如何仅将一个向量的最后一个元素添加到另一个向量的最后一个元素？

Question

I have a dataframe with hundreds of different investments (represented by the "id" column), their cashflows, and market value. The following example demonstrates the data that I'm working with:

df <- data.frame(date = c("2020-01-31", "2020-02-29", "2020-03-31", "2020-02-29", "2020-03-31", "2020-04-30", "2020-05-31"),
                 id = c("alpha", "alpha", "alpha", "bravo", "bravo", "bravo", "bravo"),
                 cashflow = c(-100,20,4,-50,8,12,8),
                 market_value = c(100,90,80,50,110,120,115))

I ultimately want to calculate the IRR per investment. However, before I can do that, I need to add only the last market value number to the corresponding cashflow. I don't care about any market values before that. In this case, the last cashflow for "alpha" investment must be 84 (ie, 80 market value + 4 cashflow) and the last cashflow for "bravo" investment must be 123 (ie, 115 market value + 8 cashflow).

Desired output:

id	cashflow
alpha	-100
alpha	20
alpha	84
bravo	-50
bravo	8
bravo	12
bravo	123

Thanks!

Answer 1

I'm not too sure on what final output you want but here's how you'd just take the last.

df %>%
mutate(total = cashflow + market_value) %>%
group_by(id) %>%
slice_max(order_by = date) %>%
ungroup()

#> # A tibble: 2 × 5
#>   date       id    cashflow market_value total
#>   <chr>      <chr>    <dbl>        <dbl> <dbl>
#> 1 2020-03-31 alpha        4           80    84
#> 2 2020-05-31 bravo        8          115   123

^{Created on 2022-07-22 by the reprex package (v2.0.1)}

EDIT - just seen what I think is your desired output, how's this?

df %>%
  group_by(id) %>%
  mutate(
    cashflow = if_else(row_number() == n(), cashflow + market_value, cashflow)
  )
#> # A tibble: 7 × 4
#> # Groups:   id [2]
#>   date       id    cashflow market_value
#>   <chr>      <chr>    <dbl>        <dbl>
#> 1 2020-01-31 alpha     -100          100
#> 2 2020-02-29 alpha       20           90
#> 3 2020-03-31 alpha       84           80
#> 4 2020-02-29 bravo      -50           50
#> 5 2020-03-31 bravo        8          110
#> 6 2020-04-30 bravo       12          120
#> 7 2020-05-31 bravo      123          115

^{Created on 2022-07-22 by the reprex package (v2.0.1)}