diff --git a/Manifest.toml b/Manifest.toml
index 3631e75..3c0d65e 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -72,9 +72,9 @@ uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
 [[Missings]]
 deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"]
-git-tree-sha1 = "adc26d2ee85a49c413464110d922cf21efc9d233"
+git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042"
 uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.3.1"
+version = "0.4.0"
 
 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
@@ -113,6 +113,12 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 deps = ["Serialization"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
+[[Requires]]
+deps = ["Test"]
+git-tree-sha1 = "f6fbf4ba64d295e146e49e021207993b6b48c7d1"
+uuid = "ae029012-a4dd-5104-9daa-d747884805df"
+version = "0.5.2"
+
 [[Rmath]]
 deps = ["BinaryProvider", "Libdl", "Random", "Statistics", "Test"]
 git-tree-sha1 = "9a6c758cdf73036c3239b0afbea790def1dabff9"
diff --git a/Project.toml b/Project.toml
index 74d0673..3130a7e 100644
--- a/Project.toml
+++ b/Project.toml
@@ -5,6 +5,7 @@ version = "0.2.0"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
diff --git a/REQUIRE b/REQUIRE
index 1068a15..41122b2 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -1,2 +1,3 @@
 julia 0.7
 Distributions
+Requires
diff --git a/src/SimpleANOVA.jl b/src/SimpleANOVA.jl
index 7567599..fadd5e9 100644
--- a/src/SimpleANOVA.jl
+++ b/src/SimpleANOVA.jl
@@ -1,6 +1,6 @@
 module SimpleANOVA
 
-using Distributions
+using Distributions, Requires
 include("InvertedIndices.jl")
 include("AnovaEffect.jl")
 include("AnovaValue.jl")
@@ -14,9 +14,14 @@ const cellsname = "Cells"
 const errorname = "Error"
 const remaindername = "Remainder"
 
+function __init__()
+    @require DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" include("anova_dataframes.jl")
+end
+
 """
     anova(observations::Union{Array{Number}, Array{Vector{Number}}}, factortypes = FactorType[]; factornames = String[], hasreplicates = true)
-    anova(observations::Vector{Number}, factorassignments::Vector{Vector{Int}}, factortypes = FactorType[]; factornames = String[], hasreplicates = true)
+    anova(observations::Vector{Number}, factorassignments::Vector{Vector{Any}}, factortypes = FactorType[]; factornames = String[], hasreplicates = true)
+    anova(df::DataFrame, observationscolumn::Symbol, factorcolumns::Vector{Symbol}, factortypes = FactorType[]; factornames = String[])
 
 Performs an Analysis of Variance (ANOVA) computation.
 
@@ -107,7 +112,7 @@ function anova(observations::AbstractArray{T}, factortypes::Vector{FactorType} =
     anovakernel(observations, nreplicates, ncells, nnestedfactors, ncrossedfactors, nfactorlevels, crossedfactortypes, crossedfactornames, nestedfactornames)
 end
 
-function anova(observations::AbstractVector{T}, factorassignments::AbstractVector{<:AbstractVector{<:Int}}, factortypes::Vector{FactorType} = FactorType[], factornames::Vector{<:AbstractString} = String[]) where {T <: Number}
+function anova(observations::AbstractVector{T}, factorassignments::AbstractVector{<:AbstractVector}, factortypes::Vector{FactorType} = FactorType[], factornames::Vector{<:AbstractString} = String[]) where {T <: Number}
     length(observations) > 0 || return
     nfactors = length(factorassignments)
     N = length(observations)
@@ -122,7 +127,7 @@ function anova(observations::AbstractVector{T}, factorassignments::AbstractVecto
     all(nperfactorlevel .|> length .== 1) || error("Design is unbalanced.")
     nperfactorlevel = nperfactorlevel .|> first
 
-    if any(maximum.(factorlevels) .> nfactorlevels)
+    if !(factorassignements <: Number) || any(maximum.(factorlevels) .> nfactorlevels)
         compressedfactorlevels = [1:i for i ∈ nfactorlevels]
         factorlevelremapping = [factorlevels[i] .=> compressedfactorlevels[i] for i ∈ 1:nfactors]
         factorassignments = [replace(factorassignments[i], factorlevelremapping[i]...) for i ∈ 1:nfactors]
@@ -134,7 +139,7 @@ function anova(observations::AbstractVector{T}, factorassignments::AbstractVecto
     sortorder = sortperm(repeat(1:nreplicates, Int(N / nreplicates)) .+
                          sum([factorassignments[i] .* prod(nlevels[1:i]) for i ∈ 1:nfactors]))
     observationsmatrix = reshape(observations[sortorder], nlevels...)
-    anova(observationsmatrix, factortypes, factornames = factornames)
+    anova(observationsmatrix, factortypes, factornames = factornames, hasreplicates = nreplicates > 1)
 end
 
 function validate(factortypes::Vector{FactorType}, factornames::Vector{<:AbstractString}, nfactors)
diff --git a/src/anova_dataframe.jl b/src/anova_dataframe.jl
new file mode 100644
index 0000000..899d501
--- /dev/null
+++ b/src/anova_dataframe.jl
@@ -0,0 +1,7 @@
+function anova(df::DataFrame, observationscolumn::Symbol, factorcolumns::Vector{Symbol}, factortypes::Vector{FactorType} = FactorType[]; factornames::Vector{<:AbstractString} = String[])
+    observations = df[observationscolumn]
+    length(observations) > 0 || return
+    eltype(observations) <: Number || error("Obervations must be numeric")
+    isempty(factornames) && (factornames = [String(col) for col ∈ factorcolumns])
+    anova(df[observationscolumn], df[factorcolumns], factortypes, factornames = factornames)
+end