I have two datasets called 'ro' and 'rt' with different length, in each dataset we have a column called 'price' and a col called 'date'. I want to sum the prices following the same date. so I would like to create a new dataset in which, for example in date 6/1/22 (which is in both dataset) in 'ro' there's 20$ and in 'rt' there's 40$. the new dataset will have another column with the date (6/1/22) and another column with 60$ (which is the sum)
of course if there's not the same date, there won't be any sum; (in 'ro' we have date 3/5/22 with 90$, but there's not the same date 'rt', in the new dataset will be simply the same row, without any sum)
dataset 'ro'
Date A
1 2015-01-17 2
2 2015-01-18 7
3 2015-01-19 1
4 2015-01-11 8
dataset 'rt'
Date A
1 2015-01-17 1
2 2015-01-10 2
3 2015-01-19 1
4 2015-01-11 1
5 2015-02-12 5
6 2015-04-9 2
new dataset
A
1 2015-01-17 3
2 2015-01-10 2
3 2015-01-19 2
4 2015-01-11 9
5 2015-01-18 7
6 2015-02-12 5
7 2015-04-9 2
this is what I would like
We could bind the datasets and do a group by sum
library(dplyr) #version >= 1.1.0
bind_rows(ro, rt) %>%
reframe(A = sum(A), .by = Date)
-output
Date A
1 2015-01-17 3
2 2015-01-18 7
3 2015-01-19 2
4 2015-01-11 9
5 2015-01-10 2
6 2015-02-12 5
7 2015-04-9 2