R合并数据集未按预期工作（多对多）
[英]R Merge datasets not working as expected (many-to-many)
问题描述
I would like to merge two datasets that share some of the same variables to have one dataset with variables 'EF' and 'LF' appropriately assigned. 
我想合并两个共享一些相同变量的数据集，以使一个数据集具有适当分配的变量“ EF”和“ LF”。 However, for one of the variables ('Item'), the two datasets different in degree of complexity. 但是，对于其中一个变量（“项目”），两个数据集的复杂程度不同。 I have provided both datasets below (which themselves are both subsets of larger datasets). 我在下面提供了两个数据集（它们本身都是较大数据集的子集）。
It seems that the merge is happening correctly, but the output is not what I was expecting. 似乎合并正确进行，但是输出不是我期望的。 Specifically (as an example), for 'Meat' the level names that result for 'Item.x' do not match that for 'Item.y'. 具体来说（例如），对于“肉”而言，“ Item.x”的级别名称与“ Item.y”的级别名称不匹配。 I would only keep one of the 'Item' columns after the merge is complete, but I wanted to check that the output for 'Item.y' is correct for the concept before ignoring (and deleting) 'Item.x'. 合并完成后，我只会保留“ Item”列中的一列，但是我想在忽略（并删除）“ Item.x”之前检查“ Item.y”的输出对于该概念是否正确。
From previous assistance and searching, I have tried various versions of the following code to merge df1 and df2: 在以前的帮助和搜索中，我尝试了以下代码的各种版本来合并df1和df2：
merge.df <- merge(df1, df2, by=c("Region", "Group"), all=T)
For example, changing the order of df1 and df2 in the command, or adding "Item" into the 'by' function provide output that is entirely incorrect. 例如，更改命令中df1和df2的顺序，或在“ by”函数中添加“ Item”，将提供完全不正确的输出。
What am I missing? 我想念什么？
Thanks! 谢谢！
INPUT df1 输入df1
Region Group Item Stage LF
Europe Fruit.Veg FSC1 20
Europe Fruit.Veg FSC2 5
Europe Fruit.Veg FSC3 2
Europe Fruit.Veg FSC4f 5.8935
Europe Fruit.Veg FSC4p 2
Europe Fruit.Veg FSC5f 19
Europe Fruit.Veg FSC5p 15
Europe Meat Bovine FSC1 2.3
Europe Meat Mutton FSC1 10
Europe Meat Goat FSC1 10
Europe Meat Pig FSC1 2.5
Europe Meat Poultry FSC1 7
Europe Meat Bovine FSC2 0.613
Europe Meat Mutton FSC2 0.618
Europe Meat Goat FSC2 0.618
Europe Meat Pig FSC2 0.23
Europe Meat Poultry FSC2 1.765
Europe Meat FSC3 5
Europe Meat FSC4 4.033333333
Europe Meat FSC5 11
Europe Marine FSC1 9.4
Europe Marine FSC2 0.5
Europe Marine FSC3 6
Europe Marine FSC4f 9
Europe Marine FSC4p 5
Europe Marine FSC5f 11
Europe Marine FSC5p 10
Europe Milk.Eggs Milk FSC1 3.5
Europe Milk.Eggs Milk FSC2 0.5
Europe Milk.Eggs Milk FSC3 1.2
Europe Milk.Eggs Milk FSC4 0.713333333
Europe Milk.Eggs Milk FSC5 7
Europe Milk.Eggs Eggs FSC1 4
Europe Milk.Eggs Eggs FSC2 0
Europe Milk.Eggs Eggs FSC3 0.5
Europe Milk.Eggs Eggs FSC4 2
Europe Milk.Eggs Eggs FSC5 8
df2 df2
Region Sub-Region Group Item EF
Europe Europe Fruit.Veg Apples 0.227686081
Europe Europe Fruit.Veg Bananas 0.35
Europe Europe Fruit.Veg Citrus 0.344375
Europe Europe Fruit.Veg Grapes 0.41
