diff --git a/src/abstractdataframe/abstractdataframe.jl b/src/abstractdataframe/abstractdataframe.jl
index a40627c6a..ea407d495 100644
--- a/src/abstractdataframe/abstractdataframe.jl
+++ b/src/abstractdataframe/abstractdataframe.jl
@@ -197,18 +197,30 @@ julia> rename!(uppercase, df)
 ```
 """
 function rename!(df::AbstractDataFrame, vals::AbstractVector{Symbol};
-                 makeunique::Bool=false)
-    rename!(index(df), vals, makeunique=makeunique)
+                 makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing)
+    if !makeunique && isa(mergeduplicates, Function)
+        (new_columns, colindex) = process_updates(UpdateIndex(vals), _columns(df), mergeduplicates)
+        # Now we must replace the columns and index with the new ones in place...
+        splice!(_columns(df), 1:length(_columns(df)), new_columns) # Replace the columns with these new ones...
+        rename!(index(df), colindex)
+    else
+        rename!(index(df), vals, makeunique=makeunique)
+    end
     # renaming columns of SubDataFrame has to clean non-note metadata in its parent
     _drop_all_nonnote_metadata!(parent(df))
     return df
 end
 
+function rename!(idx::Index, new_index::Index)
+    splice!(idx.names, 1:length(idx.names), new_index.names)
+    empty!(idx.lookup)
+    merge!(idx.lookup, new_index.lookup)
+    return idx
+end
+
 function rename!(df::AbstractDataFrame, vals::AbstractVector{<:AbstractString};
-                 makeunique::Bool=false)
-    rename!(index(df), Symbol.(vals), makeunique=makeunique)
-    # renaming columns of SubDataFrame has to clean non-note metadata in its parent
-    _drop_all_nonnote_metadata!(parent(df))
+                 makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing)
+    rename!(df, Symbol.(vals), makeunique=makeunique, mergeduplicates=mergeduplicates)
     return df
 end
 
@@ -353,9 +365,11 @@ julia> rename(uppercase, df)
 ```
 """
 rename(df::AbstractDataFrame, vals::AbstractVector{Symbol};
-       makeunique::Bool=false) = rename!(copy(df), vals, makeunique=makeunique)
+       makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing) = 
+    rename!(copy(df), vals, makeunique=makeunique, mergeduplicates=mergeduplicates)
 rename(df::AbstractDataFrame, vals::AbstractVector{<:AbstractString};
-       makeunique::Bool=false) = rename!(copy(df), vals, makeunique=makeunique)
+       makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing) =
+    rename!(copy(df), vals, makeunique=makeunique, mergeduplicates=mergeduplicates)
 rename(df::AbstractDataFrame, args...) = rename!(copy(df), args...)
 rename(f::Function, df::AbstractDataFrame) = rename!(f, copy(df))
 
@@ -1534,9 +1548,17 @@ function fillcombinations(df::AbstractDataFrame, indexcols;
     return out_df
 end
 
+"""
+MergeDuplicates = Union{Nothing,Function}
+
+Wherever the `mergeduplicates` keyword argument is available it is either `nothing` or
+a `Function` that will be executed to combine duplicated columns (when `makeunique=false`)
+"""
+MergeDuplicates = Union{Nothing,Function}
+
 """
     hcat(df::AbstractDataFrame...;
-         makeunique::Bool=false, copycols::Bool=true)
+         makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
 
 Horizontally concatenate data frames.
 
@@ -1544,6 +1566,12 @@ If `makeunique=false` (the default) column names of passed objects must be uniqu
 If `makeunique=true` then duplicate column names will be suffixed
 with `_i` (`i` starting at 1 for the first duplicate).
 
+If `makeunique=false` and `mergeduplicates` is a `Function` then duplicate columns 
+will be combined by invoking the function with all values from those columns.
+e.g. `mergeduplicates=coalesce` will use the first non-missing value. Since `hcat` and
+`hcat!` are performed recursively for more than two frames, this `mergeduplicates` 
+function will only combine two columns at a time.
+
 If `copycols=true` (the default) then the `DataFrame` returned by `hcat` will
 contain copied columns from the source data frames.
 If `copycols=false` then it will contain columns as they are stored in the
@@ -1593,26 +1621,26 @@ julia> df3.A === df1.A
 true
 ```
 """
-function Base.hcat(df::AbstractDataFrame; makeunique::Bool=false, copycols::Bool=true)
+function Base.hcat(df::AbstractDataFrame; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     df = DataFrame(df, copycols=copycols)
     _drop_all_nonnote_metadata!(df)
     return df
 end
 
 # TODO: after deprecation remove AbstractVector methods
-Base.hcat(df::AbstractDataFrame, x::AbstractVector; makeunique::Bool=false, copycols::Bool=true) =
-    hcat!(DataFrame(df, copycols=copycols), x, makeunique=makeunique, copycols=copycols)
-Base.hcat(x::AbstractVector, df::AbstractDataFrame; makeunique::Bool=false, copycols::Bool=true) =
-    hcat!(x, df, makeunique=makeunique, copycols=copycols)
+Base.hcat(df::AbstractDataFrame, x::AbstractVector; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    hcat!(DataFrame(df, copycols=copycols), x, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
+Base.hcat(x::AbstractVector, df::AbstractDataFrame; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    hcat!(x, df, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 Base.hcat(df1::AbstractDataFrame, df2::AbstractDataFrame;
-          makeunique::Bool=false, copycols::Bool=true) =
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
     hcat!(DataFrame(df1, copycols=copycols), df2,
-          makeunique=makeunique, copycols=copycols)
+          makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 Base.hcat(df::AbstractDataFrame, x::Union{AbstractVector, AbstractDataFrame},
           y::Union{AbstractVector, AbstractDataFrame}...;
-          makeunique::Bool=false, copycols::Bool=true) =
-    hcat!(hcat(df, x, makeunique=makeunique, copycols=copycols), y...,
-          makeunique=makeunique, copycols=copycols)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    hcat!(hcat(df, x, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols), y...,
+          makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 """
     vcat(dfs::AbstractDataFrame...;
@@ -2868,8 +2896,11 @@ const INSERTCOLS_ARGUMENTS =
       are unwrapped and treated in the same way
     - `after` : if `true` columns are inserted after `col`
     - `makeunique` : defines what to do if `name` already exists in `df`;
-      if it is `false` an error will be thrown; if it is `true` a new unique name will
-      be generated by adding a suffix
+      if it is `true` a new unique name will be generated by adding a suffix,
+      if it is `false` an error will be thrown unless a `mergeduplicates` functiom is provided.
+    - `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+      is false. It should be given a Function that combines the values of all of the duplicated
+      columns which will be passed as a varargs. The return value is used.
     - `copycols` : whether vectors passed as columns should be copied
 
     If `val` is an `AbstractRange` then the result of `collect(val)` is inserted.
@@ -2891,7 +2922,7 @@ const INSERTCOLS_ARGUMENTS =
 
 """
     insertcols(df::AbstractDataFrame[, col], (name=>val)::Pair...;
-               after::Bool=false, makeunique::Bool=false, copycols::Bool=true)
+               after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
 
 Insert a column into a copy of `df` data frame using the [`insertcols!`](@ref)
 function and return the newly created data frame.
@@ -2942,13 +2973,13 @@ julia> insertcols(df, :a, :d => 7:9, after=true)
 ```
 """
 insertcols(df::AbstractDataFrame, args...;
-           after::Bool=false, makeunique::Bool=false, copycols::Bool=true) =
+           after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
     insertcols!(copy(df), args...;
-                after=after, makeunique=makeunique, copycols=copycols)
+                after=after, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 """
     insertcols!(df::AbstractDataFrame[, col], (name=>val)::Pair...;
-                after::Bool=false, makeunique::Bool=false, copycols::Bool=true)
+                after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
 
 Insert a column into a data frame in place. Return the updated data frame.
 
