## GroupedArrays.jl

Author FixedEffects
Popularity
0 Stars
Updated Last
1 Year Ago
Started In
July 2021

## Installation

The package is registered in the `General` registry and so can be installed at the REPL with

`] add GroupedArrays`.

## Introduction

GroupedArray is an AbstractArray that contains positive integers or missing values.

• `GroupedArray(x::AbstractArray)` returns a `GroupedArray` of the same length as the original array, where each distinct value is encoded as a distinct integer.
• `GroupedArray(xs...::AbstractArray)` returns a `GroupedArray` where each distinct combination of values is encoded as a distinct integer
• By default (with `coalesce = false`), `GroupedArray` encodes `missing` values as a distinct `missing` category. With `coalesce = true`, missing values are treated similarly to other values.

## Examples

```using GroupedArrays
p = repeat(["a", "b", missing], outer = 2)
GroupedArray(p)
# 6-element GroupedArray{Int64, 1}:
#  1
#  2
#   missing
#  1
#  2
#   missing
p = repeat(["a", "b", missing], outer = 2)
GroupedArray(p; coalesce = true)
# 6-element GroupedArray{Int64, 1}:
#  1
#  2
#  3
#  1
#  2
#  3
p1 = repeat(["a", "b"], outer = 3)
p2 = repeat(["d", "e"], inner = 3)
GroupedArray(p1, p2)
# 6-element GroupedArray{Int64, 1}:
#  1
#  2
#  1
#  3
#  4
#  3```

## Relation to other packages

• `GroupedArray` is similar to `PooledArray`, except that the pool is simply the set of integers from 1 to n where n is the number of groups(`missing` is encoded as 0). This allows for faster lookup in setups where the group value is not meaningful.
• The algorithm to group multiple vectors is taken from DataFrames.jl