For comparison, here is the analogous code from DataFramesMeta.
replace_syms(x, membernames) = x
function replace_syms(e::Expr, membernames)
if e.head == :call && length(e.args) == 2 && e.args[1] == :^
return e.args[2]
elseif e.head == :. # special case for :a.b
return Expr(e.head, replace_syms(e.args[1], membernames),
typeof(e.args[2]) == Expr && e.args[2].head ==
:quote ? e.args[2] : replace_syms(e.args[2], membernames))
elseif e.head != :quote
return Expr(e.head, (isempty(e.args) ? e.args : map(x ->
replace_syms(x, membernames), e.args))...)
else
if haskey(membernames, e.args[1])
return membernames[e.args[1]]
else
a = gensym()
membernames[e.args[1]] = a
return a
end
end
end
function with_helper(d, body)
membernames = Dict{Symbol, Symbol}()
body = replace_syms(body, membernames)
funargs = map(x -> :( getindex($d, $(Meta.quot(x))) ),
collect(keys(membernames)))
funname = gensym()
return(:( function $funname($(collect(values(membernames))...)) $body
end; $funname($(funargs...)) ))
end
macro with(d, body)
esc(with_helper(d, body))
end
On Wednesday, July 8, 2015 at 3:50:12 PM UTC-4, Brandon Taylor wrote:
>
> It should be noted that in R, environments are simply pointers. They take
> up no memory and shouldn't cause drastic slowdowns (I don't think).
>
> On Wednesday, July 8, 2015 at 3:48:08 PM UTC-4, Brandon Taylor wrote:
>>
>> For example, here's a code snippet from R:
>>
>> function (x, data) eval(x$expr, data, x$env)
>>
>> x is an expression which contains an environment attribute. It is
>> evaluated first checking the bindings in data (data being a dataframe) and
>> then checing the bindings in x's environment.
>>
>> On Wednesday, July 8, 2015 at 3:37:04 PM UTC-4, Brandon Taylor wrote:
>>>
>>> *reifying. Deifying environments might not be the best idea.
>>>
>>> On Wednesday, July 8, 2015 at 3:34:53 PM UTC-4, Brandon Taylor wrote:
>>>>
>>>> I was aware of those packages (though I hadn't read the discussions
>>>> referenced). Macros are great but they are incredibly difficult to reason
>>>> with concerning issues of scope (at least for me). Deifying environments
>>>> could solve all of these issues (and so much more) in one fell swoop.
>>>>
>>>> On Wednesday, July 8, 2015 at 3:20:00 PM UTC-4, David Gold wrote:
>>>>>
>>>>> Some of these issues have been thought about fairly extensively by the
>>>>> stats community in particular, precisely on account of the use cases you
>>>>> cite:
>>>>>
>>>>> https://github.com/JuliaStats/DataFrames.jl/pull/472
>>>>> https://github.com/JuliaStats/DataFrames.jl/issues/504
>>>>> <https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FJuliaStats%2FDataFrames.jl%2Fissues%2F504&sa=D&sntz=1&usg=AFQjCNHgUEZP8TyJ_BuUyyFA5SIxneOJTA>
>>>>>
>>>>> I think that the matter is still very much an open question. I have no
>>>>> sense that anything is going to be added to Base Julia itself. Currently,
>>>>> the best way (that I know of, anyway) to achieve the delayed evaluation
>>>>> effect is via the use of macros. See for instance:
>>>>>
>>>>> https://github.com/JuliaStats/DataFramesMeta.jl
>>>>> https://github.com/one-more-minute/Lazy.jl
>>>>>
>>>>> I'm hope somebody else will be able to pop in an give a more thorough
>>>>> answer, but the above may at least be a place to start.
>>>>>
>>>>> On Wednesday, July 8, 2015 at 2:03:45 PM UTC-4, Brandon Taylor wrote:
>>>>>>
>>>>>> Hadley Wickham's lazyeval package in R is pretty cool in that you can
>>>>>> attach an environment to an expression, pass it in and out of functions
>>>>>> with various modifications, and then evaluate the expression within the
>>>>>> original environment (or any other environment that you choose). R in
>>>>>> general has the functions like list2env and list(environment()) that
>>>>>> allow
>>>>>> one to convert an environment into a list and back again (list being the
>>>>>> R
>>>>>> equivalent of a Dict). Are there any plans to add these kind of features
>>>>>> to
>>>>>> Julia?
>>>>>>
>>>>>
