时间戳

首页 > 解决方案 > 如何在数据集中找到超过 6 个值的最大增量

r - 如何在数据集中找到超过 6 个值的最大增量

问题描述

我有一组核心温度值。在几个小时的持续时间内，我每 10 秒收集一个温度值。我试图在 6 个温度值内找到温度的最高增量（Tslope）。我尝试了滚动平均的方法，但我什至没有接近解决方案。数据集如下所示：

R001 <- R001_G2_Q3_T

Test Subject      Datum                Zeit Temperatur Timepoint
1     G2    R001 2018-10-27 1899-12-31 16:51:45       38.4        Q3
2     G2    R001 2018-10-27 1899-12-31 16:51:55       38.4        Q3
3     G2    R001 2018-10-27 1899-12-31 16:52:05       38.4        Q3
4     G2    R001 2018-10-27 1899-12-31 16:52:15       38.4        Q3
5     G2    R001 2018-10-27 1899-12-31 16:52:25       38.4        Q3
6     G2    R001 2018-10-27 1899-12-31 16:52:35       38.3        Q3
7     G2    R001 2018-10-27 1899-12-31 16:52:45       38.4        Q3
8     G2    R001 2018-10-27 1899-12-31 16:52:55       38.5        Q3
9     G2    R001 2018-10-27 1899-12-31 16:53:06       38.3        Q3
10    G2    R001 2018-10-27 1899-12-31 16:53:16       38.4        Q3
11    G2    R001 2018-10-27 1899-12-31 16:53:26       38.4        Q3
12    G2    R001 2018-10-27 1899-12-31 16:53:36       38.4        Q3
13    G2    R001 2018-10-27 1899-12-31 16:53:46       38.4        Q3
14    G2    R001 2018-10-27 1899-12-31 16:53:56       38.4        Q3
15    G2    R001 2018-10-27 1899-12-31 16:54:06       38.3        Q3
16    G2    R001 2018-10-27 1899-12-31 16:54:16       38.3        Q3
17    G2    R001 2018-10-27 1899-12-31 16:54:26       38.3        Q3
18    G2    R001 2018-10-27 1899-12-31 16:54:36       38.4        Q3
19    G2    R001 2018-10-27 1899-12-31 16:54:47       38.4        Q3
20    G2    R001 2018-10-27 1899-12-31 16:54:57       38.3        Q3
21    G2    R001 2018-10-27 1899-12-31 16:55:07       38.4        Q3
22    G2    R001 2018-10-27 1899-12-31 16:55:17       38.4        Q3
23    G2    R001 2018-10-27 1899-12-31 16:55:27       38.4        Q3
24    G2    R001 2018-10-27 1899-12-31 16:55:37       38.4        Q3
25    G2    R001 2018-10-27 1899-12-31 16:55:47       38.5        Q3
26    G2    R001 2018-10-27 1899-12-31 16:55:57       38.4        Q3
27    G2    R001 2018-10-27 1899-12-31 16:56:07       38.4        Q3
28    G2    R001 2018-10-27 1899-12-31 16:56:17       38.4        Q3
29    G2    R001 2018-10-27 1899-12-31 16:56:28       38.5        Q3
30    G2    R001 2018-10-27 1899-12-31 16:56:38       38.4        Q3
31    G2    R001 2018-10-27 1899-12-31 16:56:48       38.4        Q3
32    G2    R001 2018-10-27 1899-12-31 16:56:58       38.5        Q3
33    G2    R001 2018-10-27 1899-12-31 16:57:08       38.5        Q3
[...]

我期望的是，我可以说例如：“在 16:55 和 16:56 之间，核心温度升高了 0.3°C”

当我向您另外展示一张图片时，也许更容易理解我的问题： 在此处输入图像描述

我喜欢找到的是 Tslope：Tslope 将在一分钟内迭代计算，包括 6 个连续的测量时间点。当然，我的数据集包含更多的值，会有类似图像中的曲线。

如果有人能在这种情况下帮助我，那就太好了。谢谢你的帮助。

标签： rmoving-averagehighest

解决方案

这是使用data.table.

