nth function/method for groupby

An `nth` function, similar to [pandas groupby nth](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.nth.html), where the selected row/rows stay as part of the new aggregations in the presence of `by`:

```
from datatable import dt, f, by

data = {'x': [1, 1, 1, 2, 2, 3],
'y': [1, 2, 3, 1, 2, 1],
'n': [3, 2, 1, 1, 2, 1]}

DT = dt.Frame(data)

DT

| x y n
| int32 int32 int32
-- + ----- ----- -----
0 | 1 1 3
1 | 1 2 2
2 | 1 3 1
3 | 2 1 1
4 | 2 2 2
5 | 3 1 1
[6 rows x 3 columns]
```

The `nth` function/method should return the row or rows along with other aggregations:

```
DT[:, {'sum': f.y.sum(), 'nth' : f.y.nth(1)}, 'x']

| x sum nth
| int32 int64 int32
-- + ----- ----- -----
0 | 1 6 2
1 | 2 3 2
2 | 3 1 NA
[3 rows x 3 columns]

```
One way to currently implement this would be to do a `cbind`:

``dt.cbind(DT[:, {"sum": f.y.sum()}, "x"],
DT[1, 'n', by('x', add_columns=False)],
force=True)``

which might not properly align; a much safer option would be a left join.

The implementation in #2176 selects a single row or slice; and since within datatable `i` is executed before `j`, the results wont come out right. Also, at the moment, sequences are not accepted within the `i`; it would be nice to be able to select lists with the `nth` function