I have a data table and I get duplicated rows when using the summarise function combined with group_by.

I will give a simplified example of my problem. First, I am using group_by and mutate to add the sum of 'value' for each id.

```
dt <- data.table(id = rep(1:5, each=10), cpc = rep((0.1*seq(5)), each=2), value = 1:50)
dt2 <- dt %>%
group_by(id) %>%
mutate(SumValue = sum(value))
Source: local data table [50 x 4]
id cpc value SumValue
1 1 0.1 1 55
2 1 0.1 2 55
3 1 0.2 3 55
4 1 0.2 4 55
5 1 0.3 5 55
6 1 0.3 6 55
7 1 0.4 7 55
8 1 0.4 8 55
9 1 0.5 9 55
10 1 0.5 10 55
.. .. ... ... ...
```

So far, nothing wrong. But after that, when I do group_by for each id,cpc combination and use summarise, the output is not as I expected. The numbers are correct, but there are duplicated rows.

```
dt2 %>%
group_by(id, cpc) %>%
summarise(count = n(), SumValue = SumValue)
Source: local data table [50 x 4]
Groups: id
id cpc count SumValue
1 1 0.1 2 55
2 1 0.1 2 55
3 1 0.2 2 55
4 1 0.2 2 55
5 1 0.3 2 55
6 1 0.3 2 55
7 1 0.4 2 55
8 1 0.4 2 55
9 1 0.5 2 55
10 1 0.5 2 55
.. .. ... ... ...
```

Using unique() gives the desired result, but I suppose that this is not necessary.

```
dt2 %>%
group_by(id, cpc) %>%
summarise(count = n(), SumValue = SumValue) %>%
unique()
Source: local data table [25 x 4]
Groups: id
id cpc count SumValue
1 1 0.1 2 55
2 1 0.2 2 55
3 1 0.3 2 55
4 1 0.4 2 55
5 1 0.5 2 55
6 2 0.1 2 155
7 2 0.2 2 155
8 2 0.3 2 155
9 2 0.4 2 155
10 2 0.5 2 155
.. .. ... ... ...
```

I thought that group_by sets group when add=FALSE, so I don't know why the duplicated rows are emerging.

Answer:

I'm not sure the exact problem, but when you assign a vector to a summarize variable, you usually need pick one element of the vector. So in this case you need to pick which value of "SumValue" to assign to a the summarised "SumValue". I usually just do this with variable[1], but first(variable) might be better. Does this give you the result you're looking for?

```
dt <- data_frame(id = rep(1:5, each=10),
cpc = rep((0.1*seq(5)), each=10),
value = 1:50)
dt2 <- dt %>%
group_by(id) %>%
mutate(SumValue = sum(value))
dt2
dt2 %>%
group_by(id, cpc) %>%
summarise(count = n(), SumValue = first(SumValue))
```

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