我按会计年度（不是日历年）预测包含开始日期和时间长度或结束日期或两者的合同的收入。我的方法包括查找每个会计年度中存在的合同比例并应用合同总价值（在示例代码中由
amountslibrary(dplyr)
library(lubridate)
FY16 <- interval("2015-10-01", "2016-09-30")
FY17 <- interval("2016-10-01", "2017-09-30")
FY18 <- interval("2017-10-01", "2018-09-30")
FY19 <- interval("2018-10-01", "2019-09-30")
FY20 <- interval("2019-10-01", "2020-09-30")
FY21 <- interval("2020-10-01", "2021-09-30")
FY22 <- interval("2021-10-01", "2022-09-30")
FY23 <- interval("2022-10-01", "2023-09-30")
FY24 <- interval("2023-10-01", "2024-09-30")
FY25 <- interval("2024-10-01", "2025-09-30")
FY26 <- interval("2025-10-01", "2026-09-30")
FY27 <- interval("2026-10-01", "2027-09-30")
FY28 <- interval("2027-10-01", "2028-09-30")
FY29 <- interval("2028-10-01", "2029-09-30")
FY30 <- interval("2029-10-01", "2030-09-30")
FY31 <- interval("2030-10-01", "2031-09-30")
FY32 <- interval("2031-10-01", "2032-09-30")
data <- tibble(startDates = c("2018-04-01", "2025-09-17", "2021-07-21", "2026-11-17"),
years = c(5,3,3,2),
amounts = c(350000, 1200000, 7500000, 130000),
surcharge = c(0.035, 0.04, 0.375, 0.05))
data <- as.data.frame(data)%>%
mutate(startDates =as.POSIXct(`startDates`),
endDate = `startDates`%m+%years(years),
contractInterval = interval(startDates, endDate),
`Days in Contract` = startDates%--%endDate/ddays(1),
`FY16 Interval` = intersect(FY16, contractInterval),
`FY17 Interval` = intersect(FY17, contractInterval),
`FY18 Interval` = intersect(FY18, contractInterval),
`FY19 Interval` = intersect(FY19, contractInterval),
`FY20 Interval` = intersect(FY20, contractInterval),
`FY21 Interval` = intersect(FY21, contractInterval),
#and so on to FY32
`Days in FY16` = int_start(`FY16 Interval`)%--%int_end(`FY16 Interval`)/ddays(1),
`Days in FY17` = int_start(`FY17 Interval`)%--%int_end(`FY17 Interval`)/ddays(1),
`Days in FY18` = int_start(`FY18 Interval`)%--%int_end(`FY18 Interval`)/ddays(1),
`Days in FY19` = int_start(`FY19 Interval`)%--%int_end(`FY19 Interval`)/ddays(1),
`Days in FY20` = int_start(`FY20 Interval`)%--%int_end(`FY20 Interval`)/ddays(1),
`Days in FY21` = int_start(`FY21 Interval`)%--%int_end(`FY21 Interval`)/ddays(1),
#and so on to FY32
`FY16 Amount` = amounts*`Days in FY16`/`Days in Contract`,
`FY17 Amount` = amounts*`Days in FY17`/`Days in Contract`,
`FY18 Amount` = amounts*`Days in FY18`/`Days in Contract`,
`FY19 Amount` = amounts*`Days in FY19`/`Days in Contract`,
`FY20 Amount` = amounts*`Days in FY20`/`Days in Contract`,
`FY21 Amount` = amounts*`Days in FY21`/`Days in Contract`,
#and so on to FY32
`FY16 Surcharge Amount` = surcharge*`FY 16 Amount`,
`FY17 Surcharge Amount` = surcharge*`FY 17 Amount`,
`FY18 Surcharge Amount` = surcharge*`FY 18 Amount`,
`FY19 Surcharge Amount` = surcharge*`FY 19 Amount`,
`FY20 Surcharge Amount` = surcharge*`FY 20 Amount`,
`FY21 Surcharge Amount` = surcharge*`FY 21 Amount`
#and so on to FY32
)
data.summary <- data%>%
summarise(`FY16 Total` = sum(`FY16 Amount`, na.rm = T),
`FY17 Total` = sum(`FY17 Amount`, na.rm = T),
`FY18 Total` = sum(`FY18 Amount`, na.rm = T),
`FY19 Total` = sum(`FY19 Amount`, na.rm = T),
`FY20 Total` = sum(`FY20 Amount`, na.rm = T),
`FY21 Total` = sum(`FY21 Amount`, na.rm = T),
# and so on to FY32
`FY16 Surcharge Total` = sum(`FY16 Surcharge Amount`, na.rm = T),
`FY17 Surcharge Total` = sum(`FY17 Surcharge Amount`, na.rm = T),
`FY18 Surcharge Total` = sum(`FY18 Surcharge Amount`, na.rm = T),
`FY19 Surcharge Total` = sum(`FY19 Surcharge Amount`, na.rm = T),
`FY20 Surcharge Total` = sum(`FY20 Surcharge Amount`, na.rm = T),
`FY21 Surcharge Total` = sum(`FY21 Surcharge Amount`, na.rm = T),
#and so on to FY32
)
它非常简单并且工作正常，但是编码起来非常乏味！更糟糕的是，我的方法已经引起了人们的关注，并且很可能会被部署到所有地区和部门。好消息，对吧？当我必须在准备和总结中分别对每个会计年度进行编码时就不行了。难道我的生活就只剩下复制粘贴和更改两位数了吗？请说不是。 我怎样才能更有效地做到这一点？我欢迎所有建议，并提前感谢您。
我建议以“长数据”的方式而不是“宽数据”的方式来思考这个问题：它更加动态，代码更短，并且允许轻松更改为更少或更多的会计年度，而不需要太多努力。
首先，我认为我们有兴趣涵盖这些财政年度：
