在数据框上每年移动 window
Annual moving window over a data frame
我有一个放电数据的数据框。下面是一个可重现的例子：
library(lubridate)
Date <- sample(seq(as.Date('1981/01/01'), as.Date('1982/12/31'), by="day"), 24)
Date <- sort(Date, decreasing = F)
Station <- rep(as.character("A"), 24)
Discharge <- rnorm(n = 24, mean = 1, 1)
df <- cbind.data.frame(Station, Date, Discharge)
df$Year <- year(df$Date)
df$Month <- month(df$Date)
df$Day <- day(df$Date)
输出：
> df
Station Date Discharge Year Month Day
1 A 1981-01-23 0.75514968 1981 1 23
2 A 1981-02-17 -0.08552776 1981 2 17
3 A 1981-03-20 1.47586712 1981 3 20
4 A 1981-04-26 3.64823544 1981 4 26
5 A 1981-05-22 1.21880453 1981 5 22
6 A 1981-05-23 2.19482857 1981 5 23
7 A 1981-07-02 -0.13598754 1981 7 2
8 A 1981-07-23 0.12365626 1981 7 23
9 A 1981-07-24 2.12557882 1981 7 24
10 A 1981-09-02 2.79879494 1981 9 2
11 A 1981-09-04 1.67926948 1981 9 4
12 A 1981-11-06 0.49720784 1981 11 6
13 A 1981-12-21 -0.25272271 1981 12 21
14 A 1982-04-08 1.39706157 1982 4 8
15 A 1982-04-19 -0.13965981 1982 4 19
16 A 1982-05-26 0.55238425 1982 5 26
17 A 1982-06-23 3.94639154 1982 6 23
18 A 1982-06-25 -0.03415929 1982 6 25
19 A 1982-07-15 1.00996167 1982 7 15
20 A 1982-09-11 3.18225186 1982 9 11
21 A 1982-10-17 0.30875497 1982 10 17
22 A 1982-10-30 2.26209011 1982 10 30
23 A 1982-11-06 0.34430489 1982 11 6
24 A 1982-11-19 2.28251458 1982 11 19
我需要做的是使用基础 R 创建一个移动 window 函数。我尝试使用 runner 包，但事实证明它不是那么灵活。此移动 window（比如 3）应一次取 3 行并计算 mean 排放量。此 window 将持续到 1981 年的最后一天。另一个 window 将从 1982 年开始并执行相同的操作。如何解决这个问题？
您可以使用 dplyr 和 zoo 中的 rollmean 或 rollmeanr 函数来完成此操作。
您按年份对数据进行分组，并在 mutate 函数中应用 rollmeanr。
图书馆（dplyr）
df %>%
group_by(Year) %>%
mutate(avg = zoo::rollmeanr(Discharge, k = 3, fill = NA))
# A tibble: 24 x 7
# Groups: Year [2]
Station Date Discharge Year Month Day avg
<chr> <date> <dbl> <dbl> <dbl> <int> <dbl>
1 A 1981-01-04 1.00 1981 1 4 NA
2 A 1981-03-26 0.0468 1981 3 26 NA
3 A 1981-03-28 0.431 1981 3 28 0.494
4 A 1981-05-04 1.30 1981 5 4 0.593
5 A 1981-08-26 2.06 1981 8 26 1.26
6 A 1981-10-14 1.09 1981 10 14 1.48
7 A 1981-12-10 1.28 1981 12 10 1.48
8 A 1981-12-23 0.668 1981 12 23 1.01
9 A 1982-01-02 -0.333 1982 1 2 NA
10 A 1982-04-13 0.800 1982 4 13 NA
# ... with 14 more rows
请告诉我这是否是您所期待的
基本版本：
result <- transform(df,
Discharge_mean = ave(Discharge,Year,
FUN= function(x) rollapply(x,width = 3, mean, align='right',fill=NA))
)
dplyr 版本：
result <-df %>%
group_by(Year)%>%
mutate(Discharge_mean=rollapply(Discharge,3,mean,align='right',fill=NA))
输出：
> result
Station Date Discharge Year Month Day Discharge_mean
1 A 1981-01-09 0.560448487 1981 1 9 NA
2 A 1981-01-17 0.006777809 1981 1 17 NA
3 A 1981-02-08 2.008959399 1981 2 8 0.8587286
4 A 1981-02-21 1.166452993 1981 2 21 1.0607301
5 A 1981-04-12 3.120080595 1981 4 12 2.0984977
6 A 1981-04-24 2.647325960 1981 4 24 2.3112865
7 A 1981-05-01 0.764980310 1981 5 1 2.1774623
