Very slow `@astable` macro outside a function

Here is the experiment.

Given the dataframe and functions f0, f1 below

using DataFrames, DataFramesMeta, StatsBase
df = DataFrame(a=1:10_000)  # I know the df is small but big enough to show the issue
f0(df::DataFrame) = begin
	@chain df begin
		@rtransform(:b = :a * 10)
		@rtransform(:c = mean(:b))
		@rtransform(:d = :b - :c)
		@select(:a, :d)
	end
end
f1(df::DataFrame) = begin
	@chain df begin
		@rtransform @astable begin
			b = :a * 10
			c = mean(b)
			:d = b - c
		end
	end
end

We get an improvement in performance in f1, which is what one would expect given it does not need to create columns b, c .

@time f0(df)
0.001146 seconds (728 allocations: 898.516 KiB)
@time f1(df)
0.000503 seconds (161 allocations: 243.609 KiB)

However, if one uses this code outside a function (see below) it becomes 46 times slower! Making it unusable for datasets of a larger size.

@time @chain df begin
	@rtransform @astable begin
		b = :a * 10
		c = mean(b)
		:d = b - c
	end
end
->  2.331518 seconds (335.93 k allocations: 13.028 MiB, 4.69% compilation time)

@time @chain df begin
	@rtransform(:b = :a * 10)
	@rtransform(:c = mean(:b))
	@rtransform(:d = :b - :c)
	@select(:a, :d)
end
->  0.056910 seconds (34.81 k allocations: 3.137 MiB, 95.06% compilation time)

Thanks for the great work :)

Start by reproducing the two @chain experiments in the issue, comparing the @astable form outside a function with the equivalent chained transformations. Trace the @astable and @rtransform macro expansions to identify why top-level execution allocates and compiles so much more. Done means the top-level form no longer has the reported severe slowdown while preserving the function behavior and output.