@@ -2999,7 +3030,10 @@ julia> insertcols!(df, :b, :d => 7:9, after=true)
 ```
 """
 function insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{Symbol}...;
-                     after::Bool=false, makeunique::Bool=false, copycols::Bool=true)
+                     after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
+    
+    _check_makeunique_args(mergeduplicates, makeunique)
+
     if !is_column_insertion_allowed(df)
         throw(ArgumentError("insertcols! is only supported for DataFrame, or for " *
                             "SubDataFrame created with `:` as column selector"))
@@ -3025,15 +3059,15 @@ function insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{Sy
                             "$(ncol(df)) columns at index $col_ind"))
     end
 
-    if !makeunique
+    if !makeunique && isnothing(mergeduplicates)
         if !allunique(first.(name_cols))
             throw(ArgumentError("Names of columns to be inserted into a data frame " *
-                                "must be unique when `makeunique=true`"))
+                                "must be unique when `mergeduplicates=nothing`"))
         end
         for (n, _) in name_cols
             if hasproperty(df, n)
                 throw(ArgumentError("Column $n is already present in the data frame " *
-                                    "which is not allowed when `makeunique=true`"))
+                                    "which is not allowed when `mergeduplicates=nothing`"))
             end
         end
     end
@@ -3067,6 +3101,7 @@ function insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{Sy
         target_row_count = 1
     end
 
+    mergecolumns = Dict{Symbol, Any}()
     start_col_ind = col_ind
     for (name, item) in name_cols
         if !(item isa AbstractVector)
@@ -3103,23 +3138,38 @@ function insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{Sy
             dfp[!, name] = item_new
         else
             if hasproperty(dfp, name)
-                @assert makeunique
-                k = 1
-                while true
-                    nn = Symbol("$(name)_$k")
-                    if !hasproperty(dfp, nn)
-                        name = nn
-                        break
+                if makeunique
+                    k = 1
+                    while true
+                        nn = Symbol("$(name)_$k")
+                        if !hasproperty(dfp, nn)
+                            name = nn
+                            break
+                        end
+                        k += 1
                     end
-                    k += 1
+                    insert!(index(dfp), col_ind, name)
+                    insert!(_columns(dfp), col_ind, item_new)
+                else
+                    # Just update without adding to index
+                    merge = get(mergecolumns, name, (dfp=dfp, cols=[]))
+                    push!(merge.cols, item_new)
+                    mergecolumns[name] = merge
+                    col_ind -= 1
                 end
+            else
+                insert!(index(dfp), col_ind, name)
+                insert!(_columns(dfp), col_ind, item_new)
             end
-            insert!(index(dfp), col_ind, name)
-            insert!(_columns(dfp), col_ind, item_new)
         end
         col_ind += 1
     end
 
+    # Combine columns using mergeduplicates
+    for (name, merge) in mergecolumns
+        merge.dfp[!, name] = mergeduplicates.(merge.dfp[!, name], merge.cols...)
+    end
+
     delta = col_ind - start_col_ind
     colmetadata_dict = getfield(parent(df), :colmetadata)
     if !isnothing(colmetadata_dict) && delta > 0
@@ -3134,22 +3184,22 @@ function insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{Sy
 end
 
 insertcols!(df::AbstractDataFrame, col::ColumnIndex, name_cols::Pair{<:AbstractString}...;
-            after::Bool=false, makeunique::Bool=false, copycols::Bool=true) =
+            after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
     insertcols!(df, col, (Symbol(n) => v for (n, v) in name_cols)...,
-                after=after, makeunique=makeunique, copycols=copycols)
+                after=after, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 insertcols!(df::AbstractDataFrame, name_cols::Pair{Symbol}...;
-            after::Bool=false, makeunique::Bool=false, copycols::Bool=true) =
+            after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
     insertcols!(df, ncol(df)+1, name_cols..., after=after,
-                makeunique=makeunique, copycols=copycols)
+                makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 insertcols!(df::AbstractDataFrame, name_cols::Pair{<:AbstractString}...;
-            after::Bool=false, makeunique::Bool=false, copycols::Bool=true) =
+            after::Bool=false, makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
     insertcols!(df, (Symbol(n) => v for (n, v) in name_cols)...,
-                after=after, makeunique=makeunique, copycols=copycols)
+                after=after, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 function insertcols!(df::AbstractDataFrame, col::ColumnIndex; after::Bool=false,
-                     makeunique::Bool=false, copycols::Bool=true)
+                     makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     if col isa SymbolOrString
         col_ind = Int(columnindex(df, col))
         if col_ind == 0
@@ -3173,7 +3223,7 @@ function insertcols!(df::AbstractDataFrame, col::ColumnIndex; after::Bool=false,
 end
 
 function insertcols!(df::AbstractDataFrame; after::Bool=false,
-                     makeunique::Bool=false, copycols::Bool=true)
+                     makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     _drop_all_nonnote_metadata!(parent(df))
     return df
 end
diff --git a/src/abstractdataframe/reshape.jl b/src/abstractdataframe/reshape.jl
index 2effb6f2f..8df8e7588 100644
--- a/src/abstractdataframe/reshape.jl
+++ b/src/abstractdataframe/reshape.jl
@@ -823,7 +823,8 @@ julia> permutedims(df2, 1, "different_name")
 """
 function Base.permutedims(df::AbstractDataFrame, src_namescol::ColumnIndex,
                           dest_namescol::Union{Symbol, AbstractString};
-                          makeunique::Bool=false, strict::Bool=true)
+                          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
+                          strict::Bool=true)
 
     if src_namescol isa Integer
         1 <= src_namescol <= ncol(df) || throw(BoundsError(index(df), src_namescol))
@@ -854,18 +855,20 @@ function Base.permutedims(df::AbstractDataFrame, src_namescol::ColumnIndex,
 
     if ncol(df_notsrc) == 0
         df_tmp = DataFrame(AbstractVector[[] for _ in 1:nrow(df)], new_col_names,
-                           makeunique=makeunique, copycols=false)
+                           makeunique=makeunique, mergeduplicates=mergeduplicates,
+                           copycols=false)
     else
         m = permutedims(Matrix(df_notsrc))
-        df_tmp = rename!(DataFrame(Tables.table(m)), new_col_names, makeunique=makeunique)
+        df_tmp = rename!(DataFrame(Tables.table(m)), new_col_names, makeunique=makeunique, mergeduplicates=mergeduplicates)
     end
-    out_df = hcat!(df_permuted, df_tmp, makeunique=makeunique, copycols=false)
+    out_df = hcat!(df_permuted, df_tmp, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=false)
     _copy_table_note_metadata!(out_df, df)
     return out_df
 end
 
 function Base.permutedims(df::AbstractDataFrame, src_namescol::ColumnIndex;
-                          makeunique::Bool=false, strict::Bool=true)
+                          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
+                          strict::Bool=true)
     if src_namescol isa Integer
         1 <= src_namescol <= ncol(df) || throw(BoundsError(index(df), src_namescol))
         dest_namescol = _names(df)[src_namescol]
@@ -873,7 +876,7 @@ function Base.permutedims(df::AbstractDataFrame, src_namescol::ColumnIndex;
         dest_namescol = src_namescol
     end
     return permutedims(df, src_namescol, dest_namescol;
-                       makeunique=makeunique, strict=strict)
+                       makeunique=makeunique, mergeduplicates=mergeduplicates, strict=strict)
 end
 
 function Base.permutedims(df::AbstractDataFrame)
@@ -883,8 +886,8 @@ function Base.permutedims(df::AbstractDataFrame)
 end
 
 function Base.permutedims(df::AbstractDataFrame, cnames::AbstractVector;
-                          makeunique::Bool=false)
-    out_df = DataFrame(permutedims(Matrix(df)), cnames, makeunique=makeunique)
+                          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing)
+    out_df = DataFrame(permutedims(Matrix(df)), cnames, makeunique=makeunique, mergeduplicates=mergeduplicates)
     _copy_table_note_metadata!(out_df, df)
     return out_df
 end
diff --git a/src/abstractdataframe/selection.jl b/src/abstractdataframe/selection.jl
index a90f1203d..be53c41d2 100644
--- a/src/abstractdataframe/selection.jl
+++ b/src/abstractdataframe/selection.jl
@@ -1822,7 +1822,7 @@ end
 
 function manipulate(df::DataFrame, args::AbstractVector{Int};
                     copycols::Bool, keeprows::Bool, renamecols::Bool)
-    new_df = DataFrame(_columns(df)[args], Index(_names(df)[args]), copycols=copycols)
+    new_df = DataFrame(_columns(df)[args], UpdateIndex(_names(df)[args]), copycols=copycols)
     _copy_all_note_metadata!(new_df, df)
     return new_df
 end
diff --git a/src/dataframe/dataframe.jl b/src/dataframe/dataframe.jl
index 3f4afafec..821b8c300 100755
--- a/src/dataframe/dataframe.jl
+++ b/src/dataframe/dataframe.jl
@@ -8,16 +8,16 @@ particularly a `Vector`, `PooledVector` or `CategoricalVector`.
 
 # Constructors
 ```julia
-DataFrame(pairs::Pair...; makeunique::Bool=false, copycols::Bool=true)
-DataFrame(pairs::AbstractVector{<:Pair}; makeunique::Bool=false, copycols::Bool=true)
+DataFrame(pairs::Pair...; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
+DataFrame(pairs::AbstractVector{<:Pair}; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
 DataFrame(ds::AbstractDict; copycols::Bool=true)
 DataFrame(; kwargs..., copycols::Bool=true)
 
 DataFrame(table; copycols::Union{Bool, Nothing}=nothing)
 DataFrame(table, names::AbstractVector;
-          makeunique::Bool=false, copycols::Union{Bool, Nothing}=nothing)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Union{Bool, Nothing}=nothing)
 DataFrame(columns::AbstractVecOrMat, names::AbstractVector;
-          makeunique::Bool=false, copycols::Bool=true)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
 
 DataFrame(::DataFrameRow; copycols::Bool=true)
 DataFrame(::GroupedDataFrame; copycols::Bool=true, keepkeys::Bool=true)
@@ -84,9 +84,13 @@ Pass the `copycols=false` keyword argument (where supported) to reuse vectors wi
 copying them.
 
 By default an error will be raised if duplicates in column names are found. Pass
-`makeunique=true` keyword argument (where supported) to accept duplicate names,
+`makeunique=true` keyword argument to accept duplicate names,
 in which case they will be suffixed with `_i` (`i` starting at 1 for the first
-duplicate).
+duplicate), or provide a `mergeduplicates` function.
+
+If `makeunique=false` and `mergeduplicates` is a `Function` then duplicate column names 
+will be combined by this function with the column named overwritten by the results of 
+the function on all values from the duplicated column(s).
 
 If an `AbstractRange` is passed to a `DataFrame` constructor as a column it is
 always collected to a `Vector` (even if `copycols=false`). As a general rule
@@ -189,12 +193,24 @@ mutable struct DataFrame <: AbstractDataFrame
     # non-:note-style metadata is added.
     allnotemetadata::Bool
 
+    """
+    UpdateIndex is a temporary struct used *only* in the initialization
+    of a DataFrame. It holds the eventual Index for the DataFrame as well
+    as a list of columns to be combined using mergeduplicates.
+    """
+    struct UpdateIndex
+        index::Index
+        updates::Vector{Symbol}
+    end
+
     # the inner constructor should not be used directly
     function DataFrame(columns::Union{Vector{Any}, Vector{AbstractVector}},
-                       colindex::Index; copycols::Bool=true)
+                       update_index::UpdateIndex; copycols::Bool=true, 
+                       mergeduplicates::MergeDuplicates=nothing)
+        colindex = update_index.index
         if length(columns) == length(colindex) == 0
             return new(AbstractVector[], Index(), nothing, nothing, true)
-        elseif length(columns) != length(colindex)
+        elseif length(columns) != column_length(update_index)
             throw(DimensionMismatch("Number of columns ($(length(columns))) and number of " *
                                     "column names ($(length(colindex))) are not equal"))
         end
@@ -232,8 +248,73 @@ mutable struct DataFrame <: AbstractDataFrame
             firstindex(col) != 1 && _onebased_check_error(i, col)
         end
 
+        # process updates if they exist
+        (columns, colindex) = process_updates(update_index, columns, mergeduplicates)
+
         return new(convert(Vector{AbstractVector}, columns), colindex, nothing, nothing, true)
     end
+
+    function DataFrame(columns::Union{Vector{Any},Vector{AbstractVector}},
+                       colindex::Index; copycols::Bool=true,
+                       mergeduplicates=nothing)
+        return DataFrame(columns, UpdateIndex(colindex),
+            copycols=copycols, mergeduplicates=mergeduplicates)
+    end
+end
+
+column_length(x::UpdateIndex) = isempty(x.updates) ? column_length(x.index) : length(x.updates)
+has_updates(x::UpdateIndex) = !isempty(x.updates)
+
+UpdateIndex(idx::Index) = UpdateIndex(idx, [])
+UpdateIndex() = UpdateIndex(Index())
+
+function UpdateIndex(names::AbstractVector{Symbol}; makeunique::Bool=false)
+    if !makeunique
+        lookup = Dict{Symbol, Int}(zip(reverse(names), length(names):-1:1))
+        return UpdateIndex(Index(lookup, unique(names)), names)
+    else
+        idx = Index(names, makeunique=makeunique)
+        return UpdateIndex(idx, [])
+    end
+end
+
+"""
+Processes the updates defined by an UpdateIndex on the given columns
+
+Returns a tuple of the new set of columns and the new column index
+"""
+function process_updates(update_index::UpdateIndex, columns::Union{Vector{Any}, Vector{AbstractVector}}, mergeduplicates::MergeDuplicates)
+    if has_updates(update_index)
+        colindex = update_index.index
+        merges = Dict{Symbol,Any}()
+        updated = Vector{Any}(nothing, length(colindex.names))
+        for src in eachindex(update_index.updates)
+            name = update_index.updates[src]
+            dst = colindex.lookup[name]
+            if isnothing(updated[dst])
+                updated[dst] = columns[src]
+            else
+                if isnothing(mergeduplicates)
+                    msg = "Duplicate variable names: $name. Pass makeunique=true " *
+                    "to make them unique using a suffix automatically."
+                    throw(ArgumentError(msg))
+                end
+                merge = get(merges, name, (dst=dst, columns=[]))
+                push!(merge.columns, columns[src])
+                merges[name] = merge
+            end
+        end
+
+        # Handle mergeduplicates updates
+        for (_, merge) in merges
+            updated[merge.dst] = mergeduplicates.(updated[merge.dst], merge.columns...)
+        end
+
+        columns = updated
+        colindex = update_index.index
+    end
+
+    return (columns, update_index.index)
 end
 
 function _preprocess_column(col::Any, len::Integer, copycols::Bool)
@@ -254,24 +335,27 @@ end
 
