Complete column with group_by and complete

2020-03-23 02:16发布

I've got a little problem using dplyr group_by function. After doing this :

datasetALL %>% group_by(YEAR,Region) %>% summarise(count_number = n()) 

here is the result :

YEAR Region count_number
<int>  <int>        <int>
1   1946      1            2
2   1946      2            3
3   1946      3            1
4   1946      5            1
5   1947      3            1
6   1947      4            1

I would like something like :

YEAR Region count_number
<int>  <int>        <int>
1   1946      1            2
2   1946      2            3
3   1946      3            1
4   1946      5            1
5   1946      4            0 #order is no important
6   1947      1            0
7   1947      2            0
8   1947      3            1
9   1947      4            1
10  1947      5            0

I try to use complete() from tidyr package, but it's not succeeding...

标签: r dplyr tidyr
2条回答
家丑人穷心不美
2楼-- · 2020-03-23 02:55

Using complete from the tidyr package should work. You can find documentation about it here.

What probably happened is that you did not remove the grouping. Then complete tries to add each of the combinations of YEAR and Region within each group. But all these combinations are already in the grouping. Thus first remove the grouping and then do the complete.

datasetALL %>% 
    group_by(YEAR,Region) %>% 
    summarise(count_number = n()) %>%
    ungroup() %>%
    complete(Year, Region, fill = list(count_number = 1))
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等我变得足够好
3楼-- · 2020-03-23 03:14

It has been already mentioned, but you can solve this problem in its entirety by using tidyr and the parameter nesting in it:

complete(df, YEAR, nesting(Region), fill = list(count_number = 0))

    YEAR Region count_number
   <int>  <int>        <dbl>
 1  1946      1            2
 2  1946      2            3
 3  1946      3            1
 4  1946      4            0
 5  1946      5            1
 6  1947      1            0
 7  1947      2            0
 8  1947      3            1
 9  1947      4            1
10  1947      5            0
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