Running 'testthat.R' [184s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)) {
  +     library(testthat)
  +     library(dataPreparation)
  +     test_check("dataPreparation")
  + }
  dataPreparation 1.1.1
  Type data_preparation_news() to see new features/changes/bug fixes.
  [1] "aggregate_by_key: I start to aggregate"
  [1] "aggregate_by_key: 6 columns have been constructed. It took 0.01 seconds. "
  [1] "find_and_transform_dates: It took me 2.07s to identify formats"
  [1] "find_and_transform_dates: It took me 0.15s to transform 4 columns to a Date format."
  [1] "find_and_transform_dates: It took me 0.01s to identify formats"
  [1] "find_and_transform_dates: There are no dates to transform.\n                   (If i missed something please provide the date format in inputs or\n                   consider using set_col_as_date to transform it)."
  [1] "identify_dates: column date_col seems to have an ambiguity, I try to solve it."
  [1] "V2"
  [1] "fast_discretization: V2 aren't columns of types numeric i do nothing for those variables."
  [1] "fast_discretization: I will build splits for 1 numeric columns using, equal_width method."
  [1] "fast_discretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fast_discretization: I will build splits for 1 numeric columns using, equal_freq method."
  [1] "fast_discretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fast_discretization: I will build splits for 1 numeric columns using, equal_width method."
  [1] "fast_discretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fast_discretization: I will build splits for 0 numeric columns using, equal_width method."
  [1] "fast_discretization: it took me: 0s to build splits for 0 numeric columns."
  [1] "fast_discretization: I will build splits for 1 numeric columns using, equal_width method."
  [1] "equal_width_splits: constant_col can't provide 10 equal width bins; instead you will have 0 bins."
  [1] "fast_discretization: column constant_col seems to be constant, I do nothing."
  [1] "fast_discretization: it took me: 0s to build splits for 0 numeric columns."
  [1] "equal_width_splits: data_set can't provide 10 equal width bins; instead you will have 0 bins."
  [1] "equal_freq_splits: data_set can't provide 10 equal freq bins; instead you will have 2 bins."
  [1] "fast_discretization: I will build splits for 1 numeric columns using, equal_width method."
  [1] "fast_discretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fast_discretization: I will discretize 1 numeric columns using, bins."
  [1] "fast_discretization: it took me: 0s to transform 1 numeric columns into, binary columns."
  [1] "un_factor: I will identify variable that are factor but shouldn't be."
  [1] "un_factor: I un-factor false_factor."
  [1] "un_factor: It took me 0s to un-factor 1 column(s)."
  [1] "un_factor: I will identify variable that are factor but shouldn't be."
  [1] "un_factor: I un-factor true_factor."
  [1] "un_factor: I un-factor false_factor."
  [1] "un_factor: It took me 0s to un-factor 2 column(s)."
  [1] "fast_filter_variables: I check for constant columns."
  [1] "fast_filter_variables: I delete 1 constant column(s) in data_set."
  [1] "fast_filter_variables: I check for columns in double."
  [1] "fast_filter_variables: I delete 1 column(s) that are in double in data_set."
  [1] "fast_filter_variables: I check for columns that are bijections of another column."
  [1] "fast_filter_variables: I delete 3 column(s) that are bijections of another column in data_set."
  [1] "fast_filter_variables: I check for columns that are included in another column."
  [1] "fast_filter_variables: I delete 1 column(s) that are bijections of another column in data_set."
  [1] "string_column"
  [1] "fast_round: string_column aren't columns of types numeric or integer i do nothing for those variables."
  [1] "string_column"
  [1] "fast_round: string_column aren't columns of types numeric or integer i do nothing for those variables."
  [1] "generate_from_character: it took me: 0s to transform 1 character columns into, 3 new columns."
  [1] "generate_from_character: it took me: 0s to transform 3 character columns into, 9 new columns."
  [1] "generate_from_character: it took me: 0s to transform 1 character columns into, 3 new columns."
  [1] "generate_factor_from_date: I will create a factor column from each date column."
  [1] "generate_factor_from_date: It took me 0s to transform 1 column(s)."
