计算R中特定日期之前的死亡率累计
Calculating cumsum of mortality before a specific date in R
我需要帮助为两件事编写 for 循环或 dply 代码：
- 计算治疗前规定时期（7 天）内按单位分组的死亡率累积总和 (%)。
- 制作一个向量，计算上次治疗到下一次治疗的天数post。
数据集如下所示：
Unit Date Prcent_daily.mortality Date.treatment
A 20.07.2020 0.2 NA
A 21.07.2020 0 NA
A 22.07.2020 0.4 NA
A 23.07.2020 0.3 NA
A 24.07.2020 0.6 NA
A 25.07.2020 0.05 NA
A 26.07.2020 0 NA
A 27.07.2020 0 NA
A 28.07.2020 0.01 28.07.2020
A 29.07.2020 0.1 NA
A 30.07.2020 0.2 NA
A 31.07.2020 0 NA
A 01.08.2020 0.2 NA
A 02.08.2020 0.3 NA
A 03.08.2020 0.3 NA
A 04.08.2020 0.05 NA
A 05.08.2020 0 NA
A 06.08.2020 0 NA
A 07.08.2020 0.01 05.08.2020
A 08.08.2020 0.1 NA
A 09.08.2020 0.2 NA
我想实现这个：
Unit Date Prcent_daily.mortality Date.treatment akkum.7dbt days.post.treatment
A 20.07.2020 0.2 NA NA NA
A 21.07.2020 0 NA 1.35 NA
A 22.07.2020 0.4 NA 1.35 NA
A 23.07.2020 0.3 NA 1.35 NA
A 24.07.2020 0.6 NA 1.35 NA
A 25.07.2020 0.05 NA 1.35 NA
A 26.07.2020 0 NA 1.35 NA
A 27.07.2020 0 NA 1.35 NA
A 28.07.2020 0.01 28.07.2020 1.35 0
A 29.07.2020 0.1 NA NA 1
A 30.07.2020 0.2 NA NA 2
A 31.07.2020 0 NA 0.85 3
A 01.08.2020 0.2 NA 0.85 4
A 02.08.2020 0.3 NA 0.85 5
A 03.08.2020 0.3 NA 0.85 6
A 04.08.2020 0.05 NA 0.85 7
A 05.08.2020 0 NA 0.85 8
A 06.08.2020 0 NA 0.85 9
A 07.08.2020 0.01 05.08.2020 0.85 0
A 08.08.2020 0.1 NA NA 1
A 09.08.2020 0.2 NA NA 2
感谢自学 R 业余爱好者的所有帮助。
一个data.table解决方案。虽然您正在寻找 dplyr 解决方案，但我只是想分享。
这个想法是创建标志，指示治疗前 7（或 8？）天和治疗后的天数。
library(data.table)
odt <- fread('Unit Date Prcent_daily.mortality Date.treatment
A 20.07.2020 0.2 NA
A 21.07.2020 0 NA
A 22.07.2020 0.4 NA
A 23.07.2020 0.3 NA
A 24.07.2020 0.6 NA
A 25.07.2020 0.05 NA
A 26.07.2020 0 NA
A 27.07.2020 0 NA
A 28.07.2020 0.01 28.07.2020
A 29.07.2020 0.1 NA
A 30.07.2020 0.2 NA
A 31.07.2020 0 NA
A 01.08.2020 0.2 NA
A 02.08.2020 0.3 NA
A 03.08.2020 0.3 NA
A 04.08.2020 0.05 NA
A 05.08.2020 0 NA
A 06.08.2020 0 NA
A 07.08.2020 0.01 05.08.2020
A 08.08.2020 0.1 NA
A 09.08.2020 0.2 NA')
#create group flags
odt[,postgrp:=cumsum(!is.na(Date.treatment)),by=.(Unit)]
odt[,pregrp:= c(if (postgrp-1 < 0) 0 else postgrp-1,rep(postgrp,.N-1)),by=.(Unit,postgrp)]
treat_date <- odt[,.I[!is.na(Date.treatment)]]
pre7_date <- unlist(Map(seq,treat_date-7,treat_date))
odt[!pre7_date,pregrp:=NA][]