FYs <- tibble(FYstart = as.Date("2015-10-01") %m+% years(0:15)) %>%
mutate(
FYend = FYstart %m+% years(1) - 1,
FY = paste0("FY", substr(year(FYend), 3, 4))
)
从那里开始，我们将从最初的四列开始，然后加入
FYsdata <-
tibble(startDates = as.POSIXct(c("2018-04-01", "2025-09-17", "2021-07-21", "2026-11-17")),
years = c(5,3,3,2),
amounts = c(350000, 1200000, 7500000, 130000),
surcharge = c(0.035, 0.04, 0.375, 0.05)) %>%
mutate(endDates = startDates %m+% years(years)) %>%
cross_join(FYs) |> # admittedly "expensive"
filter(between(startDates, FYstart, FYend) |
between(endDates, FYstart, FYend) |
(startDates < FYstart & endDates > FYend)) %>%
mutate(
FYstart = pmax(startDates, FYstart),
FYend = pmin(endDates, FYend),
`Days in Contract` = as.numeric(endDates - startDates, units = "days"),
`Days in FY` = as.numeric(FYend - FYstart, units = "days"),
`FY Amount` = amounts * `Days in FY` / `Days in Contract`,
`FY Surcharge Amount` = surcharge * `FY Amount`
)
data
# # A tibble: 17 × 12
# startDates years amounts surcharge endDates FYstart FYend FY `Days in Contract` `Days in FY` `FY Amount` `FY Surcharge Amount`
# <dttm> <dbl> <dbl> <dbl> <dttm> <dttm> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2018-04-01 00:00:00 2018-09-29 20:00:00 FY18 1826 182. 34853. 1220.
# 2 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2018-09-30 20:00:00 2019-09-29 20:00:00 FY19 1826 364 69770. 2442.
# 3 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2019-09-30 20:00:00 2020-09-29 20:00:00 FY20 1826 365 69962. 2449.
# 4 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2020-09-30 20:00:00 2021-09-29 20:00:00 FY21 1826 364 69770. 2442.
# 5 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2021-09-30 20:00:00 2022-09-29 20:00:00 FY22 1826 364 69770. 2442.
# 6 2018-04-01 00:00:00 5 350000 0.035 2023-04-01 00:00:00 2022-09-30 20:00:00 2023-04-01 00:00:00 FY23 1826 182. 34917. 1222.
# 7 2025-09-17 00:00:00 3 1200000 0.04 2028-09-17 00:00:00 2025-09-17 00:00:00 2025-09-29 20:00:00 FY25 1096 12.8 14051. 562.
# 8 2025-09-17 00:00:00 3 1200000 0.04 2028-09-17 00:00:00 2025-09-30 20:00:00 2026-09-29 20:00:00 FY26 1096 364 398540. 15942.
# 9 2025-09-17 00:00:00 3 1200000 0.04 2028-09-17 00:00:00 2026-09-30 20:00:00 2027-09-29 20:00:00 FY27 1096 364 398540. 15942.
# 10 2025-09-17 00:00:00 3 1200000 0.04 2028-09-17 00:00:00 2027-09-30 20:00:00 2028-09-17 00:00:00 FY28 1096 352. 385584. 15423.
# 11 2021-07-21 00:00:00 3 7500000 0.375 2024-07-21 00:00:00 2021-07-21 00:00:00 2021-09-29 20:00:00 FY21 1096 70.8 484717. 181769.
# 12 2021-07-21 00:00:00 3 7500000 0.375 2024-07-21 00:00:00 2021-09-30 20:00:00 2022-09-29 20:00:00 FY22 1096 364 2490876. 934078.
# 13 2021-07-21 00:00:00 3 7500000 0.375 2024-07-21 00:00:00 2022-09-30 20:00:00 2023-09-29 20:00:00 FY23 1096 364 2490876. 934078.
# 14 2021-07-21 00:00:00 3 7500000 0.375 2024-07-21 00:00:00 2023-09-30 20:00:00 2024-07-21 00:00:00 FY24 1096 294. 2013002. 754876.
# 15 2026-11-17 00:00:00 2 130000 0.05 2028-11-17 00:00:00 2026-11-17 00:00:00 2027-09-29 20:00:00 FY27 731 317. 56338. 2817.
# 16 2026-11-17 00:00:00 2 130000 0.05 2028-11-17 00:00:00 2027-09-30 20:00:00 2028-09-29 20:00:00 FY28 731 365 64911. 3246.
# 17 2026-11-17 00:00:00 2 130000 0.05 2028-11-17 00:00:00 2028-09-30 20:00:00 2028-11-17 00:00:00 FY29 731 47.2 8395. 420.
总结也很直接：
data.summary <- data %>%
summarize(
.by = FY,
across(c(`FY Amount`, `FY Surcharge Amount`), ~ sum(.x, na.rm = TRUE),
.names = "{.col} Total")
)
data.summary
# # A tibble: 12 × 3
# FY `FY Amount Total` `FY Surcharge Amount Total`
# <chr> <dbl> <dbl>
# 1 FY18 34853. 1220.
# 2 FY19 69770. 2442.
# 3 FY20 69962. 2449.
# 4 FY21 554487. 184211.
# 5 FY22 2560646. 936520.
# 6 FY23 2525793. 935301.
# 7 FY25 14051. 562.
# 8 FY26 398540. 15942.
# 9 FY27 454878. 18758.
# 10 FY28 450495. 18669.
# 11 FY24 2013002. 754876.
# 12 FY29 8395. 420.