This package allows computing one- or multi-way frequency tables (a.k.a. contingency or pivot tables) from
any type of vector or array. It includes support for CategoricalArray
and Tables.jl compliant objects, as well as for weighted counts.
Tables are represented as NamedArray objects.
julia> using FreqTables
julia> x = repeat(["a", "b", "c", "d"], outer=[100]);
julia> y = repeat(["A", "B", "C", "D"], inner=[10], outer=[10]);
julia> tbl = freqtable(x)
4-element Named Array{Int64,1}
Dim1 │
──────┼────
a │ 100
b │ 100
c │ 100
d │ 100
julia> prop(tbl)
4-element Named Array{Float64,1}
Dim1 │
──────┼─────
a │ 0.25
b │ 0.25
c │ 0.25
d │ 0.25
julia> freqtable(x, y)
4×4 Named Array{Int64,2}
Dim1 ╲ Dim2 │ A B C D
────────────┼───────────────
a │ 30 20 30 20
b │ 30 20 30 20
c │ 20 30 20 30
d │ 20 30 20 30
julia> tbl2 = freqtable(x, y, subset=1:20)
4×2 Named Array{Int64,2}
Dim1 ╲ Dim2 │ A B
────────────┼─────
a │ 3 2
b │ 3 2
c │ 2 3
d │ 2 3
julia> prop(tbl2, margins=2)
4×2 Named Array{Float64,2}
Dim1 ╲ Dim2 │ A B
────────────┼─────────
a │ 0.3 0.2
b │ 0.3 0.2
c │ 0.2 0.3
d │ 0.2 0.3
julia> freqtable(x, y, subset=1:20, weights=repeat([1, .5], outer=[10]))
4×2 Named Array{Float64,2}
Dim1 ╲ Dim2 │ A B
────────────┼─────────
a │ 3.0 2.0
b │ 1.5 1.0
c │ 2.0 3.0
d │ 1.0 1.5For convenience, when working with tables (like e.g. a DataFrame) one can pass a table object and columns as symbols:
julia> using DataFrames, CSV
julia> iris = DataFrame(CSV.File(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv")));
julia> iris.LongSepal = iris.SepalLength .> 5.0;
julia> freqtable(iris, :Species, :LongSepal)
3×2 Named Array{Int64,2}
Species ╲ LongSepal │ false true
────────────────────┼─────────────
setosa │ 28 22
versicolor │ 3 47
virginica │ 1 49
julia> freqtable(iris, :Species, :LongSepal, subset=iris.PetalLength .< 4.0)
2×2 Named Array{Int64,2}
Species ╲ LongSepal │ false true
────────────────────┼─────────────
setosa │ 28 22
versicolor │ 3 8Note that when one of the input variables contains integers, Name(i) has to be used
when indexing into the table to prevent i to be interpreted as a numeric index:
julia> df = DataFrame(A = 101:103, B = ["x","y","y"]);
julia> ft = freqtable(df, :A, :B)
3×2 Named Array{Int64,2}
Dim1 ╲ Dim2 │ x y
────────────┼─────
101 │ 1 0
102 │ 0 1
103 │ 0 1
julia> ft[Name(101), "x"]
1
julia> ft[101,"x"]
ERROR: BoundsError: attempt to access 10×2 Array{Int64,2} at index [101, 1]