# how best to add these two data frames together?

Below is the sample data. Here is what I am trying to do.. I have data
in the first data frame. It is an adjustment. The second data frame is the existing data. For the year, d2022, I need to substract the values from first from the items in second. How would I go about this?

`````` indcode  <- c(482000,482000,482000,524000,524000,524000)
area <- c(000032,029820,039900,000032,029820,039900)
ownership <- c(50,50,50,50,50,50)
d2022 <- c(10,6,2,100,60,20)

First<-data.frame(indcode,area,ownership,d2022)

## This is the existing data

indcode <- c(482000,482000,482000,484000,484000,484000,524000,524000,524000,531000,531000,531000)
area <- c(000032,029820,039900,000032,029820,039900,000032,029820,039900,000032,029820,039900)
ownership <- c(50,50,50,20,30,50,50,50,50,20,30,50)
d2020 <- c(200,199,198,197,196,195,194,193,192,191,190,189)
d2021 <- c(201,200,199,198,197,196,195,194,193,192,191,190)
d2022 <- c(204,203,202,201,200,199,198,197,196,195,194,193)

second <- data.frame(indcode1,area1,ownership1,d2020,d2021,d2022)
``````

### >Solution :

Well this works, but its hard coded explicitly for the year 2022 that you asked about, but I’m guessing you maybe have corrections for other years too?

``````library(tidyverse)
library(zoo)

second %>%
left_join(First,join_by(indcode, area, ownership)) %>%
mutate(d2022 = d2022.x-na.fill(d2022.y,0)) %>%
select(-matches("\\.[xy]\$"))
``````

Data:

``````> second %>%
+     left_join(First,join_by(indcode, area, ownership)) %>%
+     mutate(d2022 = d2022.x-na.fill(d2022.y,0)) %>%
+     select(-matches("\\.[xy]\$"))
+    indcode  area ownership d2020 d2021 d2022
1   482000    32        50   200   201   194
2   482000 29820        50   199   200   197
3   482000 39900        50   198   199   200
4   484000    32        20   197   198   201
5   484000 29820        30   196   197   200
6   484000 39900        50   195   196   199
7   524000    32        50   194   195    98
8   524000 29820        50   193   194   137
9   524000 39900        50   192   193   176
10  531000    32        20   191   192   195
11  531000 29820        30   190   191   194
12  531000 39900        50   189   190   193
``````