Reducer for non-parallel for loops/multiline comprehensions - loops

Question

Julia has a parallel macro for for loops, which allows things like:
s = #sync #parallel vcat for i in 1:9
k = iseven(i) ? i÷2 : 3i+1
k^2
end
and since the reducer specified is vcat, we get back an array of numbers.
Is it possible to do something like this with a normal for loop (without having to explicitly initialize and push! into the array)?
Since I'm only looking to reduce using vcat, another way to ask this question is: is there a neat readable multiline form of array comprehensions? It's possible to stretch to usual comprehension syntax like this:
s = [
(k = iseven(i) ? i÷2 : 3i+1;
k^2)
for i in 1:9
]
but that seems messy and less readable compared to the #parallel vcat for syntax. Is there a better way of doing multiline comprehensions?

Answer 1

Extending on #Gnimuc's answer, I think mapreduce plus do-syntax is pretty nice:
julia> mapreduce(vcat, 1:9) do i
k = iseven(i) ? i÷2 : 3i+1
k^2
end
9-element Array{Int64,1}:
16
1
100
4
256
9
484
16
784

Answer 2

The short answer is to write multiline functions(or do-blocks as #phg reminds) with a single line array comprehension or map/mapreduce:
s = [
(k = iseven(i) ? i÷2 : 3i+1;
k^2)
for i in 1:9
]
This example is pure comprehension, no reducer is involved. Array comprehension is usually written in one line, for example, s = [iseven(i) ? i÷2 : 3i+1 |> x->x^2 for i in 1:9]. As #phg suggested, multi-line functions can be enclosed in a do-block:
julia> map(1:9) do x
k = iseven(x) ? x÷2 : 3x+1
k^2
end
However, no reducer such as vcat is needed in this case, but if the output of f in the above example is a vector:
julia> function f(x)
k = iseven(x) ? x÷2 : 3x+1
[k^2]
end
f (generic function with 1 method)
julia> s = [f(i) for i in 1:9]
9-element Array{Array{Int64,1},1}:
[16]
[1]
[100]
[4]
[256]
[9]
[484]
[16]
[784]
array comprehension will give you an array of vectors. This time you need to use mapreduce instead:
julia> mapreduce(f, vcat, 1:9)
9-element Array{Int64,1}:
16
1
100
4
256
9
484
16
784