In [13]:

    

using Colors
using DataFrames
using Distributions
using Gadfly


    

In [14]:

    

function auc{R <: Real, N <: Integer}(h::Tuple{AbstractVector{R}, AbstractVector{N}})
    auc(h[1], h[2])
end

function auc{R <: Real, N <: Integer}(edges::AbstractVector{R}, counts::AbstractVector{N})
    deltax = edges[2 : end] - edges[1 : end - 1]
    sum(deltax .* counts)
end;


    

In [15]:

    

d = Distributions.Normal(0, 1)


    

    
Out[15]:





Distributions.Normal(μ=0.0, σ=1.0)

In [37]:

    

srand(1)

n = 10000
bins = 100

x = rand(d, n)

(edges, counts) = hist(x, bins)

xauc = auc(edges, counts)

xvec = collect(edges)

xdf = DataFrame(
    xmin = collect(xvec[1 : (end - 1)]),
    xmax = collect(xvec[2 : end]),
    count = counts )

xdf[:density] = xdf[:count] / xauc;

xdf[:x] = (xdf[:xmin] .+ xdf[:xmax]) ./ 2;


    

In [17]:

    

lower = floor(Int64, xvec[1])
upper = ceil(Int64, xvec[end])
xₛ = linspace(lower, upper, (upper - lower) * 100 + 1)
xpdf = DataFrame(x = xₛ, density = pdf(TDist(12), xₛ));


    

In [50]:

    

Gadfly.plot(
    layer(
        xpdf,
        x = :x,
        y = :density,
        Geom.line,
        Theme(default_color = colorant"orange") ),
    layer(
        xdf,
        x = :xmin,
        y = :density,
        Geom.bar,
        Theme(default_color = colorant"gainsboro") ) )


    

    
Out[50]:

In [38]:

    

plot(xdf, x = :x, y = :density, Geom.bar)


    

    
Out[38]:

In [34]:

    

reshape(xdf[:x], 23, 3)


    

    
Out[34]:





DataArrays.DataArray{Float64,1}

In [36]:

    

typeof(xdf[:density])


    

    
Out[36]:





DataArrays.DataArray{Float64,1}