diff --git a/examples/learn_julia/krige.jl b/examples/learn_julia/krige.jl
index eb666fe2..2d064afa 100644
--- a/examples/learn_julia/krige.jl
+++ b/examples/learn_julia/krige.jl
@@ -24,7 +24,7 @@ for i in 1:n
 	end
 end
 
-Gadfly.plot(Gadfly.layer(z=k, x=x, y=y, Gadfly.Geom.contour(levels=collect(range(minimum(k); stop=maximum(k), length=51))), Gadfly.layer(x=X[1,:], y=X[2,:], Geom.point, Gadfly.Theme(default_color=parse(Colors.Colorant, "red"), point_size=4pt)))
+Gadfly.plot(Gadfly.layer(z=k, x=x, y=y, Gadfly.Geom.contour(levels=collect(range(minimum(k); stop=maximum(k), length=51)))), Gadfly.layer(x=X[1,:], y=X[2,:], Geom.point, Gadfly.Theme(default_color=parse(Colors.Colorant, "red"), point_size=4pt)))
 
 fig = PyPlot.figure(figsize=(8, 6))
 ax = fig.gca(projection="3d")
diff --git a/examples/model_analysis/bayes.jl b/examples/model_analysis/bayes.jl
index 65ea937c..f5c42c71 100644
--- a/examples/model_analysis/bayes.jl
+++ b/examples/model_analysis/bayes.jl
@@ -9,5 +9,5 @@ mcmcchain = Mads.bayessampling(md; nsteps=1000000, burnin=1000, thinning=1000, s
 Mads.scatterplotsamples(md, permutedims(mcmcchain.value), joinpath("bayes_results", "bayes_scatter.png"))
 Mads.display(joinpath("bayes_results", "bayes_scatter.png"))
 o = Mads.forward(md, mcmcchain.value)
-Mads.spaghettiplot(md, o, filename=joinpath("bayes_results" "bayes_spaghetti.png"))
+Mads.spaghettiplot(md, o, filename=joinpath("bayes_results", "bayes_spaghetti.png"))
 Mads.display(joinpath("bayes_results", "bayes_spaghetti.png"))
\ No newline at end of file
diff --git a/examples/ode/ode-driver.jl b/examples/ode/ode-driver.jl
index fcded8e2..d11088f8 100644
--- a/examples/ode/ode-driver.jl
+++ b/examples/ode/ode-driver.jl
@@ -19,7 +19,7 @@ function madsmodelrun(parameters::AbstractDict)
 	times = collect(0:.1:100)
 	initialconditions = [1.,0.]
 	t, y = ODE.ode23s(funcosc, initialconditions, times; points=:specified)
-	ys = permutedims(hcat(y...) # vectorize the output and transpose with
+	ys = permutedims(hcat(y...)) # vectorize the output and transpose with
 	predictions = OrderedCollections.OrderedDict{String, Float64}(zip(map(i -> string("o", i), times), ys[:,1]))
 	return predictions
 end