如何将一个数据框中的特定行的总和输出到另一个数据框中的新列？

Question

I would ultimately like to have df2 with certain dates and the cumulative sum of values connected to those date ranges from df1.

df1 = data.frame("date"=c("10/01/2020","10/02/2020","10/03/2020","10/04/2020","10/05/2020",
                          "10/06/2020","10/07/2020","10/08/2020","10/09/2020","10/10/2020"),
                 "value"=c(1:10))
df1

> df1
   date       value
1  10/01/2020     1
2  10/02/2020     2
3  10/03/2020     3
4  10/04/2020     4
5  10/05/2020     5
6  10/06/2020     6
7  10/07/2020     7
8  10/08/2020     8
9  10/09/2020     9
10 10/10/2020    10

df2 = data.frame("date"=c("10/05/2020","10/10/2020"))
df2

> df2
  date
1 10/05/2020
2 10/10/2020

I realize this is incorrect, but I am not sure how to define df2$value as the sums of certain df1$value rows:

df2$value = filter(df1, c(sum(1:5),sum(6:10)))
df2

I would like the output to look like this:

> df2
   date       value
1  10/05/2020    15
2  10/10/2020    40

Answer 1

We may use a non-equi join after converting the 'date' columns to Date class

library(lubridate)
library(data.table)
setDT(df1)[, date := mdy(date)]
setDT(df2)[, date := mdy(date)]
df2[, start_date := fcoalesce(shift(date) + days(1), floor_date(date, 'month'))]

df1[df2,.(value = sum(value)), on = .( date >= start_date, 
      date <= date), by = .EACHI][, -1, with = FALSE]
         date value
       <Date> <int>
1: 2020-10-05    15
2: 2020-10-10    40

---

Or another option is creating a group with findInterval and then do the group by sum

library(dplyr)
df1 %>% 
  group_by(grp = findInterval(date, df2$date, left.open = TRUE)) %>% 
  summarise(date = last(date), value = sum(value)) %>% 
  select(-grp)

-output

# A tibble: 2 × 2
  date       value
  <date>     <int>
1 2020-10-05    15
2 2020-10-10    40

Answer 2

Here is another approach using dplyr and lubridate :

library(lubridate)
library(dplyr)

df1 %>% 
  mutate(date = dmy(date)) %>%
  mutate(date = if_else(date == "2020-05-10" |
                      date == "2020-10-10", date, NA_Date_)) %>% 
  fill(date, .direction = "up") %>% 
  group_by(date) %>% 
  summarise(value = sum(value))

  date       value
  <date>     <int>
1 2020-05-10    15
2 2020-10-10    40