diff --git a/docs/src/man/working_with_dataframes.md b/docs/src/man/working_with_dataframes.md
index e65d0ab03..a74298c18 100755
--- a/docs/src/man/working_with_dataframes.md
+++ b/docs/src/man/working_with_dataframes.md
@@ -812,14 +812,21 @@ julia> df = DataFrame(A=1:4, B=4.0:-1.0:1.0)
    3 │     3      2.0
    4 │     4      1.0
 
-julia> combine(df, names(df) .=> sum)
+julia> combine(df, All() .=> sum)
 1×2 DataFrame
  Row │ A_sum  B_sum
      │ Int64  Float64
 ─────┼────────────────
    1 │    10     10.0
 
-julia> combine(df, names(df) .=> sum, names(df) .=> prod)
+julia> combine(df, All() .=> sum, All() .=> prod)
+1×4 DataFrame
+ Row │ A_sum  B_sum    A_prod  B_prod
+     │ Int64  Float64  Int64   Float64
+─────┼─────────────────────────────────
+   1 │    10     10.0      24     24.0
+
+julia> combine(df, All() .=> [sum prod]) # the same using 2-dimensional broadcasting
 1×4 DataFrame
  Row │ A_sum  B_sum    A_prod  B_prod
      │ Int64  Float64  Int64   Float64
@@ -830,6 +837,90 @@ julia> combine(df, names(df) .=> sum, names(df) .=> prod)
 If you would prefer the result to have the same number of rows as the source
 data frame, use `select` instead of `combine`.
 
+In the remainder of this section we will discuss more advanced topics related
+to the operation specification syntax, so you may decide to skip them if you
+want to focus on the most common usage patterns.
+
+A `DataFrame` can store values of any type as its columns, for example
+below we show how one can store a `Tuple`:
+
+```
+julia> df2 = combine(df, All() .=> extrema)
+1×2 DataFrame
+ Row │ A_extrema  B_extrema
+     │ Tuple…     Tuple…
+─────┼───────────────────────
+   1 │ (1, 4)     (1.0, 4.0)
+```
+
+Later you might want to expand the tuples into separate columns storing the computed
+minima and maxima. This can be achieved by passing multiple columns for the output.
+Here is an example of how this can be done by writing the column names by-hand for a single
+input column:
+
+```
+julia> combine(df2, "A_extrema" => identity => ["A_min", "A_max"])
+1×2 DataFrame
+ Row │ A_min  A_max
+     │ Int64  Int64
+─────┼──────────────
+   1 │     1      4
+```
+
+You can extend it to handling all columns in `df2` using broadcasting:
+
+```
+julia> combine(df2, All() .=> identity .=> [["A_min", "A_max"], ["B_min", "B_max"]])
+1×4 DataFrame
+ Row │ A_min  A_max  B_min    B_max
+     │ Int64  Int64  Float64  Float64
+─────┼────────────────────────────────
+   1 │     1      4      1.0      4.0
+```
+
+This approach works, but can be improved. Instead of writing all the column names
+manually we can instead use a function as a way to specify target column names
+based on source column names:
+
+```
+julia> combine(df2, All() .=> identity .=> c -> first(c) .* ["_min", "_max"])
+1×4 DataFrame
+ Row │ A_min  A_max  B_min    B_max
+     │ Int64  Int64  Float64  Float64
+─────┼────────────────────────────────
+   1 │     1      4      1.0      4.0
+```
+
+Note that in this example we needed to pass `identity` explicitly since with
+`All() => (c -> first(c) .* ["_min", "_max"])` the right-hand side part would be
+treated as a transformation and not as a rule for target column names generation.
+
+You might want to perform the transformation of the source data frame into the result
+we have just shown in one step. This can be achieved with the following expression:
+
+```
+julia> combine(df, All() .=> Ref∘extrema .=> c -> c .* ["_min", "_max"])
+1×4 DataFrame
+ Row │ A_min  A_max  B_min    B_max
+     │ Int64  Int64  Float64  Float64
+─────┼────────────────────────────────
+   1 │     1      4      1.0      4.0
+```
+
+Note that in this case we needed to add a `Ref` call in the `Ref∘extrema` operation specification.
+Without `Ref`, `combine` iterates the contents of the value returned by the operation specification function,
+which in our case is a tuple of numbers, and tries to expand it assuming that each produced value represents one row,
+so one gets an error:
+
+```
+julia> combine(df, All() .=> extrema .=> [c -> c .* ["_min", "_max"]])
+ERROR: ArgumentError: 'Tuple{Int64, Int64}' iterates 'Int64' values,
+which doesn't satisfy the Tables.jl `AbstractRow` interface
+```
+
+Note that we used `Ref` as it is a container that is typically used in DataFrames.jl when one
+wants to store one row, however, in general it could be another iterator (e.g. a tuple).
+
 ## Handling of Columns Stored in a `DataFrame`
 
 Functions that transform a `DataFrame` to produce a