#> Unit Date Prcent_daily.mortality Date.treatment postgrp pregrp
#> 1: A 20.07.2020 0.20 <NA> 0 NA
#> 2: A 21.07.2020 0.00 <NA> 0 0
#> 3: A 22.07.2020 0.40 <NA> 0 0
#> 4: A 23.07.2020 0.30 <NA> 0 0
#> 5: A 24.07.2020 0.60 <NA> 0 0
#> 6: A 25.07.2020 0.05 <NA> 0 0
#> 7: A 26.07.2020 0.00 <NA> 0 0
#> 8: A 27.07.2020 0.00 <NA> 0 0
#> 9: A 28.07.2020 0.01 28.07.2020 1 0
#> 10: A 29.07.2020 0.10 <NA> 1 NA
#> 11: A 30.07.2020 0.20 <NA> 1 NA
#> 12: A 31.07.2020 0.00 <NA> 1 1
#> 13: A 01.08.2020 0.20 <NA> 1 1
#> 14: A 02.08.2020 0.30 <NA> 1 1
#> 15: A 03.08.2020 0.30 <NA> 1 1
#> 16: A 04.08.2020 0.05 <NA> 1 1
#> 17: A 05.08.2020 0.00 <NA> 1 1
#> 18: A 06.08.2020 0.00 <NA> 1 1
#> 19: A 07.08.2020 0.01 05.08.2020 2 1
#> 20: A 08.08.2020 0.10 <NA> 2 NA
#> 21: A 09.08.2020 0.20 <NA> 2 NA
#> Unit Date Prcent_daily.mortality Date.treatment postgrp pregrp
#calculation
odt[!is.na(pregrp),akkum.7dbty:=sum(tail(Prcent_daily.mortality[-.N],7)),by=.(Unit,pregrp)]
odt[postgrp!=0,days.post.treatment:= 0:(.N-1),by=.(Unit,postgrp)]
#result
odt[,c("postgrp","pregrp"):=NULL][]
#> Unit Date Prcent_daily.mortality Date.treatment akkum.7dbty
#> 1: A 20.07.2020 0.20 <NA> NA
#> 2: A 21.07.2020 0.00 <NA> 1.35
#> 3: A 22.07.2020 0.40 <NA> 1.35
#> 4: A 23.07.2020 0.30 <NA> 1.35
#> 5: A 24.07.2020 0.60 <NA> 1.35
#> 6: A 25.07.2020 0.05 <NA> 1.35
#> 7: A 26.07.2020 0.00 <NA> 1.35
#> 8: A 27.07.2020 0.00 <NA> 1.35
#> 9: A 28.07.2020 0.01 28.07.2020 1.35
#> 10: A 29.07.2020 0.10 <NA> NA
#> 11: A 30.07.2020 0.20 <NA> NA
#> 12: A 31.07.2020 0.00 <NA> 0.85
#> 13: A 01.08.2020 0.20 <NA> 0.85
#> 14: A 02.08.2020 0.30 <NA> 0.85
#> 15: A 03.08.2020 0.30 <NA> 0.85
#> 16: A 04.08.2020 0.05 <NA> 0.85
#> 17: A 05.08.2020 0.00 <NA> 0.85
#> 18: A 06.08.2020 0.00 <NA> 0.85
#> 19: A 07.08.2020 0.01 05.08.2020 0.85
#> 20: A 08.08.2020 0.10 <NA> NA
#> 21: A 09.08.2020 0.20 <NA> NA
#> Unit Date Prcent_daily.mortality Date.treatment akkum.7dbty
#> days.post.treatment
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> 6: NA
#> 7: NA
#> 8: NA
#> 9: 0
#> 10: 1
#> 11: 2
#> 12: 3
#> 13: 4
#> 14: 5
#> 15: 6
#> 16: 7
#> 17: 8
#> 18: 9
#> 19: 0
#> 20: 1
#> 21: 2
#> days.post.treatment
由 reprex package (v0.3.0)
于 2020-07-21 创建
试试这个结合了 base R 和 dplyr 的解决方案：
library(dplyr)
library(tidyr)
