在基R中，您可以创建一个从 Storm 日期起+/- 3天的序列，然后使用简单的逻辑检查您的df$Date值是否在使用%in%的 Storm 日期向量中。
(Note我将示例数据框命名为df，将示例 Storm 日期命名为events）

df$AR_occurence <- df$Date %in% (rep(events$Date, each=7) + -3:3) # thanks @thelatemail

输出：

Date  NO3  cumP Season AR_occurence
1  1990-01-15 2.37 122.8      R         TRUE
2  1990-03-15 3.42 104.0      R        FALSE
3  1990-05-15 3.13  19.9      F        FALSE
4  1990-07-15 2.24  20.4      I        FALSE
5  1990-08-15 1.97   0.0      H        FALSE
6  1990-09-15 2.22   8.8      R        FALSE
7  1990-10-15 2.58 134.3      R        FALSE
8  1990-11-15 2.15 232.8      R         TRUE
9  1990-12-15 2.05 168.3      R         TRUE
10 1991-01-15 3.09 171.3      R        FALSE

这是可行的：

a = structure(list(Date = structure(c(7319, 7378, 7439, 7500, 7531, 7562, 7592, 7623, 7653, 7684), class = "Date"), NO3 = c(2.37, 3.42, 3.13, 2.24, 1.97, 2.22, 2.58, 2.15, 2.05, 3.09), cumP = c(122.8, 104, 19.9, 20.4, 0, 8.8, 134.3, 232.8, 168.3, 171.3), Season = c("R", "R", "F", "I", "H", "R", "R", "R", "R", "R")), row.names = c(NA,
                                                                                                                                                                                                                                                                                                                                        10L), class = "data.frame")
a2 = structure(list(Date = structure(c(7311, 7313, 7316, 7329, 7338, 7345, 7355, 7451, 7458, 7474, 7580, 7581, 7586, 7598, 7601, 7602, 7615, 7617, 7618, 7619, 7620, 7621, 7630, 7631, 7632, 7637, 7641, 7642, 7646, 7647, 7655), class = "Date")), row.names = c(NA, -31L), class = "data.frame")

storm_dates = a2 %>% pull  

  a %>% rowwise %>%
   mutate(closest_date = storm_dates[which.min(abs(storm_dates - Date))]) %>% 
   mutate(AR_ocurrence = abs(Date - closest_date) <= 3, , .keep = 'unused')

 # NO3  cumP Season AR_ocurrence
 # <dbl> <dbl> <chr>  <lgl>       
 # 1  2.37 123.  R      TRUE        
 # 2  3.42 104   R      FALSE       
 # 3  3.13  19.9 F      FALSE       
 # 4  2.24  20.4 I      FALSE       
 # 5  1.97   0   H      FALSE       
 # 6  2.22   8.8 R      FALSE       
 # 7  2.58 134.  R      FALSE       
 # 8  2.15 233.  R      TRUE        
 # 9  2.05 168.  R      TRUE        
 # 10  3.09 171.  R      FALSE

编辑：改变了我的答案。还添加了删除closest_date的位