  [1] "ID"
  [1] "generate_date_diffs: ID aren't columns of types date i do nothing for those variables."
  [1] "generate_date_diffs: I will generate difference between dates."
  [1] "generate_date_diffs: It took me 0.01s to create 3 column(s)."
   [1] "date1"         "date2"         "date3"         "date4"        
   [5] "num1"          "num2"          "constant"      "num3"         
   [9] "age"           "fnlwgt"        "education_num" "capital_gain" 
  [13] "capital_loss"  "hr_per_week"  
  [1] "generate_from_factor: c(\"date1\", \"date2\", \"date3\", \"date4\", \"num1\", \"num2\", \"constant\", \"num3\", \"age\", \"fnlwgt\", \"education_num\", \"capital_gain\", \"capital_loss\", \"hr_per_week\") aren't columns of types factor i do nothing for those variables."
  [1] "generate_from_factor: it took me: 0.02s to transform 10 factor columns into, 30 new columns."
  [1] "generate_from_factor: it took me: 0s to transform 4 factor columns into, 12 new columns."
  [1] "one_hot_encoder: Since you didn't provide encoding, I compute them with build_encoding."
  [1] "build_encoding: I will compute encoding on 1 character and factor columns."
  [1] "build_encoding: it took me: 0s to compute encoding for 1 character and factor columns."
  [1] "one_hot_encoder: I will one hot encode some columns."
  [1] "one_hot_encoder: I am doing column: character_col"
  [1] "one_hot_encoder: It took me 0s to transform 1 column(s)."
  [1] "build_encoding: I will compute encoding on 1 character and factor columns."
  [1] "build_encoding: it took me: 0s to compute encoding for 1 character and factor columns."
  [1] "build_encoding: I will compute encoding on 1 character and factor columns."
  [1] "build_encoding: it took me: 0s to compute encoding for 2 character and factor columns."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: grades."
  [1] "target_encode: Start to encode columns according to target."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: grades."
  [1] "target_encode: Start to encode columns according to target."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: target."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: target."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: target."
  [1] "real_cols: col_2 aren't columns of the table, i do nothing for those variables"
  [1] "col_2"
  [1] "real_cols: col_2 aren't columns of types numeric i do nothing for those variables."
  [1] "find_and_transform_numerics: It took me 0.01s to identify 2 numerics column(s), i will set them as numerics"
  [1] "find_and_transform_numerics: It took me 0s to transform 2 column(s) to a numeric format."
  [1] "find_and_transform_numerics: It took me 0s to identify 0 numerics column(s), i will set them as numerics"
  [1] "find_and_transform_numerics: There are no numerics to transform.(If i missed something consider using set_col_as_numeric to transform it)"
  [1] "prepare_set: step one: correcting mistakes."
  [1] "fast_filter_variables: I check for constant columns."
  [1] "fast_filter_variables: I check for columns in double."
  [1] "fast_filter_variables: I check for columns that are bijections of another column."
  [1] "fast_filter_variables: I delete 1 column(s) that are bijections of another column in data_set."
  [1] "age"          "fnlwgt"       "capital_gain" "capital_loss" "hr_per_week" 
  [1] "un_factor: c(\"age\", \"fnlwgt\", \"capital_gain\", \"capital_loss\", \"hr_per_week\") aren't columns of types factor i do nothing for those variables."
  [1] "un_factor: I will identify variable that are factor but shouldn't be."
  [1] "un_factor: I un-factor education."
  [1] "un_factor: I un-factor occupation."
  [1] "un_factor: I un-factor country."
  [1] "un_factor: It took me 0s to un-factor 3 column(s)."
  [1] "find_and_transform_numerics: It took me 0s to identify 0 numerics column(s), i will set them as numerics"
  [1] "find_and_transform_numerics: There are no numerics to transform.(If i missed something consider using set_col_as_numeric to transform it)"
  [1] "find_and_transform_dates: It took me 1.06s to identify formats"
  [1] "find_and_transform_dates: There are no dates to transform.\n                   (If i missed something please provide the date format in inputs or\n                   consider using set_col_as_date to transform it)."
  [1] "prepare_set: step two: transforming data_set."