#Create empty col for index
i1 <- which(!is.na(df$Date.treatment))
i2 <- i1-7
i1 <- i1-1
i3 <- 1:length(i1)
#Create index for second var
j1 <- which(!is.na(df$Date.treatment))
j2 <- 1:length(j1)
# i3 <- i1+1
df$Var <- NA
df$Var[i1]<-i3
df$Var[i2]<-i3
df$Var[1] <- 0
df$Var <- ifelse(!is.na(df$Date.treatment),0,df$Var)
#Fill
df %>% fill(Var) -> df1
#Create aggregations
df1 %>% filter(Var!=0) %>% group_by(Var) %>% mutate(Cum=cumsum(Prcent_daily.mortality)) %>%
filter(Cum==max(Cum)) %>% filter(!duplicated(Cum)) %>% ungroup() %>% select(c(Unit,Cum)) -> Ag1
#Create another var
df$Var2 <- NA
df$Var2[j1] <- j2
df$Var2[1] <- 0
#Fill
df %>% fill(Var2) -> df2
#Create cums and days
df2 %>% group_by(Unit,Var2) %>% mutate(Day=(1:n())-1) %>% ungroup() %>% select(-c(Var2)) -> df3
#Empty var for cums
df3$Cum <- NA
df3$Cum[i1+1] <- Ag1$Cum
#Fill 2
df3 %>% fill(Cum,.direction = 'up') -> df4
#Some adjusts
df4$Day[1:i1[1]]<-NA
df4$Cum[1] <- NA
df4$Cum <- ifelse((df4$Day==1 | df4$Day==2) & !is.na(df4$Day),NA,df4$Cum)
这将产生：
Unit Date Prcent_daily.mortality Date.treatment Var Day Cum
1 A 20.07.2020 0.20 <NA> 0 NA NA
2 A 21.07.2020 0.00 <NA> 1 NA 1.35
3 A 22.07.2020 0.40 <NA> NA NA 1.35
4 A 23.07.2020 0.30 <NA> NA NA 1.35
5 A 24.07.2020 0.60 <NA> NA NA 1.35
6 A 25.07.2020 0.05 <NA> NA NA 1.35
7 A 26.07.2020 0.00 <NA> NA NA 1.35
8 A 27.07.2020 0.00 <NA> 1 NA 1.35
9 A 28.07.2020 0.01 28.07.2020 0 0 1.35
10 A 29.07.2020 0.10 <NA> NA 1 NA
11 A 30.07.2020 0.20 <NA> NA 2 NA
12 A 31.07.2020 0.00 <NA> 2 3 0.85
13 A 01.08.2020 0.20 <NA> NA 4 0.85
14 A 02.08.2020 0.30 <NA> NA 5 0.85
15 A 03.08.2020 0.30 <NA> NA 6 0.85
16 A 04.08.2020 0.05 <NA> NA 7 0.85
17 A 05.08.2020 0.00 <NA> NA 8 0.85
18 A 06.08.2020 0.00 <NA> 2 9 0.85
19 A 07.08.2020 0.01 05.08.2020 0 0 0.85
20 A 08.08.2020 0.10 <NA> NA 1 NA
21 A 09.08.2020 0.20 <NA> NA 2 NA
更新： 在 df4 上工作，您可以使用下一个代码获得 Prcent_daily.mortality 的累积和：
#You can work with df4 to complete the rest of aggregations
#First create an dpuplicate var
df4$DateD <- df4$Date.treatment
#Now fill and mutate
df4 %>% fill(DateD) -> df4
#Create index for replacement
k <- df4$Date.treatment==df4$DateD & !is.na(df4$Date.treatment)
#Assign a value for aggregations not considered
df4$DateD[k]<-'NULL'
#Cumsum