8 A 1981-05-20 2.203700845 1981 5 20 1.8720024
9 A 1981-06-19 0.519390897 1981 6 19 1.1626907
10 A 1981-07-06 1.704146872 1981 7 6 1.4757462
# 14 more rows
仅使用基础 R
w=3
df$DischargeM=sapply(1:nrow(df),function(x){
tmp=NA
if (x>=w) {
if (length(unique(df$Year[(x-w+1):x]))==1) {
tmp=mean(df$Discharge[(x-w+1):x])
}
}
tmp
})
Station Date Discharge Year Month Day DischargeM
1 A 1981-01-21 2.0009355 1981 1 21 NA
2 A 1981-02-11 0.5948567 1981 2 11 NA
3 A 1981-04-17 0.2637090 1981 4 17 0.95316705
4 A 1981-04-18 3.9180253 1981 4 18 1.59219699
5 A 1981-05-09 -0.2589129 1981 5 9 1.30760712
6 A 1981-07-05 1.1055913 1981 7 5 1.58823456
7 A 1981-07-11 0.7561600 1981 7 11 0.53427946
8 A 1981-07-22 0.0978999 1981 7 22 0.65321706
9 A 1981-08-04 0.5410163 1981 8 4 0.46502541
10 A 1981-08-13 -0.5044425 1981 8 13 0.04482458
11 A 1981-10-06 1.5954315 1981 10 6 0.54400178
12 A 1981-11-08 -0.5757041 1981 11 8 0.17176164
13 A 1981-12-24 1.3892440 1981 12 24 0.80299047
14 A 1982-01-07 1.9363874 1982 1 7 NA
15 A 1982-02-20 1.4340554 1982 2 20 NA
16 A 1982-05-29 0.4536461 1982 5 29 1.27469632
17 A 1982-06-10 2.9776761 1982 6 10 1.62179253
18 A 1982-06-17 1.6371733 1982 6 17 1.68949847
19 A 1982-06-28 1.7585579 1982 6 28 2.12446908
20 A 1982-08-17 0.8297518 1982 8 17 1.40849432
21 A 1982-09-21 1.6853808 1982 9 21 1.42456348
22 A 1982-11-13 0.6066167 1982 11 13 1.04058309
23 A 1982-11-16 1.4989263 1982 11 16 1.26364126
24 A 1982-11-28 0.2273658 1982 11 28 0.77763625
（确保您的 df 已订购）。
我有一个放电数据的数据框。下面是一个可重现的例子：
library(lubridate)
Date <- sample(seq(as.Date('1981/01/01'), as.Date('1982/12/31'), by="day"), 24)
Date <- sort(Date, decreasing = F)
Station <- rep(as.character("A"), 24)
Discharge <- rnorm(n = 24, mean = 1, 1)
df <- cbind.data.frame(Station, Date, Discharge)
df$Year <- year(df$Date)
df$Month <- month(df$Date)
df$Day <- day(df$Date)
输出：
> df
Station Date Discharge Year Month Day
1 A 1981-01-23 0.75514968 1981 1 23
2 A 1981-02-17 -0.08552776 1981 2 17
3 A 1981-03-20 1.47586712 1981 3 20
4 A 1981-04-26 3.64823544 1981 4 26
5 A 1981-05-22 1.21880453 1981 5 22
6 A 1981-05-23 2.19482857 1981 5 23
7 A 1981-07-02 -0.13598754 1981 7 2
8 A 1981-07-23 0.12365626 1981 7 23
9 A 1981-07-24 2.12557882 1981 7 24
10 A 1981-09-02 2.79879494 1981 9 2
11 A 1981-09-04 1.67926948 1981 9 4
12 A 1981-11-06 0.49720784 1981 11 6
13 A 1981-12-21 -0.25272271 1981 12 21
14 A 1982-04-08 1.39706157 1982 4 8
15 A 1982-04-19 -0.13965981 1982 4 19
16 A 1982-05-26 0.55238425 1982 5 26
17 A 1982-06-23 3.94639154 1982 6 23
18 A 1982-06-25 -0.03415929 1982 6 25
19 A 1982-07-15 1.00996167 1982 7 15
20 A 1982-09-11 3.18225186 1982 9 11
21 A 1982-10-17 0.30875497 1982 10 17
22 A 1982-10-30 2.26209011 1982 10 30
23 A 1982-11-06 0.34430489 1982 11 6
24 A 1982-11-19 2.28251458 1982 11 19
我需要做的是使用基础 R 创建一个移动 window 函数。我尝试使用 runner 包，但事实证明它不是那么灵活。此移动 window（比如 3）应一次取 3 行并计算 mean 排放量。此 window 将持续到 1981 年的最后一天。另一个 window 将从 1982 年开始并执行相同的操作。如何解决这个问题？
