如何解决汇总时间序列数据，获得平均值，R中不包含NA或0
我正在尝试汇总我的时间序列数据，我想获得汇总值的平均值，而不包括NA或0。
这是我的数据。
V1 423 470 473 626
1: 2018-01-01 00:00:00 0.00000 0 0.0 0
2: 2018-01-01 00:01:00 8.00000 0 95.0 0
3: 2018-01-01 00:02:00 0.00000 0 0.0 0
4: 2018-01-01 00:03:00 31.00000 0 24.5 0
5: 2018-01-01 00:04:00 37.00000 28 33.0 31
我正尝试每隔5分钟汇总一次， 我的预期输出是
V1 423 470 473 626
2018-01-01 00:05:00 34.00000 28 50.8 31
~
: 2018-01-01 00:10:00 A B C D
如何在5分钟间隔内汇总它们，同时获得平均值不包括0 s或NA ？
编辑
structure(list(V1 = c("2018-01-01 00:00:00","2018-01-01 00:01:00","2018-01-01 00:02:00","2018-01-01 00:03:00","2018-01-01 00:04:00","2018-01-01 00:05:00","2018-01-01 00:06:00","2018-01-01 00:07:00","2018-01-01 00:08:00","2018-01-01 00:09:00","2018-01-01 00:10:00","2018-01-01 00:11:00","2018-01-01 00:12:00","2018-01-01 00:13:00","2018-01-01 00:14:00","2018-01-01 00:15:00","2018-01-01 00:16:00","2018-01-01 00:17:00","2018-01-01 00:18:00","2018-01-01 00:19:00"
),`423` = c(0,8,31,37,26.1111111111111,39.375,35.5,19.3,21.5454545454545,41.2,27.375,24.3076923076923,26.1666666666667,24,26.8,30.8181818181818),`470` = c(0,28,27,21,21.5,10,46,19.5,0),`473` = c(0,95,24.5,33,55,50,47,45,35.4,23,32.5,55),`626` = c(0,26,16,75,48,0)),row.names = c(NA,-20L
),.internal.selfref = <pointer: 0x0000029131ff1ef0>,class = c("data.table","data.frame"))
解决方法
这项工作：
> df %>% mutate(ID = rep(letters[1:ceiling(nrow(df)/5)],each = 5)) %>%
+ group_by(as.numeric(as.factor(ID))) %>%
+ select(-c(v1,ID)) %>% summarise(across(`423`:`625`,~ mean(.x[which(.x>0)]))) %>%
+ select(-1) %>% mutate(v1 = seq.POSIXt(ymd_hms('2018-01-01 00:05:00'),by = '5 mins',length.out = n()))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 2 x 5
`423` `470` `473` `625` v1
<dbl> <dbl> <dbl> <dbl> <dttm>
1 25.3 28 50.8 31 2018-01-01 00:05:00
2 20.7 26 49.7 32 2018-01-01 00:10:00
>
使用的数据：
> df
# A tibble: 10 x 5
v1 `423` `470` `473` `625`
<dttm> <dbl> <dbl> <dbl> <dbl>
1 2018-01-01 00:00:00 0 0 0 0
2 2018-01-01 00:01:00 8 0 95 0
3 2018-01-01 00:02:00 0 0 0 0
4 2018-01-01 00:03:00 31 0 24.5 0
5 2018-01-01 00:04:00 37 28 33 31
6 2018-01-01 00:05:00 0 0 0 0
7 2018-01-01 00:06:00 8 0 95 0
8 2018-01-01 00:07:00 0 0 0 0
9 2018-01-01 00:08:00 30 0 20 0
10 2018-01-01 00:09:00 24 26 34 32
>
运行新数据：
> dput(BB)
structure(list(V1 = structure(c(1514764800,1514764860,1514764920,1514764980,1514765040,1514765100,1514765160,1514765220,1514765280,1514765340,1514764800,1514765340),class = c("POSIXct","POSIXt"),tzone = "UTC"),`423` = c(0,8,31,37,26.1111111111111,39.375,35.5,19.3,21.5454545454545,41.2,27.375,24.3076923076923,26.1666666666667,24,26.8,30.8181818181818),`470` = c(0,28,27,21,21.5,10,46,19.5,0),`473` = c(0,95,24.5,33,55,50,47,45,35.4,23,32.5,55),`626` = c(0,26,16,75,48,0)),row.names = c(NA,-20L
),class = c("data.table","data.frame"))
> BB$V1 <- ymd_hms(BB$V1)
> BB %>% mutate(ID = rep(letters[1:ceiling(nrow(BB)/5)],each = 5)) %>%
+ group_by(as.numeric(as.factor(ID))) %>%
+ select(-c(V1,ID)) %>% summarise(across(`423`:`626`,~ mean(.x[which(.x>0)]))) %>%
+ select(-1) %>% mutate(V1 = seq.POSIXt(ymd_hms('2018-01-01 00:05:00'),length.out = n()))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 4 x 5
`423` `470` `473` `626` V1
<dbl> <dbl> <dbl> <dbl> <dttm>
1 25.3 28 50.8 31 2018-01-01 00:05:00
2 30.3 24 49.2 21 2018-01-01 00:10:00
3 30.4 21.5 35.4 75 2018-01-01 00:15:00
4 26.4 25.2 36.8 48 2018-01-01 00:20:00
>
以下内容使用cut将V1列按5分钟间隔组成一个分组变量，然后使用自定义函数进行汇总，以计算没有NA或零值的均值。我在两个代码行中都保留了此功能，以使其更具可读性，但它可能只是
f <- function(x) mean(x[x != 0],na.rm = TRUE)
日期时间列V1首先被强制为类"POSIXct"。
library(data.table)
f <- function(x){
y <- x[x != 0]
mean(y,na.rm = TRUE)
}
df[,V1 := as.POSIXct(V1)]
df[,V1 := cut(V1,"5 mins")]
df[,lapply(.SD,f),by = V1]
# V1 423 470 473 626
#1: 2018-01-01 00:00:00 25.33333 28.00000 50.83333 31
#2: 2018-01-01 00:05:00 30.25722 24.00000 49.25000 21
#3: 2018-01-01 00:10:00 30.42409 21.50000 35.40000 75
#4: 2018-01-01 00:15:00 26.41851 25.16667 36.83333 48
单线可能是
df[,by = cut(as.POSIXct(V1),"5 mins")]
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