df4 %>% group_by(DateD) %>% mutate(CumAfter=cumsum(Prcent_daily.mortality)) -> df4
#Now remove redundant values in the cum and drop the reference var
df4 %>% ungroup() %>% mutate(CumAfter=ifelse(is.na(DateD) | DateD=='NULL',NA,CumAfter)) %>%
select(-DateD) -> df4
接下来的输出是：
Unit Date Prcent_daily.mortality Date.treatment Var Day Cum CumAfter
1 A 20.07.2020 0.20 <NA> 0 NA NA NA
2 A 21.07.2020 0.00 <NA> 1 NA 1.35 NA
3 A 22.07.2020 0.40 <NA> NA NA 1.35 NA
4 A 23.07.2020 0.30 <NA> NA NA 1.35 NA
5 A 24.07.2020 0.60 <NA> NA NA 1.35 NA
6 A 25.07.2020 0.05 <NA> NA NA 1.35 NA
7 A 26.07.2020 0.00 <NA> NA NA 1.35 NA
8 A 27.07.2020 0.00 <NA> 1 NA 1.35 NA
9 A 28.07.2020 0.01 28.07.2020 0 0 1.35 NA
10 A 29.07.2020 0.10 <NA> NA 1 NA 0.10
11 A 30.07.2020 0.20 <NA> NA 2 NA 0.30
12 A 31.07.2020 0.00 <NA> 2 3 0.85 0.30
13 A 01.08.2020 0.20 <NA> NA 4 0.85 0.50
14 A 02.08.2020 0.30 <NA> NA 5 0.85 0.80
15 A 03.08.2020 0.30 <NA> NA 6 0.85 1.10
16 A 04.08.2020 0.05 <NA> NA 7 0.85 1.15
17 A 05.08.2020 0.00 <NA> NA 8 0.85 1.15
18 A 06.08.2020 0.00 <NA> 2 9 0.85 1.15
19 A 07.08.2020 0.01 05.08.2020 0 0 0.85 NA
20 A 08.08.2020 0.10 <NA> NA 1 NA 0.10
21 A 09.08.2020 0.20 <NA> NA 2 NA 0.30
我需要帮助为两件事编写 for 循环或 dply 代码：
- 计算治疗前规定时期（7 天）内按单位分组的死亡率累积总和 (%)。
- 制作一个向量，计算上次治疗到下一次治疗的天数post。
数据集如下所示：
Unit Date Prcent_daily.mortality Date.treatment
A 20.07.2020 0.2 NA
A 21.07.2020 0 NA
A 22.07.2020 0.4 NA
A 23.07.2020 0.3 NA
A 24.07.2020 0.6 NA
A 25.07.2020 0.05 NA
A 26.07.2020 0 NA
A 27.07.2020 0 NA
A 28.07.2020 0.01 28.07.2020
A 29.07.2020 0.1 NA
A 30.07.2020 0.2 NA
A 31.07.2020 0 NA
A 01.08.2020 0.2 NA
A 02.08.2020 0.3 NA
A 03.08.2020 0.3 NA
A 04.08.2020 0.05 NA
A 05.08.2020 0 NA
A 06.08.2020 0 NA
A 07.08.2020 0.01 05.08.2020
A 08.08.2020 0.1 NA
A 09.08.2020 0.2 NA
我想实现这个：
Unit Date Prcent_daily.mortality Date.treatment akkum.7dbt days.post.treatment
A 20.07.2020 0.2 NA NA NA
A 21.07.2020 0 NA 1.35 NA
A 22.07.2020 0.4 NA 1.35 NA
A 23.07.2020 0.3 NA 1.35 NA
A 24.07.2020 0.6 NA 1.35 NA
A 25.07.2020 0.05 NA 1.35 NA
A 26.07.2020 0 NA 1.35 NA
A 27.07.2020 0 NA 1.35 NA
A 28.07.2020 0.01 28.07.2020 1.35 0
A 29.07.2020 0.1 NA NA 1
A 30.07.2020 0.2 NA NA 2
A 31.07.2020 0 NA 0.85 3
A 01.08.2020 0.2 NA 0.85 4
A 02.08.2020 0.3 NA 0.85 5
A 03.08.2020 0.3 NA 0.85 6