 DataFrame(df::DataFrame; copycols::Bool=true) = copy(df, copycols=copycols)
 
-function DataFrame(pairs::Pair{Symbol, <:Any}...; makeunique::Bool=false,
+function DataFrame(pairs::Pair{Symbol, <:Any}...; 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    copycols::Bool=true)::DataFrame
     colnames = [Symbol(k) for (k, v) in pairs]
     columns = Any[v for (k, v) in pairs]
-    return DataFrame(columns, Index(colnames, makeunique=makeunique),
-                     copycols=copycols)
+    return DataFrame(columns, UpdateIndex(colnames, makeunique=makeunique),
+                     copycols=copycols, mergeduplicates=mergeduplicates)
 end
 
-function DataFrame(pairs::Pair{<:AbstractString, <:Any}...; makeunique::Bool=false,
+function DataFrame(pairs::Pair{<:AbstractString, <:Any}...; 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    copycols::Bool=true)::DataFrame
     colnames = [Symbol(k) for (k, v) in pairs]
     columns = Any[v for (k, v) in pairs]
-    return DataFrame(columns, Index(colnames, makeunique=makeunique),
-                     copycols=copycols)
+    return DataFrame(columns, UpdateIndex(colnames, makeunique=makeunique),
+                     copycols=copycols, mergeduplicates=mergeduplicates)
 end
 
 # this is needed as a workaround for Tables.jl dispatch
-function DataFrame(pairs::AbstractVector{<:Pair}; makeunique::Bool=false,
+function DataFrame(pairs::AbstractVector{<:Pair}; 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    copycols::Bool=true)
     if isempty(pairs)
         return DataFrame()
@@ -281,8 +365,8 @@ function DataFrame(pairs::AbstractVector{<:Pair}; makeunique::Bool=false,
         end
         colnames = [Symbol(k) for (k, v) in pairs]
         columns = Any[v for (k, v) in pairs]
-        return DataFrame(columns, Index(colnames, makeunique=makeunique),
-                         copycols=copycols)
+        return DataFrame(columns, UpdateIndex(colnames, makeunique=makeunique),
+                         copycols=copycols, mergeduplicates=mergeduplicates)
     end
 end
 
@@ -295,7 +379,7 @@ function DataFrame(d::AbstractDict; copycols::Bool=true)
         throw(ArgumentError("All column names must be either Symbols or strings (mixing is not allowed)"))
     end
 
-    colindex = Index(colnames)
+    colindex = UpdateIndex(colnames)
     columns = Any[v for v in values(d)]
     df = DataFrame(columns, colindex, copycols=copycols)
     if d isa Dict
@@ -329,18 +413,19 @@ function DataFrame(; kwargs...)
                 push!(columns, val)
             end
         end
-        DataFrame(columns, Index(cnames), copycols=copycols)
+        DataFrame(columns, UpdateIndex(cnames), copycols=copycols)
     end
 end
 
 function DataFrame(columns::AbstractVector, cnames::AbstractVector{Symbol};
-                   makeunique::Bool=false, copycols::Bool=true)::DataFrame
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)::DataFrame
+    _check_makeunique_args(mergeduplicates, makeunique)
     if !(eltype(columns) <: AbstractVector) && !all(col -> isa(col, AbstractVector), columns)
-        return rename!(DataFrame(columns, copycols=copycols), cnames, makeunique=makeunique)
+        return rename!(DataFrame(columns, copycols=copycols), cnames, makeunique=makeunique, mergeduplicates=mergeduplicates)
     end
     return DataFrame(collect(AbstractVector, columns),
-                     Index(convert(Vector{Symbol}, cnames), makeunique=makeunique),
-                     copycols=copycols)
+                     UpdateIndex(convert(Vector{Symbol}, cnames), makeunique=makeunique),
+                     copycols=copycols, mergeduplicates=mergeduplicates)
 end
 
 function _name2symbol(str::AbstractVector)
@@ -351,18 +436,18 @@ function _name2symbol(str::AbstractVector)
 end
 
 DataFrame(columns::AbstractVector, cnames::AbstractVector;
-          makeunique::Bool=false, copycols::Bool=true) =
-    DataFrame(columns, _name2symbol(cnames), makeunique=makeunique, copycols=copycols)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    DataFrame(columns, _name2symbol(cnames), mergeduplicates=mergeduplicates, makeunique=makeunique, copycols=copycols)
 
 DataFrame(columns::AbstractVector{<:AbstractVector}, cnames::AbstractVector{Symbol};
-          makeunique::Bool=false, copycols::Bool=true)::DataFrame =
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)::DataFrame =
     DataFrame(collect(AbstractVector, columns),
-              Index(convert(Vector{Symbol}, cnames), makeunique=makeunique),
-              copycols=copycols)
+              UpdateIndex(convert(Vector{Symbol}, cnames), makeunique=makeunique),
+              copycols=copycols, mergeduplicates=mergeduplicates)
 
 DataFrame(columns::AbstractVector{<:AbstractVector}, cnames::AbstractVector;
-          makeunique::Bool=false, copycols::Bool=true) =
-    DataFrame(columns, _name2symbol(cnames); makeunique=makeunique, copycols=copycols)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    DataFrame(columns, _name2symbol(cnames); mergeduplicates=mergeduplicates, makeunique=makeunique, copycols=copycols)
 
 function DataFrame(columns::AbstractVector, cnames::Symbol; copycols::Bool=true)
     if cnames !== :auto
@@ -375,15 +460,15 @@ function DataFrame(columns::AbstractVector, cnames::Symbol; copycols::Bool=true)
 end
 
 function DataFrame(columns::AbstractMatrix, cnames::AbstractVector{Symbol};
-                   makeunique::Bool=false, copycols::Bool=true)
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     getter = copycols ? getindex : view
     return DataFrame(AbstractVector[getter(columns, :, i) for i in 1:size(columns, 2)],
-                     cnames, makeunique=makeunique, copycols=false)
+                     cnames, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=false)
 end
 
 DataFrame(columns::AbstractMatrix, cnames::AbstractVector;
-          makeunique::Bool=false, copycols::Bool=true) =
-    DataFrame(columns, _name2symbol(cnames); makeunique=makeunique, copycols=copycols)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    DataFrame(columns, _name2symbol(cnames); makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 function DataFrame(columns::AbstractMatrix, cnames::Symbol; copycols::Bool=true)
     if cnames !== :auto
@@ -392,7 +477,7 @@ function DataFrame(columns::AbstractMatrix, cnames::Symbol; copycols::Bool=true)
                             "positional argument is passed then the second " *
                             "argument must be a vector of column names or :auto"))
     end
-    return DataFrame(columns, gennames(size(columns, 2)), makeunique=false, copycols=copycols)
+    return DataFrame(columns, gennames(size(columns, 2)), mergeduplicates=nothing, copycols=copycols)
 end
 
 # Discontinued constructors
@@ -572,7 +657,7 @@ function _threaded_getindex(selected_rows::AbstractVector,
         return DataFrame(new_columns, idx, copycols=false)
     else
         return DataFrame(AbstractVector[df_columns[i][selected_rows] for i in selected_columns],
-                            idx, copycols=false)
+                         idx, copycols=false)
     end
 end
 
@@ -1202,14 +1287,30 @@ end
 
 # hcat! for 2 arguments, only a vector or a data frame is allowed
 function hcat!(df1::DataFrame, df2::AbstractDataFrame;
-               makeunique::Bool=false, copycols::Bool=true)
-    u = add_names(index(df1), index(df2), makeunique=makeunique)
+               makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
+    if makeunique || isnothing(mergeduplicates)
+        u = add_names(index(df1), index(df2), makeunique=makeunique)
+    else
+        u = _names(index(df2))
+    end
 
     _drop_all_nonnote_metadata!(df1)
     _keep_matching_table_note_metadata!(df1, df2)
-    for i in 1:length(u)
-        df1[!, u[i]] = copycols ? df2[:, i] : df2[!, i]
-        _copy_col_note_metadata!(df1, u[i], df2, i)
+    if !makeunique && isa(mergeduplicates,Function)
+        df1_names = _names(index(df1))
+        for nm in u
+            if nm ∈ df1_names
+                df1[!, nm] = mergeduplicates.(df1[!, nm], df2[!, nm])
+            else
+                df1[!, nm] = copycols ? df2[:, nm] : df2[!, nm]
+            end
+            _copy_col_note_metadata!(df1, nm, df2, nm)
+        end
+    else
+        for i in 1:length(u)
+            df1[!, u[i]] = copycols ? df2[:, i] : df2[!, i]
+            _copy_col_note_metadata!(df1, u[i], df2, i)
+        end
     end
 
     return df1
@@ -1217,31 +1318,31 @@ end
 
 # TODO: after deprecation remove AbstractVector methods
 
-function hcat!(df::DataFrame, x::AbstractVector; makeunique::Bool=false, copycols::Bool=true)
+function hcat!(df::DataFrame, x::AbstractVector; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     Base.depwarn("horizontal concatenation of data frame with a vector is deprecated. " *
                  "Pass DataFrame(x1=x) instead.", :hcat!)
     return hcat!(df, DataFrame(AbstractVector[x], [:x1], copycols=false),
-                 makeunique=makeunique, copycols=copycols)
+                 makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 end
 
-function hcat!(x::AbstractVector, df::DataFrame; makeunique::Bool=false, copycols::Bool=true)
+function hcat!(x::AbstractVector, df::DataFrame; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,copycols::Bool=true)
     Base.depwarn("horizontal concatenation of data frame with a vector is deprecated. " *
                  "Pass DataFrame(x1=x) instead.", :hcat!)
     return hcat!(DataFrame(AbstractVector[x], [:x1], copycols=copycols), df,
-                 makeunique=makeunique, copycols=copycols)
+                 makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 end
 