   [1] "age"           "type_employer" "fnlwgt"        "education"    
   [5] "marital"       "occupation"    "relationship"  "race"         
   [9] "sex"           "capital_gain"  "capital_loss"  "hr_per_week"  
  [13] "country"       "income"       
  [1] "prepare_set: c(\"age\", \"type_employer\", \"fnlwgt\", \"education\", \"marital\", \"occupation\", \"relationship\", \"race\", \"sex\", \"capital_gain\", \"capital_loss\", \"hr_per_week\", \"country\", \"income\") aren't columns of types date i do nothing for those variables."
  [1] "generate_date_diffs: I will generate difference between dates."
  [1] "generate_date_diffs: It took me 0s to create 0 column(s)."
  [1] "generate_factor_from_date: I will create a factor column from each date column."
  [1] "generate_factor_from_date: It took me 0s to transform 0 column(s)."
   [1] "age"           "type_employer" "fnlwgt"        "marital"      
   [5] "relationship"  "race"          "sex"           "capital_gain" 
   [9] "capital_loss"  "hr_per_week"   "income"       
  [1] "prepare_set: c(\"age\", \"type_employer\", \"fnlwgt\", \"marital\", \"relationship\", \"race\", \"sex\", \"capital_gain\", \"capital_loss\", \"hr_per_week\", \"income\") aren't columns of types character i do nothing for those variables."
  [1] "generate_from_character: it took me: 0s to transform 2 character columns into, 6 new columns."
  [1] "build_target_encoding: Start to compute encoding for target_encoding according to col: capital_gain."
  [1] "target_encode: Start to encode columns according to target."
  [1] "aggregate_by_key: I start to aggregate"
  [1] "aggregate_by_key: 63 columns have been constructed. It took 0.16 seconds. "
  [1] "prepare_set: step three: filtering data_set."
  [1] "fast_filter_variables: I check for constant columns."
  [1] "fast_filter_variables: I delete 2 constant column(s) in result."
  [1] "fast_filter_variables: I check for columns in double."
  [1] "fast_filter_variables: I check for columns that are bijections of another column."
  [1] "fast_filter_variables: I delete 6 column(s) that are bijections of another column in result."
  [1] "country"
  [1] "fast_round: country aren't columns of types numeric or integer i do nothing for those variables."
  [1] "prepare_set: step four: handling NA."
  [1] "prepare_set: step five: shaping result."
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: I am doing the column country."
  [1] "set_col_as_factor: it took me: 0s to transform 1 column(s) to factor."
  [1] "shape_set: Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "shape_set: Previous distribution of column types:"
  col_class_init
  character   numeric 
          1        54 
  [1] "shape_set: Current distribution of column types:"
  col_class_end
   factor numeric 
       43      12 
  [1] "remove_sd_outlier: I start to filter categorical rare events"
  [1] "remove_sd_outlier: dropped 1 row(s) that are rare event on num_col."
  [1] "remove_sd_outlier: 1 have been dropped. It took 0 seconds. "
  [1] "remove_sd_outlier: I start to filter categorical rare events"
  [1] "remove_sd_outlier: dropped 0 row(s) that are rare event on num_col."
  [1] "remove_sd_outlier: 0 have been dropped. It took 0.01 seconds. "
  [1] "remove_rare_categorical: I start to filter categorical rare events"
  [1] "remove_rare_categorical: dropped 1 row(s) that are rare event on cat_col."
  [1] "remove_rare_categorical: 1 have been dropped. It took 0.02 seconds. "
  [1] "remove_percentile_outlier: I start to filter categorical rare events"
  [1] "remove_percentile_outlier: dropped 2 row(s) that are rare event on num_col."
  [1] "remove_percentile_outlier: 2 have been dropped. It took 0 seconds. "
  [1] "remove_percentile_outlier: I start to filter categorical rare events"
  [1] "remove_percentile_outlier: dropped 2 row(s) that are rare event on num_col."