样本数据

library( data.table )

DT <- fread("ID Test Subject      Datum                Zeit Temperatur Timepoint
1     G2    R001 2018-10-27 '1899-12-31 16:51:45'       38.4        Q3
2     G2    R001 2018-10-27 '1899-12-31 16:51:55'       38.4        Q3
3     G2    R001 2018-10-27 '1899-12-31 16:52:05'       38.4        Q3
4     G2    R001 2018-10-27 '1899-12-31 16:52:15'       38.4        Q3
5     G2    R001 2018-10-27 '1899-12-31 16:52:25'       38.4        Q3
6     G2    R001 2018-10-27 '1899-12-31 16:52:35'       38.3        Q3
7     G2    R001 2018-10-27 '1899-12-31 16:52:45'       38.4        Q3
8     G2    R001 2018-10-27 '1899-12-31 16:52:55'       38.5        Q3
9     G2    R001 2018-10-27 '1899-12-31 16:53:06'       38.3        Q3
10    G2    R001 2018-10-27 '1899-12-31 16:53:16'       38.4        Q3
11    G2    R001 2018-10-27 '1899-12-31 16:53:26'       38.4        Q3
12    G2    R001 2018-10-27 '1899-12-31 16:53:36'       38.4        Q3
13    G2    R001 2018-10-27 '1899-12-31 16:53:46'       38.4        Q3
14    G2    R001 2018-10-27 '1899-12-31 16:53:56'       38.4        Q3
15    G2    R001 2018-10-27 '1899-12-31 16:54:06'       38.3        Q3
16    G2    R001 2018-10-27 '1899-12-31 16:54:16'       38.3        Q3
17    G2    R001 2018-10-27 '1899-12-31 16:54:26'       38.3        Q3
18    G2    R001 2018-10-27 '1899-12-31 16:54:36'       38.4        Q3
19    G2    R001 2018-10-27 '1899-12-31 16:54:47'       38.4        Q3
20    G2    R001 2018-10-27 '1899-12-31 16:54:57'       38.3        Q3
21    G2    R001 2018-10-27 '1899-12-31 16:55:07'       38.4        Q3
22    G2    R001 2018-10-27 '1899-12-31 16:55:17'       38.4        Q3
23    G2    R001 2018-10-27 '1899-12-31 16:55:27'       38.4        Q3
24    G2    R001 2018-10-27 '1899-12-31 16:55:37'       38.4        Q3
25    G2    R001 2018-10-27 '1899-12-31 16:55:47'       38.5        Q3
26    G2    R001 2018-10-27 '1899-12-31 16:55:57'       38.4        Q3
27    G2    R001 2018-10-27 '1899-12-31 16:56:07'       38.4        Q3
28    G2    R001 2018-10-27 '1899-12-31 16:56:17'       38.4        Q3
29    G2    R001 2018-10-27 '1899-12-31 16:56:28'       38.5        Q3
30    G2    R001 2018-10-27 '1899-12-31 16:56:38'       38.4        Q3
31    G2    R001 2018-10-27 '1899-12-31 16:56:48'       38.4        Q3
32    G2    R001 2018-10-27 '1899-12-31 16:56:58'       38.5        Q3
33    G2    R001 2018-10-27 '1899-12-31 16:57:08'       38.5        Q3", quote = "'")

代码

#extract time from Zeit
DT[, Zeit := gsub(".* (.*$)", "\\1", Zeit)]
#create posixct timestamp
DT[, timestamp := as.POSIXct( paste0( Datum, "T", Zeit ), format = "%Y-%m-%dT%H:%M:%S", tz = "GMT" ) ]
#create a minute-colum to summarise on
DT[, time_summ := format( timestamp, "%Y-%m-%dT%H:%M" ) ]
#calculate delta
DT[, deltaT := Temperatur - shift( Temperatur, type = "lag" )]
#summarise, het the maximum deltaT by minute
DT[, .(max_deltaT = max( deltaT, na.rm = TRUE ) ), by = .(period = time_summ ) ]

输出

#              period max_deltaT
# 1: 2018-10-27T16:51        0.0
# 2: 2018-10-27T16:52        0.1
# 3: 2018-10-27T16:53        0.1
# 4: 2018-10-27T16:54        0.1
# 5: 2018-10-27T16:55        0.1
# 6: 2018-10-27T16:56        0.1
# 7: 2018-10-27T16:57        0.0

推荐阅读