# Metaprogramming Queryverse/Query Pipelines
**URL:** https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032
**Category:** General Usage
**Tags:** metaprogramming, query, piping, queryverse
**Created:** [July 31, 2019, 12:55pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032 "2019-07-31T12:55:09Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 12:55pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/1 "2019-07-31T12:55:09Z")
Hello,
I havenโt done any real metaprogramming yet (other than tinkering around with it) and I have a situation that I think could be a good fit for a metaprogramming solution. Iโm working with a dataset in Queryverse and Iโm running the same pipeline numerous times for different variables so Iโd like to wrap it in a function and then be able to pass in the variable that I want:
```julia
# For example, to filter by age, I would do this:
by_age = naws |>
@groupby(_.AGE) |>
@map( { AGE= key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.AGE) |>
DataFrame
# And to filter by gender, I would do this:
by_gender = naws |>
@groupby(_.GENDER) |>
@map( { GENDER= key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.GENDER) |>
DataFrame
# I tried to do this, but I get an error:
function group_map(user_var::String)
return @eval :(naws |>
@groupby(_.$user_var) |>
@map( { $user_var = key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.$user_var) |>
DataFrame)
end
group_map("AGE")
# Output:
ERROR: UndefVarError: user_var not defined
```
Does anyone know how to make this work?
---
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 1:10pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/2 "2019-07-31T13:10:01Z")
So I have another clue, which is shining a light on how much of a train wreck the above code is ๐ต. When I define a `user_var` variable in the global scope, and then run the function, I get:
```julia
user_var = "AGE"
group_map("AGE")
# Output:
:((((naws |> #= REPL[69]:3 =# @groupby(_.:("AGE"))) |> #= REPL[69]:4 =# @map({"AGE" = key(_), Count = sum(_.PWTYCRD)})) |> #= REPL[69]:5 =# @orderby(_.:("AGE"))) |> DataFrame)
```
So it appears that `@eval` is looking for a global variable when it evaluates `user_var` and itโs not going to be able to get this value from the function argument. It also appears that my function is just returning an expression without actually evaluating itโฆ
Metaprogramming for me is kind of this elusive Shangri-La that I just canโt seem to nail down ๐
---
### Author:  [@zgornel](https://discourse.julialang.org/u/zgornel)
#### Post date: [July 31, 2019, 1:16pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/3 "2019-07-31T13:16:03Z")
Try
```julia
function group_map(user_var::Symbol)
return :(naws |>
@groupby(_.$user_var) |>
@map( { $user_var = key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.$user_var) |>
DataFrame)
end
eval(group_map(:AGE))
```
---
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 1:17pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/4 "2019-07-31T13:17:10Z")
Iโm getting closer to the Shangri-La of metaprogrammingโฆIโve just had a glimpse of it:
```julia
user_var = :(AGE)
@eval begin
naws |>
@groupby(_.$user_var) |>
@map( { $user_var = key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.$user_var) |>
DataFrame
end
# Output:
77ร2 DataFrame
โ Row โ AGE โ Count โ
โ โ Float64โฐ โ Float64 โ
โโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโค
โ 1 โ 14.0 โ 234.899 โ
โ 2 โ 15.0 โ 408.661 โ
โ 3 โ 16.0 โ 882.28 โ
โ 4 โ 17.0 โ 1399.91 โ
โ 5 โ 18.0 โ 2210.2 โ
โ 6 โ 19.0 โ 2410.58 โ
โ 7 โ 20.0 โ 2475.35 โ
โ 8 โ 21.0 โ 2553.29 โ
โ 9 โ 22.0 โ 2291.98 โ
โ 10 โ 23.0 โ 2426.32 โ
โฎ
```
Now, I just need to figure out how to make this work in a function callโฆor if I can figure out how to use metaprogramming to generate functions for all of the variables Iโm interested in, full Nirvana will be achieved and I think Iโll just retire. ๐
---
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 1:20pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/5 "2019-07-31T13:20:43Z")
> [@zgornel](#):
>
> function group\_map(user\_var::Symbol) return :(naws |\> @groupby(_.$user\_var) |\> @map( { $user\_var = key(_), Count = sum(_.PWTYCRD) } ) |\> @orderby(_.$user\_var) |\> DataFrame) end eval(group\_map(:AGE))
This is a nice solution! Iโm wondering if, instead of having to do `eval()`, I can write a macro `@group_map` that would do the same thing? So the final solution would be `@group_map :AGE`, or something like that.
---
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 1:25pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/6 "2019-07-31T13:25:02Z")
So this appears to work:
```julia
function group_map(user_var::Symbol)
@eval begin
naws |>
@groupby(_.$user_var) |>
@map( { $user_var = key(_), Count = sum(_.PWTYCRD) } ) |>
@orderby(_.$user_var) |>
DataFrame
end
end
group_map(:AGE)
# Output:
77ร2 DataFrame
โ Row โ AGE โ Count โ
โ โ Float64โฐ โ Float64 โ
โโโโโโโผโโโโโโโโโโโผโโโโโโโโโโโค
โ 1 โ 14.0 โ 234.899 โ
โ 2 โ 15.0 โ 408.661 โ
โ 3 โ 16.0 โ 882.28 โ
โ 4 โ 17.0 โ 1399.91 โ
โ 5 โ 18.0 โ 2210.2 โ
โ 6 โ 19.0 โ 2410.58 โ
โ 7 โ 20.0 โ 2475.35 โ
โ 8 โ 21.0 โ 2553.29 โ
โ 9 โ 22.0 โ 2291.98 โ
โ 10 โ 23.0 โ 2426.32 โ
โฎ
```
I โค Julia
---
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [July 31, 2019, 1:44pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/7 "2019-07-31T13:44:34Z")
Just in case anyone else is interested in this, Iโve taken it a step further and itโs getting really cool (at least for my use case ๐):
```julia
function filter_group(filter_expr::Expr, group_var::Symbol)
@eval begin
naws |>
@filter($filter_expr) |>
@groupby(_.$group_var) |>
@map( { $group_var = key(_), Estimate = sum(_.PWTYCRD) } ) |>
@orderby(_.$group_var) |>
DataFrame
end
end
# You can then call the function with whatever filtering criteria/grouping variable you need:
filter_group(:(_.FLC == 1.0), :WAGET1)
```
---
### Author:  [@zgornel](https://discourse.julialang.org/u/zgornel)
#### Post date: [July 31, 2019, 1:47pm UTC](https://discourse.julialang.org/t/metaprogramming-queryverse-query-pipelines/27032/8 "2019-07-31T13:47:53Z")
You can most certaily can; should be quite straightforward ๐