Europe Europe Fruit.Veg Fruit.Other 2.029212613
Europe Europe Fruit.Veg Vegetables 0.917924249
Europe Europe Meat Bovine 22.68119086
Europe Europe Meat Mutton 20.99167461
Europe Europe Meat Goat 18.19083333
Europe Europe Meat Pig 5.04123312
Europe Europe Meat Poultry 3.712
Europe Europe Marine Fish.Seafood 4.117510463
Europe Europe Milk.Eggs Milk 1.308859756
Europe Europe Milk.Eggs Eggs 3.83
OUTPUT merge.df 输出merge.df
Region Group Item.x Stage LF Sub.Region Item.y EF
Europe Fruit.Veg FSC1 20 Europe Apples 0.227686081
Europe Fruit.Veg FSC1 20 Europe Bananas 0.35
Europe Fruit.Veg FSC1 20 Europe Citrus 0.344375
Europe Fruit.Veg FSC1 20 Europe Grapes 0.41
Europe Fruit.Veg FSC1 20 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC1 20 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC2 5 Europe Apples 0.227686081
Europe Fruit.Veg FSC2 5 Europe Bananas 0.35
Europe Fruit.Veg FSC2 5 Europe Citrus 0.344375
Europe Fruit.Veg FSC2 5 Europe Grapes 0.41
Europe Fruit.Veg FSC2 5 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC2 5 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC3 2 Europe Apples 0.227686081
Europe Fruit.Veg FSC3 2 Europe Bananas 0.35
Europe Fruit.Veg FSC3 2 Europe Citrus 0.344375
Europe Fruit.Veg FSC3 2 Europe Grapes 0.41
Europe Fruit.Veg FSC3 2 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC3 2 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC4f 5.8935 Europe Apples 0.227686081
Europe Fruit.Veg FSC4f 5.8935 Europe Bananas 0.35
Europe Fruit.Veg FSC4f 5.8935 Europe Citrus 0.344375
Europe Fruit.Veg FSC4f 5.8935 Europe Grapes 0.41
Europe Fruit.Veg FSC4f 5.8935 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC4f 5.8935 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC4p 2 Europe Apples 0.227686081
Europe Fruit.Veg FSC4p 2 Europe Bananas 0.35
Europe Fruit.Veg FSC4p 2 Europe Citrus 0.344375
Europe Fruit.Veg FSC4p 2 Europe Grapes 0.41
Europe Fruit.Veg FSC4p 2 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC4p 2 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC5f 19 Europe Apples 0.227686081
Europe Fruit.Veg FSC5f 19 Europe Bananas 0.35
Europe Fruit.Veg FSC5f 19 Europe Citrus 0.344375
Europe Fruit.Veg FSC5f 19 Europe Grapes 0.41
Europe Fruit.Veg FSC5f 19 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC5f 19 Europe Vegetables 0.917924249
Europe Fruit.Veg FSC5p 15 Europe Apples 0.227686081
Europe Fruit.Veg FSC5p 15 Europe Bananas 0.35
Europe Fruit.Veg FSC5p 15 Europe Citrus 0.344375
Europe Fruit.Veg FSC5p 15 Europe Grapes 0.41
Europe Fruit.Veg FSC5p 15 Europe Fruit.Other 2.029212613
Europe Fruit.Veg FSC5p 15 Europe Vegetables 0.917924249
Europe Marine FSC1 9.4 Europe Fish.Seafood 4.117510463
Europe Marine FSC2 0.5 Europe Fish.Seafood 4.117510463