A 04.08.2020 0.05 NA 0.85 7
A 05.08.2020 0 NA 0.85 8
A 06.08.2020 0 NA 0.85 9
A 07.08.2020 0.01 05.08.2020 0.85 0
A 08.08.2020 0.1 NA NA 1
A 09.08.2020 0.2 NA NA 2
感谢自学 R 业余爱好者的所有帮助。
一个data.table解决方案。虽然您正在寻找 dplyr 解决方案，但我只是想分享。
这个想法是创建标志，指示治疗前 7（或 8？）天和治疗后的天数。
library(data.table)
odt <- fread('Unit Date Prcent_daily.mortality Date.treatment
A 20.07.2020 0.2 NA
A 21.07.2020 0 NA
A 22.07.2020 0.4 NA
A 23.07.2020 0.3 NA
A 24.07.2020 0.6 NA
A 25.07.2020 0.05 NA
A 26.07.2020 0 NA
A 27.07.2020 0 NA
A 28.07.2020 0.01 28.07.2020
A 29.07.2020 0.1 NA
A 30.07.2020 0.2 NA
A 31.07.2020 0 NA
A 01.08.2020 0.2 NA
A 02.08.2020 0.3 NA
A 03.08.2020 0.3 NA
A 04.08.2020 0.05 NA
A 05.08.2020 0 NA
A 06.08.2020 0 NA
A 07.08.2020 0.01 05.08.2020
A 08.08.2020 0.1 NA
A 09.08.2020 0.2 NA')
#create group flags
odt[,postgrp:=cumsum(!is.na(Date.treatment)),by=.(Unit)]
odt[,pregrp:= c(if (postgrp-1 < 0) 0 else postgrp-1,rep(postgrp,.N-1)),by=.(Unit,postgrp)]
treat_date <- odt[,.I[!is.na(Date.treatment)]]
pre7_date <- unlist(Map(seq,treat_date-7,treat_date))
odt[!pre7_date,pregrp:=NA][]
#> Unit Date Prcent_daily.mortality Date.treatment postgrp pregrp
#> 1: A 20.07.2020 0.20 <NA> 0 NA
#> 2: A 21.07.2020 0.00 <NA> 0 0
#> 3: A 22.07.2020 0.40 <NA> 0 0
#> 4: A 23.07.2020 0.30 <NA> 0 0
#> 5: A 24.07.2020 0.60 <NA> 0 0
#> 6: A 25.07.2020 0.05 <NA> 0 0
#> 7: A 26.07.2020 0.00 <NA> 0 0
#> 8: A 27.07.2020 0.00 <NA> 0 0
#> 9: A 28.07.2020 0.01 28.07.2020 1 0
#> 10: A 29.07.2020 0.10 <NA> 1 NA
#> 11: A 30.07.2020 0.20 <NA> 1 NA
#> 12: A 31.07.2020 0.00 <NA> 1 1
#> 13: A 01.08.2020 0.20 <NA> 1 1
#> 14: A 02.08.2020 0.30 <NA> 1 1
#> 15: A 03.08.2020 0.30 <NA> 1 1
#> 16: A 04.08.2020 0.05 <NA> 1 1
#> 17: A 05.08.2020 0.00 <NA> 1 1
#> 18: A 06.08.2020 0.00 <NA> 1 1
#> 19: A 07.08.2020 0.01 05.08.2020 2 1
#> 20: A 08.08.2020 0.10 <NA> 2 NA
#> 21: A 09.08.2020 0.20 <NA> 2 NA
#> Unit Date Prcent_daily.mortality Date.treatment postgrp pregrp
#calculation
odt[!is.na(pregrp),akkum.7dbty:=sum(tail(Prcent_daily.mortality[-.N],7)),by=.(Unit,pregrp)]
odt[postgrp!=0,days.post.treatment:= 0:(.N-1),by=.(Unit,postgrp)]
#result
odt[,c("postgrp","pregrp"):=NULL][]
#> Unit Date Prcent_daily.mortality Date.treatment akkum.7dbty
#> 1: A 20.07.2020 0.20 <NA> NA