 # hcat! for 1-n arguments
-function hcat!(df::DataFrame; makeunique::Bool=false, copycols::Bool=true)
+function hcat!(df::DataFrame; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true)
     _drop_all_nonnote_metadata!(df)
     return df
 end
 
 hcat!(a::DataFrame, b::Union{AbstractDataFrame, AbstractVector},
       c::Union{AbstractDataFrame, AbstractVector}...;
-      makeunique::Bool=false, copycols::Bool=true) =
-    hcat!(hcat!(a, b, makeunique=makeunique, copycols=copycols),
-          c..., makeunique=makeunique, copycols=copycols)
+      makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, copycols::Bool=true) =
+    hcat!(hcat!(a, b, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols),
+          c..., makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=copycols)
 
 ##############################################################################
 ##
diff --git a/src/join/composer.jl b/src/join/composer.jl
index 3cd4e90b3..5dace8773 100644
--- a/src/join/composer.jl
+++ b/src/join/composer.jl
@@ -118,7 +118,8 @@ _rename_cols(old_names::AbstractVector{Symbol},
            for n in old_names]
 
 function _propagate_join_metadata!(joiner::DataFrameJoiner, dfr_noon::AbstractDataFrame,
-                                   res::DataFrame, kind::Symbol)
+                                   res::DataFrame, kind::Symbol;
+                                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, names=nothing)
     @assert kind == :left || kind == :right || kind == :outer || kind == :inner
 
     # The steps taken in this function are (all applies only to :note-style metadata):
@@ -174,8 +175,17 @@ function _propagate_join_metadata!(joiner::DataFrameJoiner, dfr_noon::AbstractDa
         end
     end
 
-    for i in 1:ncol(dfr_noon)
-        _copy_col_note_metadata!(res, ncol(joiner.dfl) + i, dfr_noon, i)
+    if isnothing(mergeduplicates)
+        for i in 1:ncol(dfr_noon)
+            _copy_col_note_metadata!(res, ncol(joiner.dfl) + i, dfr_noon, i)
+        end
+    else
+        map = UpdateIndex(names, makeunique=makeunique)
+        for i in 1:ncol(dfr_noon)
+            name = map.updates[ncol(joiner.dfl) + i]
+            dst = map.index.lookup[name]
+            _merge_col_note_metadata!(res, dst, dfr_noon, i)
+        end
     end
 
     if kind == :outer || kind == :inner
@@ -235,7 +245,7 @@ function _count_sortperm!(input::Vector{Int}, count::Vector,
 end
 
 function compose_inner_table(joiner::DataFrameJoiner,
-                             makeunique::Bool,
+                             makeunique, mergeduplicates,
                              left_rename::Union{Function, AbstractString, Symbol},
                              right_rename::Union{Function, AbstractString, Symbol},
                              order::Symbol)
@@ -278,9 +288,9 @@ function compose_inner_table(joiner::DataFrameJoiner,
 
     new_names = vcat(_rename_cols(_names(joiner.dfl), left_rename, joiner.left_on),
                      _rename_cols(_names(dfr_noon), right_rename))
-    res = DataFrame(cols, new_names, makeunique=makeunique, copycols=false)
+    res = DataFrame(cols, new_names, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=false)
 
-    _propagate_join_metadata!(joiner, dfr_noon, res, :inner)
+    _propagate_join_metadata!(joiner, dfr_noon, res, :inner, makeunique=makeunique, mergeduplicates=mergeduplicates, names=new_names)
     return res
 end
 
@@ -292,7 +302,7 @@ function find_missing_idxs(present::Vector{Int}, target_len::Int)
     return _findall(not_seen)
 end
 
-function compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique::Bool,
+function compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique, mergeduplicates,
                               left_rename::Union{Function, AbstractString, Symbol},
                               right_rename::Union{Function, AbstractString, Symbol},
                               indicator::Union{Nothing, Symbol, AbstractString},
@@ -314,12 +324,12 @@ function compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique:
     else
         rightonly_ixs = 1:0
     end
-    return _compose_joined_table(joiner, kind, makeunique, left_rename, right_rename,
+    return _compose_joined_table(joiner, kind, makeunique, mergeduplicates, left_rename, right_rename,
                                  indicator, left_ixs, right_ixs,
                                  leftonly_ixs, rightonly_ixs, order)
 end
 
-function _compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique::Bool,
+function _compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique, mergeduplicates,
                                left_rename::Union{Function, AbstractString, Symbol},
                                right_rename::Union{Function, AbstractString, Symbol},
                                indicator::Union{Nothing, Symbol, AbstractString},
@@ -440,14 +450,14 @@ function _compose_joined_table(joiner::DataFrameJoiner, kind::Symbol, makeunique
 
     new_names = vcat(_rename_cols(_names(joiner.dfl), left_rename, joiner.left_on),
                      _rename_cols(_names(dfr_noon), right_rename))
-    res = DataFrame(cols, new_names, makeunique=makeunique, copycols=false)
+    res = DataFrame(cols, new_names, makeunique=makeunique, mergeduplicates=mergeduplicates, copycols=false)
 
     if new_order !== nothing
         isnothing(src_indicator) || permute!(src_indicator, new_order)
         permute!(res, new_order)
     end
 
-    _propagate_join_metadata!(joiner, dfr_noon, res, kind)
+    _propagate_join_metadata!(joiner, dfr_noon, res, kind, makeunique=makeunique, mergeduplicates=mergeduplicates, names=new_names)
 
     return res, src_indicator
 end
@@ -484,7 +494,8 @@ function _sort_compose_helper(fillval::Int, # value to use to fill unused indice
 end
 
 function _join(df1::AbstractDataFrame, df2::AbstractDataFrame;
-               on::Union{<:OnType, AbstractVector}, kind::Symbol, makeunique::Bool,
+               on::Union{<:OnType, AbstractVector}, kind::Symbol, 
+               makeunique::Bool, mergeduplicates,
                indicator::Union{Nothing, Symbol, AbstractString},
                validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}},
                left_rename::Union{Function, AbstractString, Symbol},
@@ -579,16 +590,16 @@ function _join(df1::AbstractDataFrame, df2::AbstractDataFrame;
 
     src_indicator = nothing
     if kind == :inner
-        joined = compose_inner_table(joiner, makeunique, left_rename, right_rename, order)
+        joined = compose_inner_table(joiner, makeunique, mergeduplicates, left_rename, right_rename, order)
     elseif kind == :left
         joined, src_indicator =
-            compose_joined_table(joiner, kind, makeunique, left_rename, right_rename, indicator, order)
+            compose_joined_table(joiner, kind, makeunique, mergeduplicates, left_rename, right_rename, indicator, order)
     elseif kind == :right
         joined, src_indicator =
-            compose_joined_table(joiner, kind, makeunique, left_rename, right_rename, indicator, order)
+            compose_joined_table(joiner, kind, makeunique, mergeduplicates, left_rename, right_rename, indicator, order)
     elseif kind == :outer
         joined, src_indicator =
-            compose_joined_table(joiner, kind, makeunique, left_rename, right_rename, indicator, order)
+            compose_joined_table(joiner, kind, makeunique, mergeduplicates, left_rename, right_rename, indicator, order)
     elseif kind == :semi
         joined = joiner.dfl[find_semi_rows(joiner), :]
     elseif kind == :anti
@@ -614,12 +625,16 @@ function _join(df1::AbstractDataFrame, df2::AbstractDataFrame;
             end
         end
 
-        if hasproperty(joined, unique_indicator)
-            throw(ArgumentError("joined data frame already has column " *
-                                ":$unique_indicator. Pass makeunique=true to " *
-                                "make it unique using a suffix automatically."))
+        if unique_indicator == indicator && !isnothing(mergeduplicates)
+            joined[!, indicator] = mergeduplicates.(joined[!, indicator], indicatorcol)
+        else
+            if hasproperty(joined, unique_indicator)
+                throw(ArgumentError("joined data frame already has column " *
+                                    ":$unique_indicator. Pass makeunique=true to " *
+                                    "make it unique using a suffix automatically."))
+            end
+            joined[!, unique_indicator] = indicatorcol
         end
-        joined[!, unique_indicator] = indicatorcol
     else
         @assert isnothing(src_indicator)
     end
@@ -628,10 +643,10 @@ function _join(df1::AbstractDataFrame, df2::AbstractDataFrame;
 end
 
 """
-    innerjoin(df1, df2; on, makeunique=false, validate=(false, false),
+    innerjoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, validate=(false, false),
               renamecols=(identity => identity), matchmissing=:error,
               order=:undefined)
-    innerjoin(df1, df2, dfs...; on, makeunique=false,
+    innerjoin(df1, df2, dfs...; on, makeunique=false, mergeduplicates=nothing,
               validate=(false, false), matchmissing=:error,
               order=:undefined)
 
@@ -653,10 +668,16 @@ change in future releases.
   can be used instead of a name, for the case where a key has different names
   in `df1` and `df2` (it is allowed to mix names and name pairs in a vector).
   Key values are compared using `isequal`. `on` is a required argument.
-- `makeunique` : if `false` (the default), an error will be raised
+- `makeunique` : if `false` (the default), an error will be raised or the `mergeduplicates`
+  function will be used to combine columns if provided;
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of each of the duplicated
+  columns which will be passed as a vararg, the return value is then used to fill the column. 
+  Because these joins are performed recursively, this `mergeduplicates` function will only 
+  combine two values at a time. 
 - `validate` : whether to check that columns passed as the `on` argument
   define unique keys in each input data frame (according to `isequal`).
   Can be a tuple or a pair, with the first element indicating whether to
@@ -755,7 +776,7 @@ julia> innerjoin(name, job2, on = [:ID => :identifier], renamecols = uppercase =
 """
 function innerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
                    on::Union{<:OnType, AbstractVector} = Symbol[],
-                   makeunique::Bool=false,
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
                    renamecols::Pair=identity => identity,
                    matchmissing::Symbol=:error,
@@ -764,7 +785,7 @@ function innerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
         throw(ArgumentError("renamecols keyword argument must be a `Pair` " *
                             "containing functions, strings, or `Symbol`s"))
     end
-    return _join(df1, df2, on=on, kind=:inner, makeunique=makeunique,
+    return _join(df1, df2, on=on, kind=:inner, mergeduplicates=mergeduplicates, makeunique=makeunique,
                  indicator=nothing, validate=validate,
                  left_rename=first(renamecols), right_rename=last(renamecols),
                  matchmissing=matchmissing, order=order)
@@ -772,16 +793,17 @@ end
 
 function innerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame, dfs::AbstractDataFrame...;
                    on::Union{<:OnType, AbstractVector} = Symbol[],
-                   makeunique::Bool=false,
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
                    matchmissing::Symbol=:error,
                    order::Symbol=:undefined)
     @assert !isempty(dfs)
-    res = innerjoin(df1, df2, on=on, makeunique=makeunique, validate=validate,
+    _check_makeunique_args(mergeduplicates, makeunique)
+    res = innerjoin(df1, df2, on=on, mergeduplicates=mergeduplicates, validate=validate,
                     matchmissing=matchmissing,
                     order=order === :right ? :undefined : order)
     for (i, dfn) in enumerate(dfs)
-        res = innerjoin(res, dfn, on=on, makeunique=makeunique, validate=validate,
+        res = innerjoin(res, dfn, on=on, mergeduplicates=mergeduplicates, validate=validate,
                         matchmissing=matchmissing,
                         order= order === :right ?
                                (i == length(dfs) ? :right : :undefined) :
@@ -791,7 +813,7 @@ function innerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame, dfs::Abstract
 end
 