Europe Marine FSC3 6 Europe Fish.Seafood 4.117510463
Europe Marine FSC4f 9 Europe Fish.Seafood 4.117510463
Europe Marine FSC4p 5 Europe Fish.Seafood 4.117510463
Europe Marine FSC5f 11 Europe Fish.Seafood 4.117510463
Europe Marine FSC5p 10 Europe Fish.Seafood 4.117510463
Europe Meat Bovine FSC1 2.3 Europe Bovine 22.68119086
Europe Meat Bovine FSC1 2.3 Europe Mutton 20.99167461
Europe Meat Bovine FSC1 2.3 Europe Goat 18.19083333
Europe Meat Bovine FSC1 2.3 Europe Pig 5.04123312
Europe Meat Bovine FSC1 2.3 Europe Poultry 3.712
Europe Meat Mutton FSC1 10 Europe Bovine 22.68119086
Europe Meat Mutton FSC1 10 Europe Mutton 20.99167461
Europe Meat Mutton FSC1 10 Europe Goat 18.19083333
Europe Meat Mutton FSC1 10 Europe Pig 5.04123312
Europe Meat Mutton FSC1 10 Europe Poultry 3.712
Europe Meat Goat FSC1 10 Europe Bovine 22.68119086
Europe Meat Goat FSC1 10 Europe Mutton 20.99167461
Europe Meat Goat FSC1 10 Europe Goat 18.19083333
Europe Meat Goat FSC1 10 Europe Pig 5.04123312
Europe Meat Goat FSC1 10 Europe Poultry 3.712
Europe Meat Pig FSC1 2.5 Europe Bovine 22.68119086
Europe Meat Pig FSC1 2.5 Europe Mutton 20.99167461
Europe Meat Pig FSC1 2.5 Europe Goat 18.19083333
Europe Meat Pig FSC1 2.5 Europe Pig 5.04123312
Europe Meat Pig FSC1 2.5 Europe Poultry 3.712
Europe Meat Poultry FSC1 7 Europe Bovine 22.68119086
Europe Meat Poultry FSC1 7 Europe Mutton 20.99167461
Europe Meat Poultry FSC1 7 Europe Goat 18.19083333
Europe Meat Poultry FSC1 7 Europe Pig 5.04123312
Europe Meat Poultry FSC1 7 Europe Poultry 3.712
Europe Meat Bovine FSC2 0.613 Europe Bovine 22.68119086
Europe Meat Bovine FSC2 0.613 Europe Mutton 20.99167461
Europe Meat Bovine FSC2 0.613 Europe Goat 18.19083333
Europe Meat Bovine FSC2 0.613 Europe Pig 5.04123312
Europe Meat Bovine FSC2 0.613 Europe Poultry 3.712
Europe Meat Mutton FSC2 0.618 Europe Bovine 22.68119086
Europe Meat Mutton FSC2 0.618 Europe Mutton 20.99167461
Europe Meat Mutton FSC2 0.618 Europe Goat 18.19083333
Europe Meat Mutton FSC2 0.618 Europe Pig 5.04123312
Europe Meat Mutton FSC2 0.618 Europe Poultry 3.712
Europe Meat Goat FSC2 0.618 Europe Bovine 22.68119086
Europe Meat Goat FSC2 0.618 Europe Mutton 20.99167461
Europe Meat Goat FSC2 0.618 Europe Goat 18.19083333
Europe Meat Goat FSC2 0.618 Europe Pig 5.04123312
Europe Meat Goat FSC2 0.618 Europe Poultry 3.712
Europe Meat Pig FSC2 0.23 Europe Bovine 22.68119086
Europe Meat Pig FSC2 0.23 Europe Mutton 20.99167461
Europe Meat Pig FSC2 0.23 Europe Goat 18.19083333
Europe Meat Pig FSC2 0.23 Europe Pig 5.04123312
Europe Meat Pig FSC2 0.23 Europe Poultry 3.712
Europe Meat Poultry FSC2 1.765 Europe Bovine 22.68119086
Europe Meat Poultry FSC2 1.765 Europe Mutton 20.99167461
Europe Meat Poultry FSC2 1.765 Europe Goat 18.19083333
