# Counting Functions

The package provides functions to count the occurrences of distinct values.

## Counting over an Integer Range

`StatsBase.counts`

— Function```
counts(x, [wv::AbstractWeights])
counts(x, levels::UnitRange{<:Integer}, [wv::AbstractWeights])
counts(x, k::Integer, [wv::AbstractWeights])
```

Count the number of times each value in `x`

occurs. If `levels`

is provided, only values falling in that range will be considered (the others will be ignored without raising an error or a warning). If an integer `k`

is provided, only values in the range `1:k`

will be considered.

If a weighting vector `wv`

is specified, the sum of the weights is used rather than the raw counts.

The output is a vector of length `length(levels)`

.

`StatsBase.proportions`

— Function`proportions(x, levels=span(x), [wv::AbstractWeights])`

Return the proportion of values in the range `levels`

that occur in `x`

. Equivalent to `counts(x, levels) / length(x)`

. If a weighting vector `wv`

is specified, the sum of the weights is used rather than the raw counts.

`proportions(x, k::Integer, [wv::AbstractWeights])`

Return the proportion of integers in 1 to `k`

that occur in `x`

.

`StatsBase.addcounts!`

— Method`addcounts!(r, x, levels::UnitRange{<:Int}, [wv::AbstractWeights])`

Add the number of occurrences in `x`

of each value in `levels`

to an existing array `r`

. If a weighting vector `wv`

is specified, the sum of weights is used rather than the raw counts.

## Counting over arbitrary distinct values

`StatsBase.countmap`

— Function```
countmap(x; alg = :auto)
countmap(x::AbstractVector, w::AbstractVector{<:Real}; alg = :auto)
```

Return a dictionary mapping each unique value in `x`

to its number of occurrences. A vector of weights `w`

can be provided when `x`

is a vector.

`:auto`

(default): if`StatsBase.radixsort_safe(eltype(x)) == true`

then use`:radixsort`

, otherwise use`:dict`

.`:radixsort`

: if`radixsort_safe(eltype(x)) == true`

then use the radix sort algorithm to sort the input vector which will generally lead to shorter running time. However the radix sort algorithm creates a copy of the input vector and hence uses more RAM. Choose`:dict`

if the amount of available RAM is a limitation.`:dict`

: use`Dict`

-based method which is generally slower but uses less RAM and is safe for any data type.

`StatsBase.proportionmap`

— Function`proportionmap(x)`

Return a dictionary mapping each unique value in `x`

to its proportion in `x`

.

`StatsBase.addcounts!`

— Method`addcounts!(dict, x[, wv]; alg = :auto)`

Add counts based on `x`

to a count map. New entries will be added if new values come up. If a weighting vector `wv`

is specified, the sum of the weights is used rather than the raw counts.

`alg`

can be one of:

`:auto`

(default): if`StatsBase.radixsort_safe(eltype(x)) == true`

then use`:radixsort`

, otherwise use`:dict`

.`:radixsort`

: if`radixsort_safe(eltype(x)) == true`

then use the radix sort algorithm to sort the input vector which will generally lead to shorter running time. However the radix sort algorithm creates a copy of the input vector and hence uses more RAM. Choose`:dict`

if the amount of available RAM is a limitation.`:dict`

: use`Dict`

-based method which is generally slower but uses less RAM and is safe for any data type.