您可以使用 dplyr 和 zoo 中的 rollmean 或 rollmeanr 函数来完成此操作。
您按年份对数据进行分组，并在 mutate 函数中应用 rollmeanr。
图书馆（dplyr）
df %>%
group_by(Year) %>%
mutate(avg = zoo::rollmeanr(Discharge, k = 3, fill = NA))
# A tibble: 24 x 7
# Groups: Year [2]
Station Date Discharge Year Month Day avg
<chr> <date> <dbl> <dbl> <dbl> <int> <dbl>
1 A 1981-01-04 1.00 1981 1 4 NA
2 A 1981-03-26 0.0468 1981 3 26 NA
3 A 1981-03-28 0.431 1981 3 28 0.494
4 A 1981-05-04 1.30 1981 5 4 0.593
5 A 1981-08-26 2.06 1981 8 26 1.26
6 A 1981-10-14 1.09 1981 10 14 1.48
7 A 1981-12-10 1.28 1981 12 10 1.48
8 A 1981-12-23 0.668 1981 12 23 1.01
9 A 1982-01-02 -0.333 1982 1 2 NA
10 A 1982-04-13 0.800 1982 4 13 NA
# ... with 14 more rows
请告诉我这是否是您所期待的
基本版本：
result <- transform(df,
Discharge_mean = ave(Discharge,Year,
FUN= function(x) rollapply(x,width = 3, mean, align='right',fill=NA))
)
dplyr 版本：
result <-df %>%
group_by(Year)%>%
mutate(Discharge_mean=rollapply(Discharge,3,mean,align='right',fill=NA))
输出：
> result
Station Date Discharge Year Month Day Discharge_mean
1 A 1981-01-09 0.560448487 1981 1 9 NA
2 A 1981-01-17 0.006777809 1981 1 17 NA
3 A 1981-02-08 2.008959399 1981 2 8 0.8587286
4 A 1981-02-21 1.166452993 1981 2 21 1.0607301
5 A 1981-04-12 3.120080595 1981 4 12 2.0984977
6 A 1981-04-24 2.647325960 1981 4 24 2.3112865
7 A 1981-05-01 0.764980310 1981 5 1 2.1774623
8 A 1981-05-20 2.203700845 1981 5 20 1.8720024
9 A 1981-06-19 0.519390897 1981 6 19 1.1626907
10 A 1981-07-06 1.704146872 1981 7 6 1.4757462
# 14 more rows
仅使用基础 R
w=3
df$DischargeM=sapply(1:nrow(df),function(x){
tmp=NA
if (x>=w) {
if (length(unique(df$Year[(x-w+1):x]))==1) {
tmp=mean(df$Discharge[(x-w+1):x])
}
}
tmp
})
Station Date Discharge Year Month Day DischargeM
1 A 1981-01-21 2.0009355 1981 1 21 NA
2 A 1981-02-11 0.5948567 1981 2 11 NA
3 A 1981-04-17 0.2637090 1981 4 17 0.95316705
4 A 1981-04-18 3.9180253 1981 4 18 1.59219699
5 A 1981-05-09 -0.2589129 1981 5 9 1.30760712
6 A 1981-07-05 1.1055913 1981 7 5 1.58823456
7 A 1981-07-11 0.7561600 1981 7 11 0.53427946
8 A 1981-07-22 0.0978999 1981 7 22 0.65321706
9 A 1981-08-04 0.5410163 1981 8 4 0.46502541
10 A 1981-08-13 -0.5044425 1981 8 13 0.04482458
11 A 1981-10-06 1.5954315 1981 10 6 0.54400178
12 A 1981-11-08 -0.5757041 1981 11 8 0.17176164
13 A 1981-12-24 1.3892440 1981 12 24 0.80299047
14 A 1982-01-07 1.9363874 1982 1 7 NA
15 A 1982-02-20 1.4340554 1982 2 20 NA
16 A 1982-05-29 0.4536461 1982 5 29 1.27469632
17 A 1982-06-10 2.9776761 1982 6 10 1.62179253
18 A 1982-06-17 1.6371733 1982 6 17 1.68949847
19 A 1982-06-28 1.7585579 1982 6 28 2.12446908
20 A 1982-08-17 0.8297518 1982 8 17 1.40849432
21 A 1982-09-21 1.6853808 1982 9 21 1.42456348
22 A 1982-11-13 0.6066167 1982 11 13 1.04058309
23 A 1982-11-16 1.4989263 1982 11 16 1.26364126
24 A 1982-11-28 0.2273658 1982 11 28 0.77763625
（确保您的 df 已订购）。