summarystats improvements

src/scalarstats.jl

@@ -857,6 +857,7 @@ kldivergence(p::AbstractArray{<:Real}, q::AbstractArray{<:Real}, b::Real) =
 
 struct SummaryStats{T<:Union{AbstractFloat,Missing}}
     mean::T
+    sd::T
     min::T
     q25::T
     median::T
@@ -871,14 +872,16 @@ end
     summarystats(a)
 
 Compute summary statistics for a real-valued array `a`. Returns a
-`SummaryStats` object containing the mean, minimum, 25th percentile,
-median, 75th percentile, and maxmimum.
+`SummaryStats` object containing the number of observations,
+number of missing observations, standard deviation, mean, minimum,
+25th percentile, median, 75th percentile, and maximum.
 """
 function summarystats(a::AbstractArray{T}) where T<:Union{Real,Missing}
     # `mean` doesn't fail on empty input but rather returns `NaN`, so we can use the
     # return type to populate the `SummaryStats` structure.
     s = T >: Missing ? collect(skipmissing(a)) : a
     m = mean(s)
+    stdev = std(s, mean=m)
     R = typeof(m)
     n = length(a)
     ns = length(s)
@@ -889,7 +892,7 @@ function summarystats(a::AbstractArray{T}) where T<:Union{Real,Missing}
     else
         quantile(s, [0.00, 0.25, 0.50, 0.75, 1.00])
     end
-    SummaryStats{R}(m, qs..., n, n - ns)
+    SummaryStats{R}(m, stdev, qs..., n, n - ns)
 end
 
 function Base.show(io::IO, ss::SummaryStats)
@@ -898,6 +901,7 @@ function Base.show(io::IO, ss::SummaryStats)
     ss.nobs > 0 || return
     @printf(io, "Missing Count:  %i\n", ss.nmiss)
     @printf(io, "Mean:           %.6f\n", ss.mean)
+    @printf(io, "Std. Deviation: %.6f\n", ss.sd)
     @printf(io, "Minimum:        %.6f\n", ss.min)
     @printf(io, "1st Quartile:   %.6f\n", ss.q25)
     @printf(io, "Median:         %.6f\n", ss.median)

test/misc.jl

@@ -54,6 +54,7 @@ describe(io, collect(1:10))
                            Length:         10
                            Missing Count:  0
                            Mean:           5.500000
+                           Std. Deviation: 3.027650
                            Minimum:        1.000000
                            1st Quartile:   3.250000
                            Median:         5.500000
@@ -68,6 +69,7 @@ describe(io, Union{Float32,Missing}[1.0, 4.5, missing, missing, 33.1])
                            Length:         5
                            Missing Count:  2
                            Mean:           12.866666
+                           Std. Deviation: 17.609751
                            Minimum:        1.000000
                            1st Quartile:   2.750000
                            Median:         4.500000

test/scalarstats.jl

@@ -332,30 +332,39 @@ s = summarystats(1:5)
 @test isa(s, StatsBase.SummaryStats)
 @test s.min == 1.0
 @test s.max == 5.0
+@test s.nobs   == 5
+@test s.nmiss  == 0
 @test s.mean   ≈ 3.0
 @test s.median ≈ 3.0
 @test s.q25    ≈ 2.0
 @test s.q75    ≈ 4.0
+@test s.sd     ≈ 1.5811388300841898
 
 # Issue #631
 s = summarystats([-2, -1, 0, 1, 2, missing])
 @test isa(s, StatsBase.SummaryStats)
 @test s.min == -2.0
 @test s.max == 2.0
+@test s.nobs   == 6
+@test s.nmiss  == 1
 @test s.mean   ≈ 0.0
 @test s.median ≈ 0.0
 @test s.q25    ≈ -1.0
 @test s.q75    ≈ +1.0
+@test s.sd     ≈ 1.5811388300841898
 
 # Issue #631
 s = summarystats(zeros(10))
 @test isa(s, StatsBase.SummaryStats)
 @test s.min == 0.0
 @test s.max == 0.0
+@test s.nobs   == 10
+@test s.nmiss  == 0
 @test s.mean   ≈ 0.0
 @test s.median ≈ 0.0
 @test s.q25    ≈ 0.0
 @test s.q75    ≈ 0.0
+@test s.sd     ≈ 0.0
 
 # Issue #631
 s = summarystats(Union{Float64,Missing}[missing, missing])
@@ -364,3 +373,4 @@ s = summarystats(Union{Float64,Missing}[missing, missing])
 @test s.nmiss == 2
 @test isnan(s.mean)
 @test isnan(s.median)
+@test isnan(s.sd)