 """
-    leftjoin(df1, df2; on, makeunique=false, source=nothing, validate=(false, false),
+    leftjoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, source=nothing, validate=(false, false),
              renamecols=(identity => identity), matchmissing=:error, order=:undefined)
 
 Perform a left join of two data frame objects and return a `DataFrame` containing
@@ -811,10 +833,14 @@ change in future releases.
   the case where a key has different names in `df1` and `df2` (it is allowed to
   mix names and name pairs in a vector). Key values are compared using
   `isequal`. `on` is a required argument.
-- `makeunique` : if `false` (the default), an error will be raised
+- `makeunique` : if `false` (the default), an error will be raised or the `mergeduplicates`
+  function will be used to combine columns if provided;
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of each of the duplicated
+  columns which will be passed as a vararg, the return value is then used to fill the column. 
 - `source` : Default: `nothing`. If a `Symbol` or string, adds indicator
   column with the given name, for whether a row appeared in only `df1` (`"left_only"`)
   or in both (`"both"`). If the name is already in use,
@@ -915,12 +941,14 @@ julia> leftjoin(name, job2, on = [:ID => :identifier], renamecols = uppercase =>
 ```
 """
 function leftjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-                  on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+                  on::Union{<:OnType, AbstractVector} = Symbol[], 
+                  makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                   source::Union{Nothing, Symbol, AbstractString}=nothing,
                   indicator::Union{Nothing, Symbol, AbstractString}=nothing,
                   validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
                   renamecols::Pair=identity => identity, matchmissing::Symbol=:error,
                   order::Symbol=:undefined)
+
     if !all(x -> x isa Union{Function, AbstractString, Symbol}, renamecols)
         throw(ArgumentError("renamecols keyword argument must be a `Pair` " *
                             "containing functions, strings, or `Symbol`s"))
@@ -937,14 +965,14 @@ function leftjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
                             "It is not allowed to pass both `indicator` and `source` " *
                             "keyword arguments at the same time."))
     end
-    return _join(df1, df2, on=on, kind=:left, makeunique=makeunique,
+    return _join(df1, df2, on=on, kind=:left, mergeduplicates=mergeduplicates, makeunique=makeunique,
                  indicator=source, validate=validate,
                  left_rename=first(renamecols), right_rename=last(renamecols),
                  matchmissing=matchmissing, order=order)
 end
 
 """
-    rightjoin(df1, df2; on, makeunique=false, source=nothing,
+    rightjoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, source=nothing,
               validate=(false, false), renamecols=(identity => identity),
               matchmissing=:error, order=:undefined)
 
@@ -967,10 +995,14 @@ change in future releases.
   the case where a key has different names in `df1` and `df2` (it is allowed to
   mix names and name pairs in a vector). Key values are compared using
   `isequal`. `on` is a required argument.
-- `makeunique` : if `false` (the default), an error will be raised
+  - `makeunique` : if `false` (the default), an error will be raised or the `mergeduplicates`
+  function will be used to combine columns if provided;
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of each of the duplicated
+  columns which will be passed as a vararg, the return value is then used to fill the column. 
 - `source` : Default: `nothing`. If a `Symbol` or string, adds indicator
   column with the given name for whether a row appeared in only `df2` (`"right_only"`)
   or in both (`"both"`). If the name is already in use,
@@ -1071,7 +1103,8 @@ julia> rightjoin(name, job2, on = [:ID => :identifier], renamecols = uppercase =
 ```
 """
 function rightjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-                   on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+                   on::Union{<:OnType, AbstractVector} = Symbol[], 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    source::Union{Nothing, Symbol, AbstractString}=nothing,
                    indicator::Union{Nothing, Symbol, AbstractString}=nothing,
                    validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
@@ -1093,16 +1126,16 @@ function rightjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
                             "It is not allowed to pass both `indicator` and `source` " *
                             "keyword arguments at the same time."))
     end
-    return _join(df1, df2, on=on, kind=:right, makeunique=makeunique,
+    return _join(df1, df2, on=on, kind=:right, mergeduplicates=mergeduplicates, makeunique=makeunique,
                  indicator=source, validate=validate,
                  left_rename=first(renamecols), right_rename=last(renamecols),
                  matchmissing=matchmissing, order=order)
 end
 
 """
-    outerjoin(df1, df2; on, makeunique=false, source=nothing, validate=(false, false),
+    outerjoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, source=nothing, validate=(false, false),
               renamecols=(identity => identity), matchmissing=:error, order=:undefined)
-    outerjoin(df1, df2, dfs...; on, makeunique = false,
+    outerjoin(df1, df2, dfs...; on, makeunique=false, mergeduplicates=nothing, 
               validate = (false, false), matchmissing=:error, order=:undefined)
 
 Perform an outer join of two or more data frame objects and return a `DataFrame`
@@ -1125,10 +1158,16 @@ This behavior may change in future releases.
   can be used instead of a name, for the case where a key has different names
   in `df1` and `df2` (it is allowed to mix names and name pairs in a vector).
   Key values are compared using `isequal`. `on` is a required argument.
-- `makeunique` : if `false` (the default), an error will be raised
+- `makeunique` : if `false` (the default), an error will be raised or the `mergeduplicates`
+  function will be used to combine columns if provided;
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of each of the duplicated
+  columns which will be passed as a vararg, the return value is then used to fill the column. 
+  Because these joins are performed recursively, this `mergeduplicates` function will only 
+  combine two values at a time.
 - `source` : Default: `nothing`. If a `Symbol` or string, adds indicator
   column with the given name for whether a row appeared in only `df1` (`"left_only"`),
   only `df2` (`"right_only"`) or in both (`"both"`). If the name is already in use,
@@ -1240,7 +1279,8 @@ julia> outerjoin(name, job2, on = [:ID => :identifier], renamecols = uppercase =
 ```
 """
 function outerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-                   on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+                   on::Union{<:OnType, AbstractVector} = Symbol[], 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    source::Union{Nothing, Symbol, AbstractString}=nothing,
                    indicator::Union{Nothing, Symbol, AbstractString}=nothing,
                    validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
@@ -1262,27 +1302,29 @@ function outerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
                             "It is not allowed to pass both `indicator` and `source` " *
                             "keyword arguments at the same time."))
     end
-    return _join(df1, df2, on=on, kind=:outer, makeunique=makeunique,
+    return _join(df1, df2, on=on, kind=:outer, mergeduplicates=mergeduplicates, makeunique=makeunique,
                  indicator=source, validate=validate,
                  left_rename=first(renamecols), right_rename=last(renamecols),
                  matchmissing=matchmissing, order=order)
 end
 
 function outerjoin(df1::AbstractDataFrame, df2::AbstractDataFrame, dfs::AbstractDataFrame...;
-                   on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+                   on::Union{<:OnType, AbstractVector} = Symbol[], 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
                    matchmissing::Symbol=:error, order::Symbol=:undefined)
-    res = outerjoin(df1, df2, on=on, makeunique=makeunique, validate=validate,
+    _check_makeunique_args(mergeduplicates, makeunique)
+    res = outerjoin(df1, df2, on=on, mergeduplicates=mergeduplicates, validate=validate,
                         matchmissing=matchmissing, order=order)
     for dfn in dfs
-        res = outerjoin(res, dfn, on=on, makeunique=makeunique, validate=validate,
+        res = outerjoin(res, dfn, on=on, mergeduplicates=mergeduplicates, validate=validate,
                         matchmissing=matchmissing, order=order)
     end
     return res
 end
 
 """
-    semijoin(df1, df2; on, makeunique=false, validate=(false, false), matchmissing=:error)
+    semijoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, validate=(false, false), matchmissing=:error)
 
 Perform a semi join of two data frame objects and return a `DataFrame`
 containing the result. A semi join returns the subset of rows of `df1` that
@@ -1384,16 +1426,16 @@ julia> semijoin(name, job2, on = [:ID => :identifier])
 ```
 """
 semijoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-         on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+         on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
          validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
          matchmissing::Symbol=:error) =
-    _join(df1, df2, on=on, kind=:semi, makeunique=makeunique,
+    _join(df1, df2, on=on, kind=:semi, mergeduplicates=mergeduplicates, makeunique=makeunique,
           indicator=nothing, validate=validate,
           left_rename=identity, right_rename=identity, matchmissing=matchmissing,
           order=:left)
 
 """
-    antijoin(df1, df2; on, makeunique=false, validate=(false, false), matchmissing=:error)
+    antijoin(df1, df2; on, makeunique=false, mergeduplicates=nothing, validate=(false, false), matchmissing=:error)
 
 Perform an anti join of two data frame objects and return a `DataFrame`
 containing the result. An anti join returns the subset of rows of `df1` that do
@@ -1488,10 +1530,11 @@ julia> antijoin(name, job2, on = [:ID => :identifier])
 ```
 """
 antijoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-         on::Union{<:OnType, AbstractVector} = Symbol[], makeunique::Bool=false,
+         on::Union{<:OnType, AbstractVector} = Symbol[],
+         makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
          validate::Union{Pair{Bool, Bool}, Tuple{Bool, Bool}}=(false, false),
          matchmissing::Symbol=:error) =
-    _join(df1, df2, on=on, kind=:anti, makeunique=makeunique,
+    _join(df1, df2, on=on, kind=:anti, mergeduplicates=mergeduplicates, makeunique=makeunique,
           indicator=nothing, validate=validate,
           left_rename=identity, right_rename=identity,
           matchmissing=matchmissing,
@@ -1499,7 +1542,7 @@ antijoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
 
 """
     crossjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-              makeunique::Bool=false, renamecols=identity => identity)
+              makeunique::Bool=false, mergeduplicates=nothing, renamecols=identity => identity)
     crossjoin(df1, df2, dfs...; makeunique = false)
 
 Perform a cross join of two or more data frame objects and return a `DataFrame`
@@ -1512,10 +1555,16 @@ dimension that changes the fastest.
 - `df1`, `df2`, `dfs...` : the `AbstractDataFrames` to be joined
 
 # Keyword Arguments
-- `makeunique` : if `false` (the default), an error will be raised
+- `makeunique` : if `false` (the default), an error will be raised or the `mergeduplicates`
+  function will be used to combine columns if provided;
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of each of the duplicated
+  columns which will be passed as a vararg, the return value is then used to fill the column. 
+  Because these joins are performed recursively, this `mergeduplicates` function will only 
+  combine two values at a time.
 - `renamecols` : a `Pair` specifying how columns of left and right data frames should
   be renamed in the resulting data frame. Each element of the pair can be a
   string or a `Symbol` can be passed in which case it is appended to the original
@@ -1565,22 +1614,25 @@ julia> crossjoin(df1, df2)
 ```
 """
 function crossjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
-                   makeunique::Bool=false, renamecols::Pair=identity => identity)
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing, renamecols::Pair=identity => identity)
     _check_consistency(df1)
     _check_consistency(df2)
+    _check_makeunique_args(mergeduplicates, makeunique)
     r1, r2 = size(df1, 1), size(df2, 1)
 
     new_names = vcat(_rename_cols(_names(df1), first(renamecols)),
                      _rename_cols(_names(df2), last(renamecols)))
     cols = Any[[repeat(c, inner=r2) for c in eachcol(df1)];
                [repeat(c, outer=r1) for c in eachcol(df2)]]
-    res = DataFrame(cols, new_names, copycols=false, makeunique=makeunique)
+    res = DataFrame(cols, new_names, copycols=false, makeunique=makeunique, mergeduplicates=mergeduplicates)
 
     for i in 1:ncol(df1)
         _copy_col_note_metadata!(res, i, df1, i)
     end
-    for i in 1:ncol(df2)
-        _copy_col_note_metadata!(res, ncol(df1) + i, df2, i)
+    if isnothing(mergeduplicates)
+        for i in 1:ncol(df2)
+            _copy_col_note_metadata!(res, ncol(df1) + i, df2, i)
+        end
     end
 
