afhuertass
Traverse nested list and if the list has two elements apply function
Hi!
I have been dealing with some problem where I have a nested list, I want to keep the nested structure, but if the list is a two elements list, then i want to apply a custom function (I’m dealing with multypolygon coordinates and want to apply some transform to the coordinates’ system)
So far, I came up with something using recursion:
defmodule NiceModule do
def apply_function(nested_list) do
update_in(nested_list, [Access.all()], fn item ->
case item do
[x, y] when is_number(x) and is_number(y) -> fun_fun(x, y)
sublist when is_list(sublist) -> apply_function(sublist)
other -> other
end
end)
end
def fun_fun(x,y) do
{a,b} ={ x+3 , y+2}
[a,b]
end
end
aa = [
[2, 5.0 , [ 1 , 1 ]],
[[13.0, 17.0], [25.0, 0.0], [0, 0]] ,
[ 2 ,2 ]
]
NiceModule.apply_function(aa)
Which I think works, but I’m wondering if I’m doing something anti-elixir or if there is a better way (needless to say, I’m new)
Thanks for your time and answers ![]()
Most Liked
Eiji
If that’s all you need then why not simply use Enum.map/2 instead?
Enum.map(nested_list, fn
[x, y] when is_number(x) and is_number(y) -> fun_fun(x, y)
sublist when is_list(sublist) -> apply_function(sublist)
other -> other
end)
Creating a tuple that is not needed is just waste of time and resources.
defmodule NiceModule do
def apply_function(nested_list) do
Enum.map(nested_list, fn
[x, y] when is_number(x) and is_number(y) -> [x + 3, y + 2]
sublist when is_list(sublist) -> apply_function(sublist)
other -> other
end)
end
end
This code is 40% (6 of 16 LOC) shorter and therefore definitely more readable.
Eiji
Both of course are valid - they are equivalents in this example. Enum.map/1 is just much simpler here. You use update_in/3 together with Access.all/0 in case you have to update a nested (non-recursive) structure, for example:
defmodule NiceModule do
def apply_function(map_with_nested_list) do
update_in(map_with_nested_list, [:aa, Access.all()], &do_apply_function/1)
end
defp do_apply_function([x, y]) when is_number(x) and is_number(y), do: [x + 3, y + 2]
# Pay attention that apply_function/1 call here was changed to do_apply_function1
defp do_apply_function(sublist) when is_list(sublist), do: do_apply_function(sublist)
defp do_apply_function(other), do: other
end
map = %{
# aa in your example becomes a key in map
aa: [
[2, 5.0 , [ 1 , 1 ]],
[[13.0, 17.0], [25.0, 0.0], [0, 0]] ,
[ 2 ,2 ]
]
}
NiceModule.apply_function(map)
However if in above example we may use the value of a map key as same as sublist, so we can use Map.update!/3 instead.
defmodule NiceModule do
def apply_function(map_with_nested_list) do
Map.update!(map_with_nested_list, :aa, fn
# the difference is that specified map value can be a list with 2 numbers (not nested)
[x, y] when is_number(x) and is_number(y) -> [x + 3, y + 2]
sublist when is_list(sublist) -> apply_function(sublist)
# similar case here - we allow everything else as a map value
other -> other
end)
end
end
but nested data does not need to be that simple (nested list or map with nested list), but be more complex. Simply try to find the API that do what you expect in simplest form.
In your example you did not support root list to not be nested or be something else as the item variable is the item in the root list and not the root list itself. To do same in my first example we would need to move pattern-matching to apply_function/1 definition, for example:
defmodule NiceModule do
# the difference is that specified map value can be a list with 2 numbers (not nested)
def apply_function([x, y]) when is_number(x) and is_number(y), do: [x + 3, y + 2]
def apply_function(sublist) when is_list(sublist), do: Enum.map(sublist, &apply_function/1)
# similar case here - we allow everything else as a map value
def apply_function(other), do: other
end
al2o3cr
+1 for the other suggestion about using Enum.map if you’re doing a simple “loop over every element in a list”
Another small nitpick: if you’re representing coordinates, an {x, y} tuple would usually be preferred over a two-element list [x, y]. It’s very slightly more space-efficient, and very very very slightly faster to access y.
That could also make your recursion simpler, since then the rule is “if it’s a tuple, transform it; otherwise recurse”.








