以开始和结束日期为条件的时间周期总和

Periodic sum over time conditional on start and end date

我尝试为每一天、每一周和每一月构建 x 列的总和。如果特定的一天、一周或一个月在开始日期和结束日期之间,我想包括数字 x 并将它们相加。

我构建了这个示例数据框:

library(data.table)
library(lubridate)

df <- data.frame(x=c(13,32,37,21,9,43,12,28),
                 start=c('2018-06-12','2019-02-12','2018-12-30','2020-02-05','2019-09-29','2017-05-19','2019-06-13','2020-04-12'), 
                 end=c('2018-09-13','2019-03-19','2020-01-10','2020-03-17','2020-10-10','2020-01-02','2019-07-19','2021-06-01'))

#convert columns as date
df$start <- as.Date(df$start,"%Y-%m-%d")
df$end <- as.Date(df$end,"%Y-%m-%d")

我试图在每一天进行一个 for 循环,以总结每个特定时期的 x 列,但我无法做到。

#for loop over days
days <- seq(from=as.Date("2017-01-01"), to=as.Date("2021-07-31"), by="days")
for (i in seq_along(days)){
  print(sum(df$x))}

非常感谢您的帮助:)

试试这个:

library(data.table)
library(lubridate)
library(dplyr)

df <- df %>% 
  mutate(start = as.Date(start),
         end = as.Date(end)) %>% ## convert columns as date
  as.data.table() ## convert frame to table


days <- seq(from=as.Date("2017-01-01"), to=as.Date("2021-07-31"), by="days")
total <- 0

for (day in days) {
  total <- total + df[start <= day & end >= day, sum(x)]
}


out:
> print(total)
[1] 72784

将每天的结果存储在 table:

days <- seq(from=as.Date("2017-01-01"), to=as.Date("2021-07-31"), by="days")
tab_results <- data.table(Date = as.Date(character()), 
                          x = as.integer() )

for (day in days) {
  tab_results <- tab_results %>% add_row(Date = as.Date(day, origin = "1970-01-01"), 
                                         x = df[start <= day & end >= day, sum(x)])
}

数据:

df <- data.frame(x=c(13,32,37,21,9,43,12,28),
                 start=c('2018-06-12','2019-02-12','2018-12-30','2020-02-05','2019-09-29','2017-05-19','2019-06-13','2020-04-12'), 
                 end=c('2018-09-13','2018-03-19','2020-01-10','2020-03-17','2020-10-10','2020-01-02','2019-07-19','2021-06-01'))

您可以展开每一行的 startend 日期,并用它创建一个新行。对于每个日期,您可以 sum x 值。我们使用 complete 来填充缺失的日期(如果存在)。

library(tidyverse)

df %>%
  mutate(dates = map2(start, end, seq, by = 'days')) %>%
  unnest(dates) %>%
  group_by(dates) %>%
  summarise(x = sum(x)) %>%
  complete(dates = seq(min(dates), max(dates), by = 'days'), fill = list(x = 0)) 

#   dates          x
#   <date>     <dbl>
# 1 2017-05-19    43
# 2 2017-05-20    43
# 3 2017-05-21    43
# 4 2017-05-22    43
# 5 2017-05-23    43
# 6 2017-05-24    43
# 7 2017-05-25    43
# 8 2017-05-26    43
# 9 2017-05-27    43
#10 2017-05-28    43
# … with 1,465 more rows