diff --git a/src/arg_desc.jl b/src/arg_desc.jl
index b233d6d7..cea77071 100644
--- a/src/arg_desc.jl
+++ b/src/arg_desc.jl
@@ -21,7 +21,7 @@ const _arg_desc = KW(
 :markerstrokewidth 	=> "Number. Width of the marker stroke (border. in pixels)",
 :markerstrokecolor 	=> "Color Type. Color of the marker stroke (border).  `:match` will take the value from `:foreground_color_subplot`.",
 :markerstrokealpha 	=> "Number in [0,1]. The alpha/opacity override for the marker stroke (border).  `nothing` (the default) means it will take the alpha value of markerstrokecolor.",
-:bins              	=> "Integer, NTuple{2,Integer}, AbstractVector or Symbol. For histogram-types, defines the number of bins, or the edges, of the histogram, or the auto-binning algorithm to use (:sturges, :sqrt, :rice, :scott or :fd)",
+:bins              	=> "Integer, NTuple{2,Integer}, AbstractVector or Symbol. Default is :auto. For histogram-types, defines the number of bins, or the edges, of the histogram, or the auto-binning algorithm to use (:sturges, :sqrt, :rice, :scott or :fd)",
 :smooth            	=> "Bool.  Add a regression line?",
 :group             	=> "AbstractVector. Data is split into a separate series, one for each unique value in `group`.",
 :x                 	=> "Various. Input data. First Dimension",
diff --git a/src/args.jl b/src/args.jl
index 4f4bba59..b93e2d98 100644
--- a/src/args.jl
+++ b/src/args.jl
@@ -185,7 +185,7 @@ const _series_defaults = KW(
     :markerstrokewidth => 1,
     :markerstrokecolor => :match,
     :markerstrokealpha => nothing,
-    :bins              => 30,        # number of bins for hists
+    :bins              => :auto,        # number of bins for hists
     :smooth            => false,     # regression line?
     :group             => nothing,   # groupby vector
     :x                 => nothing,
diff --git a/src/recipes.jl b/src/recipes.jl
index ef37f184..115ffa79 100644
--- a/src/recipes.jl
+++ b/src/recipes.jl
@@ -582,9 +582,11 @@ function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode
 
     v = vs[dim]
 
-    if mode == :sqrt  # Square-root choice
+    if mode == :auto
+        30
+    elseif mode == :sqrt  # Square-root choice
         _cl(sqrt(n))
-    elseif mode == :sturges || mode ==:auto  # Sturges' formula
+    elseif mode == :sturges  # Sturges' formula
         _cl(log2(n)) + 1
     elseif mode == :rice  # Rice Rule
         _cl(2 * n^(1/3))
@@ -594,7 +596,7 @@ function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode
         _cl(_span(v) / (2 * _iqr(v) / n^(1/3)))
     else
         error("Unknown auto-binning mode $mode")
-    end
+    end::Int
 end
 
 _hist_edge{N}(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Integer) = StatsBase.histrange(vs[dim], binning, :left)
@@ -611,6 +613,7 @@ _hist_norm_mode(mode::Symbol) = mode
 _hist_norm_mode(mode::Bool) = mode ? :pdf : :none
 
 function _make_hist{N}(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing)
+    info("binning = $binning")
     edges = _hist_edges(vs, binning)
     h = float( weights == nothing ?
         StatsBase.fit(StatsBase.Histogram, vs, edges, closed = :left) :