#> 2: A 21.07.2020 0.00 <NA> 1.35
#> 3: A 22.07.2020 0.40 <NA> 1.35
#> 4: A 23.07.2020 0.30 <NA> 1.35
#> 5: A 24.07.2020 0.60 <NA> 1.35
#> 6: A 25.07.2020 0.05 <NA> 1.35
#> 7: A 26.07.2020 0.00 <NA> 1.35
#> 8: A 27.07.2020 0.00 <NA> 1.35
#> 9: A 28.07.2020 0.01 28.07.2020 1.35
#> 10: A 29.07.2020 0.10 <NA> NA
#> 11: A 30.07.2020 0.20 <NA> NA
#> 12: A 31.07.2020 0.00 <NA> 0.85
#> 13: A 01.08.2020 0.20 <NA> 0.85
#> 14: A 02.08.2020 0.30 <NA> 0.85
#> 15: A 03.08.2020 0.30 <NA> 0.85
#> 16: A 04.08.2020 0.05 <NA> 0.85
#> 17: A 05.08.2020 0.00 <NA> 0.85
#> 18: A 06.08.2020 0.00 <NA> 0.85
#> 19: A 07.08.2020 0.01 05.08.2020 0.85
#> 20: A 08.08.2020 0.10 <NA> NA
#> 21: A 09.08.2020 0.20 <NA> NA
#> Unit Date Prcent_daily.mortality Date.treatment akkum.7dbty
#> days.post.treatment
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> 6: NA
#> 7: NA
#> 8: NA
#> 9: 0
#> 10: 1
#> 11: 2
#> 12: 3
#> 13: 4
#> 14: 5
#> 15: 6
#> 16: 7
#> 17: 8
#> 18: 9
#> 19: 0
#> 20: 1
#> 21: 2
#> days.post.treatment
由 reprex package (v0.3.0)
于 2020-07-21 创建试试这个结合了 base R 和 dplyr 的解决方案：
library(dplyr)
library(tidyr)
#Create empty col for index
i1 <- which(!is.na(df$Date.treatment))
i2 <- i1-7
i1 <- i1-1
i3 <- 1:length(i1)
#Create index for second var
j1 <- which(!is.na(df$Date.treatment))
j2 <- 1:length(j1)
# i3 <- i1+1
df$Var <- NA
df$Var[i1]<-i3
df$Var[i2]<-i3
df$Var[1] <- 0
df$Var <- ifelse(!is.na(df$Date.treatment),0,df$Var)
#Fill
df %>% fill(Var) -> df1
#Create aggregations
df1 %>% filter(Var!=0) %>% group_by(Var) %>% mutate(Cum=cumsum(Prcent_daily.mortality)) %>%
filter(Cum==max(Cum)) %>% filter(!duplicated(Cum)) %>% ungroup() %>% select(c(Unit,Cum)) -> Ag1
#Create another var
df$Var2 <- NA
df$Var2[j1] <- j2
df$Var2[1] <- 0
#Fill
df %>% fill(Var2) -> df2
#Create cums and days
df2 %>% group_by(Unit,Var2) %>% mutate(Day=(1:n())-1) %>% ungroup() %>% select(-c(Var2)) -> df3
#Empty var for cums
df3$Cum <- NA
df3$Cum[i1+1] <- Ag1$Cum
#Fill 2
df3 %>% fill(Cum,.direction = 'up') -> df4
#Some adjusts
df4$Day[1:i1[1]]<-NA
df4$Cum[1] <- NA
df4$Cum <- ifelse((df4$Day==1 | df4$Day==2) & !is.na(df4$Day),NA,df4$Cum)
这将产生：
Unit Date Prcent_daily.mortality Date.treatment Var Day Cum
1 A 20.07.2020 0.20 <NA> 0 NA NA
2 A 21.07.2020 0.00 <NA> 1 NA 1.35