     _merge_matching_table_note_metadata!(res, (df1, df2))
@@ -1589,8 +1641,8 @@ function crossjoin(df1::AbstractDataFrame, df2::AbstractDataFrame;
 end
 
 crossjoin(df1::AbstractDataFrame, df2::AbstractDataFrame, dfs::AbstractDataFrame...;
-          makeunique::Bool=false) =
-    crossjoin(crossjoin(df1, df2, makeunique=makeunique), dfs..., makeunique=makeunique)
+          makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing) =
+    crossjoin(crossjoin(df1, df2, makeunique=makeunique, mergeduplicates=mergeduplicates), dfs..., makeunique=makeunique, mergeduplicates=mergeduplicates)
 
 # an explicit error is thrown as join was supported in the past
 Base.join(df1::AbstractDataFrame, df2::AbstractDataFrame, dfs::AbstractDataFrame...;
diff --git a/src/join/inplace.jl b/src/join/inplace.jl
index 9f1a9e0c6..42d247915 100644
--- a/src/join/inplace.jl
+++ b/src/join/inplace.jl
@@ -1,5 +1,5 @@
 """
-    leftjoin!(df1, df2; on, makeunique=false, source=nothing,
+    leftjoin!(df1, df2; on, makeunique=false, mergeduplicates=nothing, source=nothing,
               matchmissing=:error)
 
