英文:
Sum values of two different datasets but following the same date in R
问题
我有两个数据集，分别称为'ro'和'rt'，它们的长度不同，每个数据集中都有一个名为'price'的列和一个名为'date'的列。我想要按相同的日期对价格进行求和。
所以我想创建一个新的数据集，在其中，例如，在日期6/1/22（在两个数据集中都有）中，在'ro'中有20美元，在'rt'中有40美元。新数据集将有另一列日期（6/1/22）和另一列为60美元（这是总和）。
当然，如果没有相同的日期，就不会有任何求和；（在'ro'中我们有日期3/5/22，有90美元，但在'rt'中没有相同的日期，在新数据集中将保持为同一行，不进行求和）。
数据集 'ro'
日期 价格
1 2015-01-17 2
2 2015-01-18 7
3 2015-01-19 1
4 2015-01-11 8
数据集 'rt'
日期 价格
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
新数据集
价格
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
这就是我想要的。
英文:
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
答案1
得分: 4
rbind(ro, rt) |>
aggregate(A ~ Date, data = _, FUN = sum)
# Date A
# 1 2015-01-10 2
# 2 2015-01-11 9
# 3 2015-01-17 3
# 4 2015-01-18 7
# 5 2015-01-19 2
# 6 2015-02-12 5
# 7 2015-04-9 2
英文:
Base R:
rbind(ro, rt) |>
aggregate(A ~ Date, data = _, FUN = sum)
# Date A
# 1 2015-01-10 2
# 2 2015-01-11 9
# 3 2015-01-17 3
# 4 2015-01-18 7
# 5 2015-01-19 2
# 6 2015-02-12 5
# 7 2015-04-9 2
答案2
得分: 3
We could bind the datasets and do a group by sum
library(dplyr) #版本 >= 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
英文:
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
答案3
得分: 3
使用data.table，我们可以使用rbindlist函数：
> library(data.table)
> rbindlist(list(ro, rt))[, .(A = sum(A)), Date]
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
英文:
With data.table we can use rbindlist
> library(data.table)
> rbindlist(list(ro, rt))[, .(A = sum(A)), Date]
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
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