3 A 22.07.2020 0.40 <NA> NA NA 1.35
4 A 23.07.2020 0.30 <NA> NA NA 1.35
5 A 24.07.2020 0.60 <NA> NA NA 1.35
6 A 25.07.2020 0.05 <NA> NA NA 1.35
7 A 26.07.2020 0.00 <NA> NA NA 1.35
8 A 27.07.2020 0.00 <NA> 1 NA 1.35
9 A 28.07.2020 0.01 28.07.2020 0 0 1.35
10 A 29.07.2020 0.10 <NA> NA 1 NA
11 A 30.07.2020 0.20 <NA> NA 2 NA
12 A 31.07.2020 0.00 <NA> 2 3 0.85
13 A 01.08.2020 0.20 <NA> NA 4 0.85
14 A 02.08.2020 0.30 <NA> NA 5 0.85
15 A 03.08.2020 0.30 <NA> NA 6 0.85
16 A 04.08.2020 0.05 <NA> NA 7 0.85
17 A 05.08.2020 0.00 <NA> NA 8 0.85
18 A 06.08.2020 0.00 <NA> 2 9 0.85
19 A 07.08.2020 0.01 05.08.2020 0 0 0.85
20 A 08.08.2020 0.10 <NA> NA 1 NA
21 A 09.08.2020 0.20 <NA> NA 2 NA
更新： 在 df4 上工作，您可以使用下一个代码获得 Prcent_daily.mortality 的累积和：
#You can work with df4 to complete the rest of aggregations
#First create an dpuplicate var
df4$DateD <- df4$Date.treatment
#Now fill and mutate
df4 %>% fill(DateD) -> df4
#Create index for replacement
k <- df4$Date.treatment==df4$DateD & !is.na(df4$Date.treatment)
#Assign a value for aggregations not considered
df4$DateD[k]<-'NULL'
#Cumsum
df4 %>% group_by(DateD) %>% mutate(CumAfter=cumsum(Prcent_daily.mortality)) -> df4
#Now remove redundant values in the cum and drop the reference var
df4 %>% ungroup() %>% mutate(CumAfter=ifelse(is.na(DateD) | DateD=='NULL',NA,CumAfter)) %>%
select(-DateD) -> df4
接下来的输出是：
Unit Date Prcent_daily.mortality Date.treatment Var Day Cum CumAfter
1 A 20.07.2020 0.20 <NA> 0 NA NA NA
2 A 21.07.2020 0.00 <NA> 1 NA 1.35 NA
3 A 22.07.2020 0.40 <NA> NA NA 1.35 NA
4 A 23.07.2020 0.30 <NA> NA NA 1.35 NA
5 A 24.07.2020 0.60 <NA> NA NA 1.35 NA
6 A 25.07.2020 0.05 <NA> NA NA 1.35 NA
7 A 26.07.2020 0.00 <NA> NA NA 1.35 NA
8 A 27.07.2020 0.00 <NA> 1 NA 1.35 NA
9 A 28.07.2020 0.01 28.07.2020 0 0 1.35 NA
10 A 29.07.2020 0.10 <NA> NA 1 NA 0.10
11 A 30.07.2020 0.20 <NA> NA 2 NA 0.30
12 A 31.07.2020 0.00 <NA> 2 3 0.85 0.30
13 A 01.08.2020 0.20 <NA> NA 4 0.85 0.50
14 A 02.08.2020 0.30 <NA> NA 5 0.85 0.80
15 A 03.08.2020 0.30 <NA> NA 6 0.85 1.10
16 A 04.08.2020 0.05 <NA> NA 7 0.85 1.15
17 A 05.08.2020 0.00 <NA> NA 8 0.85 1.15
18 A 06.08.2020 0.00 <NA> 2 9 0.85 1.15
19 A 07.08.2020 0.01 05.08.2020 0 0 0.85 NA
20 A 08.08.2020 0.10 <NA> NA 1 NA 0.10
21 A 09.08.2020 0.20 <NA> NA 2 NA 0.30