ForPairs

Import Weave.ForPairs from the Weave module.

local Weave = require(ReplicatedStorage.Weave)
local ForPairs = Weave.ForPairs

ForPairs transforms each key and values from a table into an Instance

local itemColors = Value.new({ shoes = Color3.new(0, 0, 0), socks = Color3.new(1, 1, 1) })

ForPairs(itemColors, function(thing, color)
    return thing, Attach(TextLabel:Clone()) {
        Text = thing,
        BackgroundColor3 = color,
        Parent = screenGui
    }
end)

owner:set({ shirt = Color3.new(1, 0, 0) })

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Usage

ForPairs functions takes

1. A Weave Computed or Value table.

2. A function that returns an Instance

local itemColors = Value.new({ shoes = Color3.new(0, 0, 0), socks = Color3.new(1, 1, 1) })

ForPairs(itemColors, makeItemFrame)

Where someFunction:

• Receives a value from the table

• Returns an any, Instance

The any is a unique identifier for the Instance (usually the key)

ForPairs(playerNames, function(thing, name)
    local textLabel = TextLabel:Clone()

    return thing, Attach(textLabel) {
        Text = playerName,
        Parent = ScreenGui
    }
end)

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Advanced: Optimizations

Optional

You don't have to memorise these optimisations to use ForPairs, but it can be helpful if you have a performance problem.

Rather than creating a new output table from scratch every time the input table is changed, ForPairs will try and reuse as much as possible to improve performance.

Since ForPairs has to depend on both keys and values, changing any value in the input table will cause a recalculation for that key-value pair.

Inversely, ForPairs won't recalculate any key-value pairs that stay the same. Instead, these will be preserved in the output table.

If you don't need the keys or the values, Fusion can offer better optimisations. For example, if you're working with an array of values where position doesn't matter, ForValues can move values between keys.

Alternatively, if you're working with a set of objects stored in keys, and don't need the values in the table, ForPairs will ignore the values for optimal performance.