所以我确实有一个配水模型的输出，它是每小时河流的流入和流量值。我做了5次模型运行
可重复的例子：
df <- data.frame(rep(seq(
from=as.POSIXct("2012-1-1 0:00", tz="UTC"),
to=as.POSIXct("2012-1-1 23:00", tz="UTC"),
by="hour"
),5),
as.factor(c(rep(1,24),rep(2,24),rep(3,24), rep(4,24),rep(5,24))),
rep(seq(1,300,length.out=24),5),
rep(seq(1,180, length.out=24),5) )
colnames(df)<-c("time", "run", "inflow", "discharge")
当然，实际上，运行的值是变化的。 （我确实有很多数据，因为我有100次运行，每小时值为35年）。
所以，起初我想计算每次运行的水稀缺系数，这意味着我需要计算类似（1 - （前6小时的排放/流入）），因为水需要6个小时才能通过集水区。
scarcityfactor <- 1 - (discharge / lag(inflow,6))
然后我想计算所有运行中的稀缺因子的平均值，最大值和最小值（以找出在每个时间步骤可能发生的稀缺度的最高，最低和平均值;根据不同的模型运行）。所以我想说，我可以计算每个时间步的平均值，最大值和最小值：
f1 <- function(x) c(Mean = (mean(x)), Max = (max(x)), Min = (min(x)))
results <- do.call(data.frame, aggregate(scarcityfactor ~ time,
data = df,
FUN = f1))
任何人都可以帮我代码？
library(tidyverse)
df %>%
group_by(run) %>%
mutate(scarcityfactor = 1 - discharge / lag(inflow,6)) %>%
group_by(time) %>%
summarise(Mean = mean(scarcityfactor),
Max = max(scarcityfactor),
Min = min(scarcityfactor))
# # A tibble: 24 x 4
# time Mean Max Min
# <dttm> <dbl> <dbl> <dbl>
# 1 2012-01-01 00:00:00 NA NA NA
# 2 2012-01-01 01:00:00 NA NA NA
# 3 2012-01-01 02:00:00 NA NA NA
# 4 2012-01-01 03:00:00 NA NA NA
# 5 2012-01-01 04:00:00 NA NA NA
# 6 2012-01-01 05:00:00 NA NA NA
# 7 2012-01-01 06:00:00 -46.7 -46.7 -46.7
# 8 2012-01-01 07:00:00 -2.96 -2.96 -2.96
# 9 2012-01-01 08:00:00 -1.34 -1.34 -1.34
#10 2012-01-01 09:00:00 -0.776 -0.776 -0.776
# # ... with 14 more rows
如果我正确理解问题描述，我相信这就是你想要的。
我会用data.table：
library(data.table)
setDT(df)
# add scarcity_factor (group by run)
df[ , scarcity_factor := 1 - discharge/shift(inflow, 6L), by = run]
# group by time, excluding times for which the
# scarcity factor is missing
df[!is.na(scarcity_factor), by = time,
.(min_scarcity = min(scarcity_factor),
mean_scarcity = mean(scarcity_factor),
max_scarcity = max(scarcity_factor))]
# time min_scarcity mean_scarcity max_scarcity
# 1: 2012-01-01 06:00:00 -46.695652174 -46.695652174 -46.695652174
# 2: 2012-01-01 07:00:00 -2.962732919 -2.962732919 -2.962732919
# 3: 2012-01-01 08:00:00 -1.342995169 -1.342995169 -1.342995169
# 4: 2012-01-01 09:00:00 -0.776086957 -0.776086957 -0.776086957
# 5: 2012-01-01 10:00:00 -0.487284660 -0.487284660 -0.487284660
# 6: 2012-01-01 11:00:00 -0.312252964 -0.312252964 -0.312252964
# 7: 2012-01-01 12:00:00 -0.194826637 -0.194826637 -0.194826637
# 8: 2012-01-01 13:00:00 -0.110586011 -0.110586011 -0.110586011
# 9: 2012-01-01 14:00:00 -0.047204969 -0.047204969 -0.047204969
# 10: 2012-01-01 15:00:00 0.002210759 0.002210759 0.002210759
# 11: 2012-01-01 16:00:00 0.041818785 0.041818785 0.041818785
# 12: 2012-01-01 17:00:00 0.074275362 0.074275362 0.074275362
# 13: 2012-01-01 18:00:00 0.101356965 0.101356965 0.101356965
# 14: 2012-01-01 19:00:00 0.124296675 0.124296675 0.124296675
# 15: 2012-01-01 20:00:00 0.143977192 0.143977192 0.143977192
# 16: 2012-01-01 21:00:00 0.161047028 0.161047028 0.161047028
# 17: 2012-01-01 22:00:00 0.175993343 0.175993343 0.175993343
# 18: 2012-01-01 23:00:00 0.189189189 0.189189189 0.189189189
你可以通过lapplying对不同的聚合器更简洁：
df[!is.na(scarcity_factor), by = time,
lapply(list(min, mean, max), function(f) f(scarcity_factor))]
最后，您可以将此视为使用聚合重塑并使用dcast：
dcast(df, time ~ ., value.var = 'scarcity_factor',
fun.aggregate = list(min, mean, max))
（如果要排除无意义的行，请在df[!is.na(scarcity_factor)]的第一个参数中使用dcast）