[英]R dplyr full_join - no common key, need common columns to blend together
I have these two dataframes for example:例如,我有这两个数据框:
dates = c('2020-11-19', '2020-11-20', '2020-11-21')
df1 <- data.frame(dates, area = c('paris', 'london', 'newyork'),
rating = c(10, 5, 6),
rating2 = c(5, 6, 7))
df2 <- data.frame(dates, area = c('budapest', 'moscow', 'valencia'),
rating = c(1, 2, 1))
> df1
dates area rating rating2
1 2020-11-19 paris 10 5
2 2020-11-20 london 5 6
3 2020-11-21 newyork 6 7
> df2
dates area rating
1 2020-11-19 budapest 1
2 2020-11-20 moscow 2
3 2020-11-21 valencia 1
When performing an outer join using dplyr:使用 dplyr 执行外连接时:
df <- df1 %>%
full_join(df2, by = c('dates', 'area'))
the result is like this:结果是这样的:
dates area rating.x rating2 rating.y
1 2020-11-19 paris 10 5 NA
2 2020-11-20 london 5 6 NA
3 2020-11-21 newyork 6 7 NA
4 2020-11-19 budapest NA NA 1
5 2020-11-20 moscow NA NA 2
6 2020-11-21 valencia NA NA 1
ie the rating columns from the two dataframes are not blended together but two separate columns are created.即来自两个数据框的评级列没有混合在一起,而是创建了两个单独的列。
How do I get a result like this?我怎样才能得到这样的结果?
dates area rating rating2
1 2020-11-19 paris 10 5
2 2020-11-20 london 5 6
3 2020-11-21 newyork 6 7
4 2020-11-19 budapest 1 NA
5 2020-11-20 moscow 2 NA
6 2020-11-21 valencia 1 NA
What you're looking for is dplyr::bind_rows()
, which will preserve common columns and fill NA
for columns that only exist in one of the data frames:您正在寻找的是
dplyr::bind_rows()
,它将保留公共列并为仅存在于其中一个数据框中的列填充NA
:
> bind_rows(df1, df2)
dates area rating rating2
1 2020-11-19 paris 10 5
2 2020-11-20 london 5 6
3 2020-11-21 newyork 6 7
4 2020-11-19 budapest 1 NA
5 2020-11-20 moscow 2 NA
6 2020-11-21 valencia 1 NA
Note that you could also continue using full_join()
- but you must ensure that all common columns between the data frames are included as keys if you don't want columns to be split:请注意,您也可以继续使用
full_join()
- 但如果您不希望列被拆分,则必须确保数据框之间的所有公共列都作为键包含:
> full_join(
+ df1, df2,
+ by = c("dates", "area", "rating")
+ )
dates area rating rating2
1 2020-11-19 paris 10 5
2 2020-11-20 london 5 6
3 2020-11-21 newyork 6 7
4 2020-11-19 budapest 1 NA
5 2020-11-20 moscow 2 NA
6 2020-11-21 valencia 1 NA
The documentation for dplyr joins mentions: dplyr 的文档加入提到:
Output columns include all
x
columns and ally
columns.Output 列包括所有
x
列和所有y
列。 If columns inx
andy
have the same name (and aren't included inby
), suffixes are added to disambiguate.如果
x
和y
中的列具有相同的名称(并且不包含在by
中),则添加后缀以消除歧义。
You could also avoid this issue by not specifying by
, in which case dplyr will use all common columns.您也可以通过不指定
by
来避免此问题,在这种情况下 dplyr 将使用所有常用列。
> full_join(df1, df2)
Joining, by = c("dates", "area", "rating")
dates area rating rating2
1 2020-11-19 paris 10 5
2 2020-11-20 london 5 6
3 2020-11-21 newyork 6 7
4 2020-11-19 budapest 1 NA
5 2020-11-20 moscow 2 NA
6 2020-11-21 valencia 1 NA
As far as I know, both methods are good for your use case.据我所知,这两种方法都适合您的用例。 In fact, I believe that the practical advantage
full_join()
has over bind_rows()
is precisely this behaviour you wanted to avoid here, ie splitting columns that aren't keys.事实上,我相信
full_join()
相对于bind_rows()
的实际优势正是您希望在此处避免的这种行为,即拆分不是键的列。
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