  [1] "remove_percentile_outlier: 2 have been dropped. It took 0 seconds. "
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: columns col_2 are missing, I create them."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: col_2 class was logical i set it to numeric."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: the following columns are in data_set but not in reference_set: I drop them: "
  [1] "col_2"
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: col_1 class was character i set it to numeric."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: col_1 class was character i set it to c(\"POSIXct\", \"POSIXt\")."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: col_1 class had different levels than in reference_set I change it."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: col_1 class had different levels than in reference_set I change it."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: col_1 class was numeric i set it to weird_class."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: col_1 class was numeric i set it to weird_class."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: columns type_employer?, type_employerFederal-gov, type_employerLocal-gov, type_employerNever-worked, type_employerPrivate, type_employerSelf-emp-inc, type_employerSelf-emp-not-inc, type_employerState-gov, type_employerWithout-pay, education11th, education12th, education1st-4th, education5th-6th, education7th-8th, education9th, educationAssoc-acdm, educationAssoc-voc, educationBachelors, educationDoctorate, educationHS-grad, educationMasters, educationPreschool, educationProf-school, educationSome-college, maritalMarried-AF-spouse, maritalMarried-civ-spouse, maritalMarried-spouse-absent, maritalNever-married, maritalSeparated, maritalWidowed, occupationAdm-clerical, occupationArmed-Forces, occupationCraft-repair, occupationExec-managerial, occupationFarming-fishing, occupationHandlers-cleaners, occupationMachine-op-inspct, occupationOther-service, occupationPriv-house-serv, occupationProf-specialty, occupationProtective-serv, occupationSales, occupationTech-support, occupationTransport-moving, relationshipNot-in-family, relationshipOther-relative, relationshipOwn-child, relationshipUnmarried, relationshipWife, raceAsian-Pac-Islander, raceBlack, raceOther, raceWhite, sexMale, capital_loss1408, capital_loss1564, capital_loss1573, capital_loss1719, capital_loss1762, capital_loss1887, capital_loss1902, capital_loss2042, capital_loss2179, countryCambodia, countryCanada, countryChina, countryColumbia, countryCuba, countryDominican-Republic, countryEcuador, countryEl-Salvador, countryEngland, countryFrance, countryGermany, countryGreece, countryGuatemala, countryHaiti, countryHoland-Netherlands, countryHonduras, countryHong, countryHungary, countryIndia, countryIran, countryIreland, countryItaly, countryJamaica, countryJapan, countryLaos, countryMexico, countryNicaragua, countryOutlying-US(Guam-USVI-etc), countryPeru, countryPhilippines, countryPoland, countryPortugal, countryPuerto-Rico, countryScotland, countrySouth, countryTaiwan, countryThailand, countryTrinadad&Tobago, countryUnited-States, countryVietnam, countryYugoslavia, income>50K are missing, I create them."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: the following columns are in data_set but not in reference_set: I drop them: "
   [1] "type_employer" "education"     "marital"       "occupation"   
   [5] "relationship"  "race"          "sex"           "capital_loss" 
   [9] "country"       "income"       
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: age class was integer i set it to numeric."
  [1] "same_shape: fnlwgt class was integer i set it to numeric."
  [1] "same_shape: education_num class was integer i set it to numeric."
  [1] "same_shape: capital_gain class was integer i set it to numeric."
  [1] "same_shape: hr_per_week class was integer i set it to numeric."
  [1] "same_shape: type_employer? class was logical i set it to numeric."
  [1] "same_shape: type_employerFederal-gov class was logical i set it to numeric."
  [1] "same_shape: type_employerLocal-gov class was logical i set it to numeric."
  [1] "same_shape: type_employerNever-worked class was logical i set it to numeric."
  [1] "same_shape: type_employerPrivate class was logical i set it to numeric."
  [1] "same_shape: type_employerSelf-emp-inc class was logical i set it to numeric."
  [1] "same_shape: type_employerSelf-emp-not-inc class was logical i set it to numeric."
  [1] "same_shape: type_employerState-gov class was logical i set it to numeric."
  [1] "same_shape: type_employerWithout-pay class was logical i set it to numeric."
  [1] "same_shape: education11th class was logical i set it to numeric."
  [1] "same_shape: education12th class was logical i set it to numeric."
  [1] "same_shape: education1st-4th class was logical i set it to numeric."
