这是我在这里遇到的第一个问题（How to aggregate hourly values into 24h-average means without timestamp）。 现在，我想根据每个12小时间隔的时间序列来计算最大值（和最小值）。
我每小时都有一次数据测量（data_measure）。现在，我将其更改为半天的时间序列。
t_measure <- ts(data = data_measure, frequency = 12)
然后我使用了{stats}的聚合函数
data_measure_daily_max <- aggregate(t_measure, 1, max)
data_measure <- structure(c(8.29, 7.96, 8.14, 7.27, 7.37, 7.3, 7.23, 7.53,
7.98, 10.2, 12.39, 14.34, 14.87, 14.39, 12.54, 11.84, 10.3, 10.62,
10.65, 10.56, 10.43, 10.35, 9.85, 9.12, 8.95, 8.82, 8.92, 9.33,
9.44, 9.3, 9.15, 9.37, 9.54, 10.24, 12.13, 12.43, 12.65, 13,
13.18, 13.58, 13.64, 13.75, 13.85, 13.94, 13.79, 13.84, 13.94,
14.26, 24.93, 24.64, 23.67, 21.46, 21.33, 20.83, 21.12, 21.1,
23.75, 25.39, 30.72, 30.71, 30.81, 30.92, 32.61, 32.37, 32.49,
30.68, 30.23, 30.45, 28.1, 26.9, 25.09, 25.07, 24.59, 24.22,
23.05, 22.21, 22.07, 21.6, 21.24, 21.22, 21.85, 24.87, 28.85,
29.42, 30.82, 30.97, 31.32, 30.81, 30.83, 29.9, 30.01, 30.31,
30, 27.91, 25.78, 25.88, 8.78, 8.47, 8.49, 7.65, 8.63, 9.02,
9.02, 8.11, 7.63, 9.19, 11.25, 12.24, 13.62, 12.09, 10.6, 11.1,
10.16, 10.44, 9.58, 10.04, 10.01, 10.23, 9.51, 9.2, 9.34, 9.6,
9.4, 9.45, 9.36, 9.26, 9.3, 9.46, 9.58, 9.89, 10.6, 11.04, 12.1,
12.61, 13.12, 13.47, 13.55, 13.51, 13.63, 13.84, 13.93, 14.17,
13.97, 13.86), .Dim = c(48L, 3L), .Dimnames = list(NULL, c("station1",
"station2", "station3")))
所以实际上我需要一个索引/向量来告诉我这些时间间隔的最大值和最小值在哪里，因此稍后我可以准确地提取这些值以用于其他数据集进行比较。
我的第一次审判：
max_index <- which(aggregate(t_measure, 1, max)) # argument to 'which' is not logical
答案 0 :(得分：3)
将which.max和which.min与aggregate一起使用
a1 <- aggregate(t_measure, 1, which.min)
a2 <- aggregate(t_measure, 1, which.max)
a1
#Time Series:
#Start = 1
#End = 4
#Frequency = 1
# station1 station2 station3
#1 7 6 9
#2 12 12 12
#3 2 8 6
#4 1 11 1
a2
#Time Series:
#Start = 1
#End = 4
#Frequency = 1
# station1 station2 station3
#1 12 11 12
#2 1 3 1
#3 12 12 12
#4 12 3 10
如果您想参考原始data_measure数据帧为分钟建立索引，我们可以
vals <- nrow(t_measure)/12
index_min <- a1 + (12 * (seq_len(vals) - 1))
index_min
#Time Series:
#Start = 1
#End = 4
#Frequency = 1
# station1 station2 station3
#1 7 6 9
#2 24 24 24
#3 26 32 30
#4 37 47 37
在station1的第1行的第7行中，可以看到data_measure的最大值，在接下来的12个小时的间隔中，在$(document).ready(function() {
$('#example').DataTable();
} );
$( "#but" ).click(function() {
var table = $('#example').DataTable();
var info = table.page();
console.log(info);
table.page("first").draw("page");
var allData = table.rows().data();
var proof = allData.$('input').serialize();
table.page(info).draw("page");
console.log(proof);
});
的第7行中存在最大值，其他电台也是如此。