r语言 - 移动方差与聚合

Question

我有一些6分钟频率的热电偶数据。热电偶安装在不同的高度，在每个高度都有许多按径向位置区分的热电偶

DT_TI_RECORDED      HEIGHT      POS             TEMPERATURE
2018-05-16 00:00:00     1       90              111
2018-05-16 00:00:00     1       180             112
2018-05-16 00:00:00     1       270             113
2018-05-16 00:00:00     2       90              112
2018-05-16 00:00:00     2       180             114
2018-05-16 00:00:00     2       270             115
2018-05-16 00:00:00     3       90              112
2018-05-16 00:00:00     3       180             112
2018-05-16 00:00:00     3       270             113
...
2018-05-16 00:06:00     1       90              111
2018-05-16 00:06:00     1       180             112
2018-05-16 00:06:00     1       270             113
2018-05-16 00:06:00     2       90              112
2018-05-16 00:06:00     2       180             114
2018-05-16 00:06:00     2       270             112
2018-05-16 00:06:00     3       90              114
2018-05-16 00:06:00     3       180             112
2018-05-16 00:06:00     3       270             114
...

对于每个独特的高度和位置组合，每 6 分钟我想计算一个向后n小时移动方差，假设每小时 4 小时。

我尝试复制的原始代码是为 SAS 统计包编写

的

PROC EXPAND DATA=Raw_data
OUT=Moving_Variance
ALIGN = BEGINNING
;
by HEIGHT POS;
ID DT_TI_RECORDED ;
CONVERT TEMPERATURE = Moving_4hour_Var /  METHOD = none TRANSFORMOUT = (MOVVAR 40); 
#/* 40 obs at 6min freq = 4hour moving variance*/
QUIT;

我花了几个小时在谷歌上搜索，我认为我需要使用的 R 库叫做zoo我想要的功能rollapply但我无法弄清楚如何将聚合与rollapply相结合。

我试过了

moving_var <- Raw_data %>%
aggregate(HEIGHT,POS) %>%
rollapply( TEMPERATURE, width = 40, FUN = sd, fill = NA)

但不起作用。我对R编程很陌生，这让我发疯。

Answer 1

尝试以下聚合：

library(zoo)
result = aggregate(temp ~ pos + height,
data = df,
FUN = function(x){
rollapply(x, width = 40, FUN = var, by = 40)
}
)

width是滚动窗口的宽度，而by是下一个窗口起点跳过的点数。每个窗口中有 40 个，您将在前一个窗口的末尾旁边获得每个窗口的开始。

生成的数据框的每个窗口都有一列。这种结构可以被认为是"宽的"。如果要将其设置为"长"格式，请使用 tidyr 中的gather或 reshape2 中的melt。

例：

df = structure(list(pos = c(0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 
180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 
0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 
270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 
180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 
0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 
270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 
180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 
0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 
270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 
180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 0, 90, 180, 270, 
0, 90, 180, 270), height = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), temp = 1:160), .Names = c("pos", 
"height", "temp"), row.names = c(NA, -160L), class = "data.frame")
> head(df,20)
pos height temp
1    0      1    1
2   90      1    2
3  180      1    3
4  270      1    4
5    0      2    5
6   90      2    6
7  180      2    7
8  270      2    8
9    0      3    9
10  90      3   10
11 180      3   11
12 270      3   12
13   0      4   13
14  90      4   14
15 180      4   15
16 270      4   16
17   0      1   17
18  90      1   18
19 180      1   19
20 270      1   20

library(zoo)
result = aggregate(temp ~ pos + height,
data = df,
FUN = function(x){
rollapply(x, width = 3, FUN = var, by = 3)
}
)

将导致：

pos height temp.1 temp.2 temp.3
1    0      1    256    256    256
2   90      1    256    256    256
3  180      1    256    256    256
4  270      1    256    256    256
5    0      2    256    256    256
6   90      2    256    256    256
7  180      2    256    256    256
8  270      2    256    256    256
9    0      3    256    256    256
10  90      3    256    256    256
11 180      3    256    256    256
12 270      3    256    256    256
13   0      4    256    256    256
14  90      4    256    256    256
15 180      4    256    256    256
16 270      4    256    256    256