Europe Meat Poultry FSC2 1.765 Europe Pig 5.04123312
Europe Meat Poultry FSC2 1.765 Europe Poultry 3.712
Europe Meat FSC3 5 Europe Bovine 22.68119086
Europe Meat FSC3 5 Europe Mutton 20.99167461
Europe Meat FSC3 5 Europe Goat 18.19083333
Europe Meat FSC3 5 Europe Pig 5.04123312
Europe Meat FSC3 5 Europe Poultry 3.712
Europe Meat FSC4 4.033333333 Europe Bovine 22.68119086
Europe Meat FSC4 4.033333333 Europe Mutton 20.99167461
Europe Meat FSC4 4.033333333 Europe Goat 18.19083333
Europe Meat FSC4 4.033333333 Europe Pig 5.04123312
Europe Meat FSC4 4.033333333 Europe Poultry 3.712
Europe Meat FSC5 11 Europe Bovine 22.68119086
Europe Meat FSC5 11 Europe Mutton 20.99167461
Europe Meat FSC5 11 Europe Goat 18.19083333
Europe Meat FSC5 11 Europe Pig 5.04123312
Europe Meat FSC5 11 Europe Poultry 3.712
Europe Milk.Eggs Milk FSC1 3.5 Europe Milk 1.308859756
Europe Milk.Eggs Milk FSC1 3.5 Europe Eggs 3.83
Europe Milk.Eggs Milk FSC2 0.5 Europe Milk 1.308859756
Europe Milk.Eggs Milk FSC2 0.5 Europe Eggs 3.83
Europe Milk.Eggs Milk FSC3 1.2 Europe Milk 1.308859756
Europe Milk.Eggs Milk FSC3 1.2 Europe Eggs 3.83
Europe Milk.Eggs Milk FSC4 0.713333333 Europe Milk 1.308859756
Europe Milk.Eggs Milk FSC4 0.713333333 Europe Eggs 3.83
Europe Milk.Eggs Milk FSC5 7 Europe Milk 1.308859756
Europe Milk.Eggs Milk FSC5 7 Europe Eggs 3.83
Europe Milk.Eggs Eggs FSC1 4 Europe Milk 1.308859756
Europe Milk.Eggs Eggs FSC1 4 Europe Eggs 3.83
Europe Milk.Eggs Eggs FSC2 0 Europe Milk 1.308859756
Europe Milk.Eggs Eggs FSC2 0 Europe Eggs 3.83
Europe Milk.Eggs Eggs FSC3 0.5 Europe Milk 1.308859756
Europe Milk.Eggs Eggs FSC3 0.5 Europe Eggs 3.83
Europe Milk.Eggs Eggs FSC4 2 Europe Milk 1.308859756
Europe Milk.Eggs Eggs FSC4 2 Europe Eggs 3.83
Europe Milk.Eggs Eggs FSC5 8 Europe Milk 1.308859756
Europe Milk.Eggs Eggs FSC5 8 Europe Eggs 3.83
1 个解决方案
解决方案1
0 已采纳 2015-08-22 20:24:38
When you're merging on just region and group, you are doing a many-to-many merge. 当您仅合并区域和组时，您在进行多对多合并。 So for example, for Europe and Fruit.Veg, you have 7 records in the first df and 6 in the second, so you end up with 42 (7*6) records in your merged table - this is the correct behavior for a many-to-many merge. 例如，对于Europe和Fruit.Veg，您在第一个df中有7个记录，在第二个df中有6个记录，因此最终在合并表中有42个（7 * 6）记录-这是许多情况下的正确行为多对多合并。
It seems like what you want is to include Item in the by statement of the merge, however it may not be giving you the behavior you expect because Item is missing in some of your df1 records. 似乎您想要在合并的by语句中包含Item，但是由于某些df1记录中缺少Item，它可能无法提供预期的行为。 If that is not the case, could you explain what the incorrect behavior is when you add Item into the merge statement. 如果不是这种情况，您能否解释将Item添加到merge语句中时的错误行为。
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