-04-13 22:51:05 379
2 A 2016-04-13 22:51:10 646
3 A 2016-04-13 22:51:15 391
4 A 2016-04-13 22:51:20 1118
5 A 2016-04-13 22:51:25 745
6 A 2016-04-13 22:51:30 546
7 A 2016-04-13 22:51:35 884
8 A 2016-04-13 22:51:40 711
9 A 2016-04-13 22:51:45 526
10 A 2016-04-13 22:51:50 484
# ... with 8,010 more rows