计算截至特定日期的每个唯一值的平均值
Calculate Mean for Each Unique Value up to a certain date
我的例子的数据。
date1 = seq(as.Date("2019/01/01"), by = "month", length.out = 48)
date2 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date3 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date4 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date = c(date1,date2,date3,date4)
subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)
b1 <- c(rnorm(48,5))
b2 <- c(rnorm(48,5))
b3 <-c(rnorm(48,5) )
b4 <- c(rnorm(48,5))
dfone <- data.frame(
"date"= date,
"subproduct"=
c(subproducts1,subproducts2,subproductsx,subproductsy),
"actuals"= c(b1,b2,b3,b4))
这会为日期 2、3、4 创建值为 0 的 2019 年 1 月。
dfone <-dfone %>%
complete(date = seq.Date(from = min(date), to = as.Date('2021-06-01'), by = 'month'),
nesting(subproduct), fill = list(actuals = 0))
问题:这会计算每个独特子产品的均值,并将 0 替换为每个子产品的均值,但我如何设置硬截止值,以便均值仅基于 2019 年 1 月至 2020 年 12 月而不是 1 月2019 年到 2022 年 12 月?
library(dplyr)
dfone_new <- dfone %>%
group_by(subproduct) %>%
mutate(actuals = replace(actuals, actuals == 0,
mean(actuals[actuals != 0], na.rm = TRUE))) %>%
ungroup
在对 'actuals' 进行子集化时,我们可能还需要一个逻辑表达式,即在计算 mean
[= 时,'date' 应该是 between
2019 年一月和 2020 年十二月13=]
library(dplyr)
library(tidyr)
dfone %>%
group_by(subproduct) %>%
mutate(actuals = replace(actuals, actuals == 0,
mean(actuals[actuals != 0 &
between(date, as.Date("2019-01-01"), as.Date("2020-12-31"))],
na.rm = TRUE)))
我的例子的数据。
date1 = seq(as.Date("2019/01/01"), by = "month", length.out = 48)
date2 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date3 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date4 = seq(as.Date("2019/02/01"), by = "month", length.out = 48)
date = c(date1,date2,date3,date4)
subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)
b1 <- c(rnorm(48,5))
b2 <- c(rnorm(48,5))
b3 <-c(rnorm(48,5) )
b4 <- c(rnorm(48,5))
dfone <- data.frame(
"date"= date,
"subproduct"=
c(subproducts1,subproducts2,subproductsx,subproductsy),
"actuals"= c(b1,b2,b3,b4))
这会为日期 2、3、4 创建值为 0 的 2019 年 1 月。
dfone <-dfone %>%
complete(date = seq.Date(from = min(date), to = as.Date('2021-06-01'), by = 'month'),
nesting(subproduct), fill = list(actuals = 0))
问题:这会计算每个独特子产品的均值,并将 0 替换为每个子产品的均值,但我如何设置硬截止值,以便均值仅基于 2019 年 1 月至 2020 年 12 月而不是 1 月2019 年到 2022 年 12 月?
library(dplyr)
dfone_new <- dfone %>%
group_by(subproduct) %>%
mutate(actuals = replace(actuals, actuals == 0,
mean(actuals[actuals != 0], na.rm = TRUE))) %>%
ungroup
在对 'actuals' 进行子集化时,我们可能还需要一个逻辑表达式,即在计算 mean
[= 时,'date' 应该是 between
2019 年一月和 2020 年十二月13=]
library(dplyr)
library(tidyr)
dfone %>%
group_by(subproduct) %>%
mutate(actuals = replace(actuals, actuals == 0,
mean(actuals[actuals != 0 &
between(date, as.Date("2019-01-01"), as.Date("2020-12-31"))],
na.rm = TRUE)))