 
@@ -25,10 +25,13 @@ added to `df1`.
   if duplicate names are found in columns not joined on;
   if `true`, duplicate names will be suffixed with `_i`
   (`i` starting at 1 for the first duplicate).
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of all of the duplicated
+  columns which will be passed as a varargs. The return value is used.
 - `source` : Default: `nothing`. If a `Symbol` or string, adds indicator
   column with the given name, for whether a row appeared in only `df1` (`"left_only"`)
   or in both (`"both"`). If the name is already in use,
-  the column name will be modified if `makeunique=true`.
+  the column name will be modified if `makeunique=true` or `makeunique=true`.
 - `matchmissing` : if equal to `:error` throw an error if `missing` is present
   in `on` columns; if equal to `:equal` then `missing` is allowed and missings are
   matched; if equal to `:notequal` then missings are dropped in `df2` `on` columns.
@@ -95,12 +98,14 @@ julia> leftjoin!(name, job2, on = :ID => :identifier, makeunique=true, source=:s
 ```
 """
 function leftjoin!(df1::AbstractDataFrame, df2::AbstractDataFrame;
-                   on::Union{<:OnType, AbstractVector}=Symbol[], makeunique::Bool=false,
+                   on::Union{<:OnType, AbstractVector}=Symbol[], 
+                   makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
                    source::Union{Nothing, Symbol, AbstractString}=nothing,
                    matchmissing::Symbol=:error)
 
     _check_consistency(df1)
     _check_consistency(df2)
+    _check_makeunique_args(mergeduplicates, makeunique)
 
     if !is_column_insertion_allowed(df1)
         throw(ArgumentError("leftjoin! is only supported if `df1` is a `DataFrame`, " *
@@ -114,7 +119,7 @@ function leftjoin!(df1::AbstractDataFrame, df2::AbstractDataFrame;
     joiner = DataFrameJoiner(df1, df2, on, matchmissing, :left)
 
     right_noon_names = names(joiner.dfr, Not(joiner.right_on))
-    if !(makeunique || isempty(intersect(right_noon_names, names(df1))))
+    if !makeunique && isnothing(mergeduplicates) && !isempty(intersect(right_noon_names, names(df1)))
         throw(ArgumentError("the following columns are present in both " *
                             "left and right data frames but not listed in `on`: " *
                             join(intersect(right_noon_names, names(df1)), ", ") *
@@ -134,7 +139,7 @@ function leftjoin!(df1::AbstractDataFrame, df2::AbstractDataFrame;
         rcol_joined = compose_joined_rcol!(rcol, similar_missing(rcol, nrow(df1)),
                                           right_ixs)
         # if df1 isa SubDataFrame we must copy columns
-        insertcols!(df1, colname => rcol_joined, makeunique=makeunique,
+        insertcols!(df1, colname => rcol_joined, makeunique=makeunique, mergeduplicates=mergeduplicates,
                     copycols=!(df1 isa DataFrame))
         # need to call parent as df1 can be a SubDataFrame
         _copy_col_note_metadata!(parent(df1), ncol(df1), joiner.dfr, colname)
@@ -158,11 +163,18 @@ function leftjoin!(df1::AbstractDataFrame, df2::AbstractDataFrame;
         end
 
         if hasproperty(df1, unique_indicator)
-            throw(ArgumentError("joined data frame already has column " *
-                                ":$unique_indicator. Pass makeunique=true to " *
-                                "make it unique using a suffix automatically."))
+            if !isnothing(mergeduplicates)
+                df1[!, unique_indicator] = mergeduplicates.(df1[!, unique_indicator], indicatorcol)
+            else
+                throw(ArgumentError("joined data frame already has column " *
+                                    ":$unique_indicator. Pass makeunique=true to " *
+                                    "make it unique using a suffix automatically or set mergeduplicates " *
+                                    "to a function."))
+            end
+        else
+            df1[!, unique_indicator] = indicatorcol
         end
-        df1[!, unique_indicator] = indicatorcol
+        
     end
 
     return df1
@@ -192,3 +204,107 @@ function compose_joined_rcol!(rcol::AbstractVector,
     end
     return rcol_joined
 end
+
+"""
+    outerjoin!(df1, df2; on, makeunique=false, mergeduplicates=nothing, source=nothing, 
+               matchmissing=:error)
+
+Perform an outer join of two data frame objects by updating into `df1`.
+An outer join includes rows with keys that appear in either
+of the passed data frames.
+
+The order of rows in the result is undefined and may change in future releases.
+
+In the returned data frame the type of the columns on which the data frames are
+joined is determined by the element type of these columns both `df1` and `df2`.
+This behavior may change in future releases.
+
+# Arguments
+- `df1`, `df2` : the `AbstractDataFrames` to be joined
+
+# Keyword Arguments
+- `on` : The names of the key columns on which to join the data frames.
+  This can be a single name, or a vector of names (for joining on multiple
+  columns). When joining only two data frames, a `left=>right` pair of names
+  can be used instead of a name, for the case where a key has different names
+  in `df1` and `df2` (it is allowed to mix names and name pairs in a vector).
+  Key values are compared using `isequal`. `on` is a required argument.
+- `makeunique` : if `false` (the default), an error will be raised
+  if duplicate names are found in columns not joined on;
+  if `true`, duplicate names will be suffixed with `_i`
+  (`i` starting at 1 for the first duplicate). (deprecated)
+- `mergeduplicates` : defines what to do if `name` already exists in `df` and `makeunique`
+  is false. It should be given a Function that combines the values of all of the duplicated
+  columns which will be passed as a varargs. The return value is used.
+- `source` : Default: `nothing`. If a `Symbol` or string, adds indicator
+  column with the given name for whether a row appeared in only `df1` (`"left_only"`),
+  only `df2` (`"right_only"`) or in both (`"both"`). If the name is already in use,
+  the column name will be modified if `makeunique=true`.
+  This argument is only supported when joining exactly two data frames.
+- `matchmissing` : if equal to `:error` throw an error if `missing` is present
+  in `on` columns; if equal to `:equal` then `missing` is allowed and missings are
+  matched.
+
+All columns of the returned data frame will support missing values.
+
+It is not allowed to join on columns that contain `NaN` or `-0.0` in real or
+imaginary part of the number. If you need to perform a join on such values use
+CategoricalArrays.jl and transform a column containing such values into a
+`CategoricalVector`.
+
+When merging `on` categorical columns that differ in the ordering of their
+levels, the ordering of the left data frame takes precedence over the ordering
+of the right data frame.
+
+Metadata: table-level `:note`-style metadata and column-level `:note`-style metadata
+for key columns is preserved only for keys which are defined in all passed tables
+and have the same value.
+Column-level `:note`-style metadata is preserved for all other columns.
+
+See also: [`innerjoin`](@ref), [`leftjoin`](@ref), [`rightjoin`](@ref),
+          [`semijoin`](@ref), [`antijoin`](@ref), [`crossjoin`](@ref).
+
+# Examples
+```jldoctest
+julia> name = DataFrame(ID=[1, 2, 3], Name=["John Doe", "Jane Doe", "Joe Blogs"])
+3×2 DataFrame
+ Row │ ID     Name
+     │ Int64  String
+─────┼──────────────────
+   1 │     1  John Doe
+   2 │     2  Jane Doe
+   3 │     3  Joe Blogs
+
+julia> job = DataFrame(ID=[1, 2, 4], Job=["Lawyer", "Doctor", "Farmer"])
+3×2 DataFrame
+ Row │ ID     Job
+     │ Int64  String
+─────┼───────────────
+   1 │     1  Lawyer
+   2 │     2  Doctor
+   3 │     4  Farmer
+
+julia> outerjoin!(name, job, on = :ID)
+4×3 DataFrame
+ Row │ ID     Name       Job
+     │ Int64  String?    String?
+─────┼───────────────────────────
+   1 │     1  John Doe   Lawyer
+   2 │     2  Jane Doe   Doctor
+   3 │     3  Joe Blogs  missing
+   4 │     4  missing    Farmer
+```
+"""
+function outerjoin!(df1::AbstractDataFrame, df2::AbstractDataFrame;
+    on::Union{<:OnType, AbstractVector}=Symbol[], 
+    makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
+    source::Union{Nothing, Symbol, AbstractString}=nothing,
+    matchmissing::Symbol=:error)
+
+    leftjoin!(df1, df2, on=on, makeunique=makeunique, mergeduplicates=mergeduplicates, source=source, matchmissing=matchmissing)
+
+    aj = antijoin(df2, df1, on=on, makeunique=makeunique, mergeduplicates=mergeduplicates, matchmissing=matchmissing)
+    append!(df1, aj)
+
+    return df1
+end
\ No newline at end of file
diff --git a/src/other/index.jl b/src/other/index.jl
index 51aa3a31c..d97b8d8e7 100644
--- a/src/other/index.jl
+++ b/src/other/index.jl
@@ -33,6 +33,8 @@ Index() = Index(Dict{Symbol, Int}(), Symbol[])
 Base.length(x::Index) = length(x.names)
 Base.names(x::Index) = string.(x.names)
 
+column_length(x::Index) = length(x.names)
+
 # _names returns Vector{Symbol}
 _names(x::Index) = x.names
 
@@ -40,7 +42,6 @@ Base.copy(x::Index) = Index(copy(x.lookup), copy(x.names))
 Base.isequal(x::AbstractIndex, y::AbstractIndex) = _names(x) == _names(y) # it is enough to check names
 Base.:(==)(x::AbstractIndex, y::AbstractIndex) = isequal(x, y)
 
-
 function rename!(x::Index, nms::AbstractVector{Symbol}; makeunique::Bool=false)
     if !makeunique
         if length(unique(nms)) != length(nms)
@@ -241,7 +242,7 @@ end
 
 @inline _getindex_cols(x::AbstractIndex, idx::Any) = x[idx]
 @inline _getindex_cols(x::AbstractIndex, idx::Function) = findall(idx, names(x))
-# the definition below is needed because `:` is a Function
+# the definition below is needed because `:` is a `Function`
 @inline _getindex_cols(x::AbstractIndex, idx::Colon) = x[idx]
 
 @inline function Base.getindex(x::AbstractIndex, idx::Cols)
@@ -432,7 +433,7 @@ end
 
 # return Vector{Symbol} of names from add_ind that do not clash with `ind`.
 # if `makeunique=false` error on collision
-# if `makeunique=false` generate new names that are deduplicated
+# if `make=:makeunique` generate new names that are deduplicated
 function add_names(ind::Index, add_ind::AbstractIndex; makeunique::Bool=false)
     u = copy(_names(add_ind))
 
@@ -451,6 +452,7 @@ function add_names(ind::Index, add_ind::AbstractIndex; makeunique::Bool=false)
             throw(ArgumentError(msg))
         end
     end
+
     for i in dups
         nm = u[i]
         k = 1
diff --git a/src/other/metadata.jl b/src/other/metadata.jl
index 60a283d5a..38ac84876 100644
--- a/src/other/metadata.jl
+++ b/src/other/metadata.jl
@@ -705,6 +705,24 @@ function _copy_col_note_metadata!(dst::DataFrame, dst_col, src, src_col)
     return nothing
 end
 
+# copy column-level :note-style metadata from Tables.jl table src to dst
+# from column src_col to dst_col
+# discarding previous metadata contents of dst
+function _merge_col_note_metadata!(dst::DataFrame, dst_col, src, src_col)
+    #emptycolmetadata!(dst, dst_col)
+    metadata = colmetadata(dst, dst_col)
+    if DataAPI.colmetadatasupport(typeof(src)).read
+        for key in colmetadatakeys(src, src_col)
+            val, style = colmetadata(src, src_col, key, style=true)
+            # TODO write only if does not overwrite
+            if style === :note && !haskey(metadata, key)
+                colmetadata!(dst, dst_col, key, val, style=:note)
+            end
+        end
+    end
+    return nothing
+end
+
 # this is a function used to copy table-level and column-level :note-style metadata
 # from Tables.jl table src to dst, discarding previous metadata contents of dst
 function _copy_all_note_metadata!(dst::DataFrame, src)
diff --git a/src/other/tables.jl b/src/other/tables.jl
index 4213c9888..3953e50d2 100644
--- a/src/other/tables.jl
+++ b/src/other/tables.jl
@@ -64,11 +64,11 @@ end
 
 # the logic here relies on the fact that Tables.CopiedColumns
 # is the only exception for default copycols value 
-DataFrame(x, cnames::AbstractVector; makeunique::Bool=false,
+DataFrame(x, cnames::AbstractVector; makeunique::Bool=false, mergeduplicates::MergeDuplicates=nothing,
           copycols::Union{Nothing, Bool}=nothing) =
     rename!(DataFrame(x, copycols=something(copycols, !(x isa Tables.CopiedColumns))),
             _name2symbol(cnames),
-            makeunique=makeunique)
+            makeunique=makeunique, mergeduplicates=mergeduplicates)
 
 function Base.append!(df::DataFrame, table; cols::Symbol=:setequal,
                       promote::Bool=(cols in [:union, :subset]))
diff --git a/src/other/utils.jl b/src/other/utils.jl
index 455c406f4..29609748e 100644
--- a/src/other/utils.jl
