R 特定日期范围的累计总和

如果您想要连续四个Rates的滚动总和，可以使用zoo/rollsum()函数:

library(dplyr)
library(zoo)

dat %>% 
  mutate(Result = rollsum(Rates, k = 4, fill = NA_real_, align = "left"))

这就是回报

# A tibble: 11 × 5
      no DATE       Rates DATE_following Result
   <dbl> <date>     <dbl> <date>          <dbl>
 1     1 2023-03-27     9 2023-03-30         21
 2     2 2023-03-28     4 2023-03-31         14
 3     3 2023-03-29     7 2023-04-01         17
 4     4 2023-03-30     1 2023-04-02         12
 5     5 2023-03-31     2 2023-04-03         14
 6     6 2023-04-01     7 2023-04-04         13
 7     7 2023-04-02     2 2023-04-05         11
 8     8 2023-04-03     3 2023-04-06         14
 9     9 2023-04-04     1 2023-04-07         NA
10    10 2023-04-05     5 2023-04-08         NA
11    11 2023-04-06     5 2023-04-09         NA

基于勒马克的 comments ，我给出了一个稍微更笼统的答案:

dat2 %>% 
  mutate(days = as.integer(DATE_following - DATE) + 1,
         res = rollapply(data = Rates, width = days, FUN = sum, align = "left", fill = NA_real_))

这就是回报

# A tibble: 11 × 6
      no DATE       Rates DATE_following  days   res
   <dbl> <date>     <dbl> <date>         <dbl> <dbl>
 1     1 2023-03-27     9 2023-03-30         4    21
 2     2 2023-03-28     4 2023-03-31         4    14
 3     3 2023-03-29     7 2023-04-01         4    17
 4     4 2023-03-30     1 2023-04-02         4    12
 5     5 2023-03-31     2 2023-04-10        11    NA
 6     6 2023-04-01     7 2023-04-04         4    13
 7     7 2023-04-02     2 2023-04-05         4    11
 8     8 2023-04-03     3 2023-04-06         4    14
 9     9 2023-04-04     1 2023-04-07         4    NA
10    10 2023-04-05     5 2023-04-08         4    NA
11    11 2023-04-06     5 2023-04-09         4    NA

由于第5行中的DATE_following不在数据中，因此此版本返回NA.此外，这个版本不是连续四天的总和，而是计算DATE到DATE_following之间的天数，并将它们应用于滚动总和.

数据

dat <- structure(list(no = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), DATE = structure(c(19443, 
19444, 19445, 19446, 19447, 19448, 19449, 19450, 19451, 19452, 
19453), class = "Date"), Rates = c(9, 4, 7, 1, 2, 7, 2, 3, 1, 
5, 5), DATE_following = structure(c(19446, 19447, 19448, 19449, 
19450, 19451, 19452, 19453, 19454, 19455, 19456), class = "Date")), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -11L), spec = structure(list(
    cols = list(no = structure(list(), class = c("collector_double", 
    "collector")), DATE = structure(list(format = ""), class = c("collector_date", 
    "collector")), Rates = structure(list(), class = c("collector_double", 
    "collector")), DATE_following = structure(list(format = ""), class = c("collector_date", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1L), class = "col_spec"))

dat2 <- structure(list(no = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), DATE = structure(c(19443, 
19444, 19445, 19446, 19447, 19448, 19449, 19450, 19451, 19452, 
19453), class = "Date"), Rates = c(9, 4, 7, 1, 2, 7, 2, 3, 1, 
5, 5), DATE_following = structure(c(19446, 19447, 19448, 19449, 
19457, 19451, 19452, 19453, 19454, 19455, 19456), class = "Date")), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -11L), spec = structure(list(
    cols = list(no = structure(list(), class = c("collector_double", 
    "collector")), DATE = structure(list(format = ""), class = c("collector_date", 
    "collector")), Rates = structure(list(), class = c("collector_double", 
    "collector")), DATE_following = structure(list(format = ""), class = c("collector_date", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1L), class = "col_spec"))