英文:
Replacing unusual values in timeseries data with moving average values
问题
我想要将异常降雨替换为合理的数值。
我希望使用2-3周的降雨移动平均来替代它们。
library(tidyverse)
library(lubridate)
week <- c("10-03-2021", "10-10-2021", "10-17-2021", "10-24-2021", "10-31-2021", "11-6-2021", "11-13-2021", "11-20-2021", "11-27-2021")
rainfall <- c(11, 12, 13, 1010, 1000, 16, 16, 18, 15)
tbl <- tibble(week = mdy(week),
rainfall = rainfall)
如果您需要进一步的翻译或解释，请告诉我。
英文:
I would like to replace unusual rainfall with reasonable values.
I wish to use moving average of 2-3 week's of rainfall to replace them.
library(tidyverse)
library(lubridate)
week <- c("10-03-2021", "10-10-2021", "10-17-2021", "10-24-2021", "10-31-2021", "11-6-2021", "11-13-2021", "11-20-2021", "11-27-2021")
rainfall <- c(11, 12, 13, 1010, 1000, 16, 16, 18, 15)
tbl <- tibble(week = mdy(week),
rainfall = rainfall)
答案1
得分: 1
You can use dplyr in combination with the zoo package.
使用 `dplyr` 与 `zoo` 包结合。
library(dplyr)
library(zoo)
tbl <- tbl %>% mutate(rainfall.MA = rollmean(rainfall, 3, fill = NA, align = 'right'))
If you only wish to replace the high values (say, 1000 or more):
如果您只想替换高值（例如，1000或更多）：
highvalues <- which(tbl$rainfall >= 1000 )
tbl$rainfall[ highvalues ] <- tbl$rainfall.MA[ highvalues ]
If your aim is to deal with outliers, you might look into logarithmic transformation, such as
如果您的目标是处理异常值，您可以考虑对数变换，例如：
tbl$ln_rainfall <- log(tbl$rainfall)
英文:
You can use dplyr in combination with the zoo package.
library(dplyr)
library(zoo)
tbl <- tbl %>% mutate(rainfall.MA = rollmean(rainfall, 3, fill = NA, align = 'right'))
If you only wish to replace the high values (say, 1000 or more):
highvalues <- which(tbl$rainfall >= 1000 )
tbl$rainfall[ highvalues ] <- tbl$rainfall.MA[ highvalues ]
If your aim is to deal with outliers, you might look into logaritmic transformation, such as
tbl$ln_rainfall <- log(tbl$rainfall)
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