fix issue in merging when CSV parses as other type than String

src/merge.jl

@@ -9,10 +9,15 @@ function files_matching_prefix_and_suffix(prefix, suffix)
     return [joinpath(dirname_, x) for x in files]
 end
 
-function load_csv_files(files)
-    data = DataFrame()
+read_output_with_types(file) = 
+    CSV.read(file, DataFrame, types=Dict(key => String for key in joining_keys()))
+
+function load_csv_files(data, files)
     for file in files
-        data = vcat(data, CSV.read(file, DataFrame))
+        new_data = read_output_with_types(file)
+        if size(new_data, 1) > 0 
+            data = vcat(data, new_data)
+        end
     end
     return data
 end
@@ -25,15 +30,15 @@ function merge_csv_files(parsed_args)
         ".csv"
     )
     # Load tmle data
-    data = load_csv_files(tmle_files)
+    data = load_csv_files(empty_tmle_output(), tmle_files)
     # Load sieve data
     sieveprefix = parsed_args["sieve-prefix"]
     if sieveprefix !== nothing
         sieve_files = files_matching_prefix_and_suffix(
             parsed_args["sieve-prefix"],
             ".csv"
         )
-        sieve_data = load_csv_files(sieve_files)
+        sieve_data = load_csv_files(empty_sieve_output(), sieve_files)
         if size(sieve_data, 1) > 0
             data = leftjoin(data, sieve_data, on=joining_keys(), matchmissing=:equal)
         end

src/sieve_variance.jl

@@ -34,6 +34,20 @@ sieve_dataframe() = DataFrame(
     TMLE_ESTIMATE=Float64[],
 )
 
+empty_sieve_output() = DataFrame(
+    PARAMETER_TYPE=String[], 
+    TREATMENTS=String[], 
+    CASE=String[], 
+    CONTROL=Union{String, Missing}[], 
+    TARGET=String[], 
+    CONFOUNDERS=String[], 
+    COVARIATES=Union{String, Missing}[], 
+    SIEVE_STD = Float64[],
+    SIEVE_PVALUE = Float64[],
+    SIEVE_LWB = Float64[],
+    SIEVE_UPB = Float64[],
+)
+
 function push_sieveless!(output, Ψ, Ψ̂)
     target = string(Ψ.target)
     param_type = param_string(Ψ)

src/utils.jl

@@ -5,7 +5,7 @@
 #####################################################################
 
 
-csv_headers(;size=0) = DataFrame(
+empty_tmle_output(;size=0) = DataFrame(
     PARAMETER_TYPE=Vector{String}(undef, size), 
     TREATMENTS=Vector{String}(undef, size), 
     CASE=Vector{String}(undef, size), 
@@ -75,7 +75,7 @@ statistics_from_result(result::MissingTMLEResult) =
     (missing, missing, missing, missing, missing)
 
 function append_csv(filename, tmle_results, logs)
-    data = csv_headers(size=size(tmle_results, 1))
+    data = empty_tmle_output(size=size(tmle_results, 1))
     for (i, (result, log)) in enumerate(zip(tmle_results, logs))
         Ψ = result.parameter
         param_type = param_string(Ψ)

test/sieve_variance.jl

@@ -313,7 +313,7 @@ end
     @test size(io["variances"]) == (10, 8)
     close(io)
     # check csv file
-    output = CSV.read(string(outprefix, ".csv"), DataFrame)
+    output = TargetedEstimation.read_output_with_types(string(outprefix, ".csv"))
     some_expected_cols = DataFrame(
         PARAMETER_TYPE = ["IATE", "IATE", "ATE", "IATE", "IATE", "ATE", "ATE", "CM"],
         TREATMENTS = ["T1_&_T2", "T1_&_T2", "T1_&_T2", "T1_&_T2", "T1_&_T2", "T1_&_T2", "T1", "T1"],
@@ -329,6 +329,13 @@ end
     @test output.SIEVE_UPB isa Vector{Float64}
     @test output.SIEVE_STD isa Vector{Float64}
 
+    tmle_output = TargetedEstimation.load_csv_files(
+        TargetedEstimation.empty_tmle_output(),
+        ["tmle_output_1.csv", "tmle_output_2.csv"]
+    )
+
+    joined = leftjoin(tmle_output, output, on=TargetedEstimation.joining_keys(), matchmissing=:equal)
+    @test all(joined.SIEVE_PVALUE .> 0 )
     # clean
     rm(string(outprefix, ".csv"))
     rm(string(outprefix, ".hdf5"))

test/tmle.jl

@@ -174,7 +174,7 @@ end
 
     ## Check CSV file
     data = CSV.read(parsed_args["csv-out"], DataFrame)
-    @test names(TargetedEstimation.csv_headers()) == names(data)
+    @test names(TargetedEstimation.empty_tmle_output()) == names(data)
     @test size(data) == (6, 19)
     all(x === missing for x in data.LOG)
     # Clean