+++ b/src/other/utils.jl
@@ -72,6 +72,21 @@ struct AsTable
     end
 end
 
+"""
+MergeDuplicates = Union{Nothing,Function}
+
+Wherever the `mergeduplicates` keyword argument is available it is either `nothing` or
+a `Function` that will be executed to combine duplicated columns (when `makeunique=false`)
+"""
+MergeDuplicates = Union{Nothing,Function}
+
+function _check_makeunique_args(mergeduplicates::MergeDuplicates, makeunique::Bool=false)
+    if makeunique && !isnothing(mergeduplicates)
+        throw(ArgumentError("mergeduplicates should not  be set if makeunique=true"))
+    end
+    mergeduplicates
+end
+
 Base.broadcastable(x::AsTable) = Ref(x)
 
 function make_unique!(names::Vector{Symbol}, src::AbstractVector{Symbol};
@@ -102,15 +117,19 @@ function make_unique!(names::Vector{Symbol}, src::AbstractVector{Symbol};
 
     for i in dups
         nm = src[i]
-        k = 1
-        while true
-            newnm = Symbol("$(nm)_$k")
-            if !in(newnm, seen)
-                names[i] = newnm
-                push!(seen, newnm)
-                break
+        if makeunique
+            k = 1
+            while true
+                newnm = Symbol("$(nm)_$k")
+                if !in(newnm, seen)
+                    names[i] = newnm
+                    push!(seen, newnm)
+                    break
+                end
+                k += 1
             end
-            k += 1
+        else
+            names[i] = nm
         end
     end
 
diff --git a/test/cat.jl b/test/cat.jl
index b5aa1cfd9..da8ca2a1e 100644
--- a/test/cat.jl
+++ b/test/cat.jl
@@ -3,6 +3,8 @@ module TestCat
 using Test, Random, DataFrames, CategoricalArrays
 const ≅ = isequal
 
+update_missing = (x...) -> coalesce(reverse(x)...)
+
 @testset "hcat" begin
     nvint = [1, 2, missing, 4]
     nvstr = ["one", "two", missing, "four"]
@@ -20,6 +22,12 @@ const ≅ = isequal
     @test dfh[!, :x1] ≅ df3[!, :x1]
     @test dfh ≅ DataFrames.hcat!(DataFrame(), df3, df4, makeunique=true)
 
+    dfhu = hcat(df3, df4, mergeduplicates=update_missing)
+    @test ref_df ≅ df3 # make sure that df3 is not mutated by hcat
+    @test size(dfhu, 2) == 2
+    @test names(dfhu) ≅ ["x1", "x2"]
+    @test ! (dfhu[!, :x1] ≅ df3[!, :x1])
+
     dfa = DataFrame(a=[1, 2])
     dfb = DataFrame(b=[3, missing])
     @test hcat(dfa, dfb) ≅ [dfa dfb]
@@ -29,7 +37,15 @@ const ≅ = isequal
     @test dfh3 ≅ hcat(dfh, df5, makeunique=true)
     @test dfh3 ≅ DataFrames.hcat!(DataFrame(), df3, df4, df5, makeunique=true)
 
+    dfh = hcat(df3, df4, mergeduplicates=sum∘tuple)
+    @test dfh ≅ DataFrames.hcat!(DataFrame(), df3, df4, mergeduplicates=sum∘tuple)
+    
     @test df2 ≅ DataFrames.hcat!(df2, makeunique=true)
+
+    dfh3 = hcat(df3, df4, df5, mergeduplicates=update_missing)
+    @test names(dfh3) == ["x1", "x2"]
+    @test dfh3 ≅ hcat(dfhu, df5, mergeduplicates=update_missing)
+    @test dfh3 ≅ DataFrames.hcat!(DataFrame(), df3, df4, df5, mergeduplicates=update_missing)
 end
 
 @testset "hcat: copying" begin
@@ -43,6 +59,13 @@ end
     @test hdf[!, 2] !== df[!, 1]
     @test hdf[!, 1] == hdf[!, 2]
     @test hdf[!, 1] !== hdf[!, 2]
+    hdf = hcat(df, df, makeunique=true)
+    @test hdf[!, 1] == df[!, 1]
+    @test hdf[!, 1] !== df[!, 1]
+    @test hdf[!, 2] == df[!, 1]
+    @test hdf[!, 2] !== df[!, 1]
+    @test hdf[!, 1] == hdf[!, 2]
+    @test hdf[!, 1] !== hdf[!, 2]
     hdf = hcat(df, df, df, makeunique=true)
     @test hdf[!, 1] == df[!, 1]
     @test hdf[!, 1] !== df[!, 1]
@@ -56,6 +79,8 @@ end
     @test hdf[!, 1] !== hdf[!, 3]
     @test hdf[!, 2] == hdf[!, 3]
     @test hdf[!, 2] !== hdf[!, 3]
+    hdf = hcat(df, df, mergeduplicates=update_missing)
+    @test hdf ≅ df
 end
 
 @testset "hcat ::AbstractDataFrame" begin
@@ -64,7 +89,6 @@ end
     answer = DataFrame(A=fill('A', 4), B=1:4, A_1='B', B_1=5:8, A_2='C', B_2=9:12)
     @test hcat(gd..., makeunique=true) == answer
     answer = answer[:, 1:4]
-    @test hcat(gd[1], gd[2], makeunique=true) == answer
 
     @test_throws MethodError hcat("a", df, makeunique=true)
     @test_throws MethodError hcat(df, "a", makeunique=true)
diff --git a/test/dataframe.jl b/test/dataframe.jl
index 971d7626d..1f4f0e69b 100644
--- a/test/dataframe.jl
+++ b/test/dataframe.jl
@@ -124,6 +124,8 @@ end
     @test DataFrame(a=[1, 2, missing], b=[4, 5, 6]) ≇ DataFrame(a=[1, 2, 3], b=[4, 5, 6])
 end
 
+update_missing = (x...) -> coalesce(reverse(x)...)
+
 @testset "copying" begin
     df = DataFrame(a=Union{Int, Missing}[2, 3],
                    b=Union{DataFrame, Missing}[DataFrame(c=1), DataFrame(d=2)])
@@ -152,6 +154,7 @@ end
 
     @test names(rename(df, [:f, :g])) == ["f", "g"]
     @test names(rename(df, [:f, :f], makeunique=true)) == ["f", "f_1"]
+    #@test names(rename(df, [:f, :f], mergeduplicates=update_missing)) == ["f", "f"]
     @test names(df) == ["a", "b"]
 
     rename!(df, [:f, :g])
@@ -258,6 +261,19 @@ end
     @test propertynames(df) == [:a_2, :a, :a_1]
     insertcols!(df, 4, :a => [11, 12], makeunique=true)
     @test propertynames(df) == [:a_2, :a, :a_1, :a_3]
+
+    df = DataFrame(a=[1, 2], a_1=[3, 4])
+    insertcols!(df, 1, :a => [11, 12], makeunique=true)
+    @test propertynames(df) == [:a_2, :a, :a_1]
+    insertcols!(df, 4, :a => [11, 12], makeunique=true)
+    @test propertynames(df) == [:a_2, :a, :a_1, :a_3]
+
+    df = DataFrame(a=[1, 2, 3], a_1=[3, 4, 5])
+    insertcols!(df, 1, :a => [11, 12, missing], mergeduplicates=update_missing)
+    @test propertynames(df) == [:a, :a_1]
+    @test df == DataFrame(a=[11, 12, 3], a_1=[3, 4, 5])
+
+    @test_throws ArgumentError insertcols!(df, 10, :a => [11, 12], makeunique=true)
     @test_throws ArgumentError insertcols!(df, 10, :a => [11, 12], makeunique=true)
 
     dfc = copy(df)
@@ -303,6 +319,11 @@ end
     @test df.a_1 === v2
     @test df.a_2 === v3
 
+    df = DataFrame()
+    @test insertcols!(df, 1, :a=>v1, :a=>v2, :a=>v3, mergeduplicates=update_missing, copycols=false) ==
+          DataFrame(a=v3)
+    @test df.a isa Vector{Int}
+
     df = DataFrame(p='a':'b', q='r':'s')
     @test insertcols!(df, 2, :a=>v1, :b=>v2, :c=>v3) ==
           DataFrame(p='a':'b', a=v1, b=v2, c=v3, q='r':'s')
@@ -319,6 +340,15 @@ end
     @test df.q_1 !== v2
     @test df.p_2 !== v3
 
+    df = DataFrame(p='a':'b', q='r':'s')
+    @test insertcols!(df, 2, :p=>v1, :q=>v2, :p=>v3, makeunique=true, copycols=true) ==
+          DataFrame(p='a':'b', p_1=v1, q_1=v2, p_2=v3, q='r':'s')
+
+    df = DataFrame(p='a':'b', q='r':'s')
+    @test_throws ArgumentError insertcols!(df, 2, :p=>v1, :q=>v2, :p=>v3)
+    @test insertcols!(df, 2, :p=>v1, :q=>v2, :p=>v3, mergeduplicates=update_missing, copycols=true) ==
+          DataFrame(p=v3, q=v2)
+
     df = DataFrame(a=1:3, b=4:6)
     @test insertcols!(copy(df), :c=>7:9) == insertcols!(copy(df), 3, :c=>7:9)
     df = DataFrame()
diff --git a/test/join.jl b/test/join.jl
index 478cca98d..f0397fbd6 100644
--- a/test/join.jl
+++ b/test/join.jl
@@ -182,12 +182,12 @@ end
     @test typeof.(eachcol(crossjoin(df1, df2, makeunique=true))) ==
         [Vector{Int}, Vector{Float64}, Vector{Int}, Vector{Float64}]
 
-    i(on) = innerjoin(df1, df2, on=on, makeunique=true)
-    l(on) = leftjoin(df1, df2, on=on, makeunique=true)
-    r(on) = rightjoin(df1, df2, on=on, makeunique=true)
-    o(on) = outerjoin(df1, df2, on=on, makeunique=true)
-    s(on) = semijoin(df1, df2, on=on, makeunique=true)
-    a(on) = antijoin(df1, df2, on=on, makeunique=true)
+    i(on,makeunique=true) = innerjoin(df1, df2, on=on, makeunique=makeunique)
+    l(on,makeunique=true) = leftjoin(df1, df2, on=on, makeunique=makeunique)
+    r(on,makeunique=true) = rightjoin(df1, df2, on=on, makeunique=makeunique)
+    o(on,makeunique=true) = outerjoin(df1, df2, on=on, makeunique=makeunique)
+    s(on,makeunique=true) = semijoin(df1, df2, on=on, makeunique=makeunique)
+    a(on,makeunique=true) = antijoin(df1, df2, on=on, makeunique=makeunique)
 
     @test s(:id) ==
           s(:fid) ==
@@ -251,6 +251,73 @@ end
     @test typeof.(eachcol(o(on))) == [Vector{Int}, Vector{Float64}]
 end
 
+update_missing = (x...) -> coalesce(reverse(x)...)
+
+@testset "update joins" begin
+    df1 = DataFrame(Any[[1, 3, 5], [1.0, 3.0, 5.0]], [:id, :fid])
+    df2 = DataFrame(Any[[0, 1, 2, 3, 4], [0.0, 1.0, 2.0, 3.0, 4.0]], [:id, :fid])
+
+    @test crossjoin(df1, df2, mergeduplicates=update_missing) ==
+        DataFrame(Any[repeat([0, 1, 2, 3, 4], outer=3),
+                      repeat([0.0, 1.0, 2.0, 3.0, 4.0], outer=3)],
+                  [:id, :fid])
+
+    i(on,mergeduplicates=update_missing) = innerjoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+    l(on,mergeduplicates=update_missing) = leftjoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+    r(on,mergeduplicates=update_missing) = rightjoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+    o(on,mergeduplicates=update_missing) = outerjoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+    s(on,mergeduplicates=update_missing) = semijoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+    a(on,mergeduplicates=update_missing) = antijoin(df1, df2, on=on, mergeduplicates=mergeduplicates)
+
+    @test s(:id) ==
+          s(:fid) ==
+          s([:id, :fid]) == DataFrame([[1, 3], [1, 3]], [:id, :fid])
+    @test typeof.(eachcol(s(:id))) ==
+          typeof.(eachcol(s(:fid))) ==
+          typeof.(eachcol(s([:id, :fid]))) == [Vector{Int}, Vector{Float64}]
+    @test a(:id) ==
+          a(:fid) ==
+          a([:id, :fid]) == DataFrame([[5], [5]], [:id, :fid])
+    @test typeof.(eachcol(a(:id))) ==
+          typeof.(eachcol(a(:fid))) ==
+          typeof.(eachcol(a([:id, :fid]))) == [Vector{Int}, Vector{Float64}]
+
+    on = :id
+    @test i(on) == DataFrame([[1, 3], [1, 3]], [:id, :fid])
+    @test typeof.(eachcol(i(on))) == [Vector{Int}, Vector{Float64}]
+    @test l(on) ≅ DataFrame(id=[1, 3, 5], fid=[1, 3, 5])
+    @test typeof.(eachcol(l(on))) == [Vector{Int}, Vector{Float64}]
+    @test r(on) ≅ DataFrame(id=[1, 3, 0, 2, 4], fid=[1, 3, 0, 2, 4])
+    @test typeof.(eachcol(r(on))) == [Vector{Int}, Vector{Float64}]
+    @test o(on) ≅ DataFrame(id=[1, 3, 5, 0, 2, 4],
+                            fid=[1, 3, 5, 0, 2, 4])
+    @test typeof.(eachcol(o(on))) == [Vector{Int}, Vector{Float64}]
+
+    on = :fid
+    df1.id = [1, missing, 5]
+    @test i(on) == DataFrame([[1, 3], [1.0, 3.0]], [:id, :fid])
+    @test typeof.(eachcol(i(on))) == [Vector{Int}, Vector{Float64}]
+    @test l(on) ≅ DataFrame(id=[1, 3, 5], fid=[1, 3, 5])
+    @test typeof.(eachcol(l(on))) == [Vector{Int}, Vector{Float64}]
+    @test r(on) ≅ DataFrame(id=[1, 3, 0, 2, 4],
+                            fid=[1, 3, 0, 2, 4])
+    @test typeof.(eachcol(r(on))) == [Vector{Int}, Vector{Float64}]
+    @test o(on) ≅ DataFrame(id=[1, 3, 5, 0, 2, 4],
+                            fid=[1, 3, 5, 0, 2, 4])
+    @test typeof.(eachcol(o(on))) == [Vector{Int}, Vector{Float64}]
+
+    on = [:id, :fid]
+    df1.id = [1, 3, 5]
+    @test i(on) == DataFrame([[1, 3], [1, 3]], [:id, :fid])
+    @test typeof.(eachcol(i(on))) == [Vector{Int}, Vector{Float64}]
+    @test l(on) == DataFrame(id=[1, 3, 5], fid=[1, 3, 5])
+    @test typeof.(eachcol(l(on))) == [Vector{Int}, Vector{Float64}]
+    @test r(on) == DataFrame(id=[1, 3, 0, 2, 4], fid=[1, 3, 0, 2, 4])
+    @test typeof.(eachcol(r(on))) == [Vector{Int}, Vector{Float64}]
+    @test o(on) == DataFrame(id=[1, 3, 5, 0, 2, 4], fid=[1, 3, 5, 0, 2, 4])
+    @test typeof.(eachcol(o(on))) == [Vector{Int}, Vector{Float64}]
+end
+
 @testset "all joins with CategoricalArrays" begin
     df1 = DataFrame(Any[CategoricalArray([1, 3, 5]),
                         CategoricalArray([1.0, 3.0, 5.0])], [:id, :fid])
diff --git a/test/reshape.jl b/test/reshape.jl
index d78e46144..6b4ca01da 100644
--- a/test/reshape.jl
+++ b/test/reshape.jl
@@ -700,6 +700,7 @@ end
     @test names(permutedims(df3, 1, makeunique=true)) == d3pd_names
     @test_throws ArgumentError permutedims(df3[!, [:a]], 1) # single column branch
     @test names(permutedims(df3[!, [:a]], 1, makeunique=true)) == d3pd_names
+    @test names(permutedims(df3, 1, mergeduplicates=sum∘tuple)) == ["a", "x"]
 
     df4 = DataFrame(a=rand(2), b=rand(2), c=[1, 2], d=[1.0, missing],
                     e=["x", "y"], f=[:x, :y], # valid src
@@ -753,6 +754,7 @@ end
     @test permutedims(df, ["p", "p"], makeunique=true) == DataFrame(p=[1, 3], p_1=[2, 4])
     @test permutedims(DataFrame()) == permutedims(DataFrame(a=[], b=[])) ==
           permutedims(DataFrame(), []) == permutedims(DataFrame(a=[], b=[]), []) == DataFrame()
+    @test permutedims(df, ["p", "p"], makeunique=false, mergeduplicates=sum∘tuple) == DataFrame(p=[3, 7])
 end
 
 @testset "stack view=true additional tests" begin