  [1] "same_shape: education5th-6th class was logical i set it to numeric."
  [1] "same_shape: education7th-8th class was logical i set it to numeric."
  [1] "same_shape: education9th class was logical i set it to numeric."
  [1] "same_shape: educationAssoc-acdm class was logical i set it to numeric."
  [1] "same_shape: educationAssoc-voc class was logical i set it to numeric."
  [1] "same_shape: educationBachelors class was logical i set it to numeric."
  [1] "same_shape: educationDoctorate class was logical i set it to numeric."
  [1] "same_shape: educationHS-grad class was logical i set it to numeric."
  [1] "same_shape: educationMasters class was logical i set it to numeric."
  [1] "same_shape: educationPreschool class was logical i set it to numeric."
  [1] "same_shape: educationProf-school class was logical i set it to numeric."
  [1] "same_shape: educationSome-college class was logical i set it to numeric."
  [1] "same_shape: maritalMarried-AF-spouse class was logical i set it to numeric."
  [1] "same_shape: maritalMarried-civ-spouse class was logical i set it to numeric."
  [1] "same_shape: maritalMarried-spouse-absent class was logical i set it to numeric."
  [1] "same_shape: maritalNever-married class was logical i set it to numeric."
  [1] "same_shape: maritalSeparated class was logical i set it to numeric."
  [1] "same_shape: maritalWidowed class was logical i set it to numeric."
  [1] "same_shape: occupationAdm-clerical class was logical i set it to numeric."
  [1] "same_shape: occupationArmed-Forces class was logical i set it to numeric."
  [1] "same_shape: occupationCraft-repair class was logical i set it to numeric."
  [1] "same_shape: occupationExec-managerial class was logical i set it to numeric."
  [1] "same_shape: occupationFarming-fishing class was logical i set it to numeric."
  [1] "same_shape: occupationHandlers-cleaners class was logical i set it to numeric."
  [1] "same_shape: occupationMachine-op-inspct class was logical i set it to numeric."
  [1] "same_shape: occupationOther-service class was logical i set it to numeric."
  [1] "same_shape: occupationPriv-house-serv class was logical i set it to numeric."
  [1] "same_shape: occupationProf-specialty class was logical i set it to numeric."
  [1] "same_shape: occupationProtective-serv class was logical i set it to numeric."
  [1] "same_shape: occupationSales class was logical i set it to numeric."
  [1] "same_shape: occupationTech-support class was logical i set it to numeric."
  [1] "same_shape: occupationTransport-moving class was logical i set it to numeric."
  [1] "same_shape: relationshipNot-in-family class was logical i set it to numeric."
  [1] "same_shape: relationshipOther-relative class was logical i set it to numeric."
  [1] "same_shape: relationshipOwn-child class was logical i set it to numeric."
  [1] "same_shape: relationshipUnmarried class was logical i set it to numeric."
  [1] "same_shape: relationshipWife class was logical i set it to numeric."
  [1] "same_shape: raceAsian-Pac-Islander class was logical i set it to numeric."
  [1] "same_shape: raceBlack class was logical i set it to numeric."
  [1] "same_shape: raceOther class was logical i set it to numeric."
  [1] "same_shape: raceWhite class was logical i set it to numeric."
  [1] "same_shape: sexMale class was logical i set it to numeric."
  [1] "same_shape: capital_loss1408 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1564 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1573 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1719 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1762 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1887 class was logical i set it to numeric."
  [1] "same_shape: capital_loss1902 class was logical i set it to numeric."
  [1] "same_shape: capital_loss2042 class was logical i set it to numeric."
  [1] "same_shape: capital_loss2179 class was logical i set it to numeric."
  [1] "same_shape: countryCambodia class was logical i set it to numeric."
  [1] "same_shape: countryCanada class was logical i set it to numeric."
  [1] "same_shape: countryChina class was logical i set it to numeric."
  [1] "same_shape: countryColumbia class was logical i set it to numeric."
  [1] "same_shape: countryCuba class was logical i set it to numeric."
  [1] "same_shape: countryDominican-Republic class was logical i set it to numeric."
  [1] "same_shape: countryEcuador class was logical i set it to numeric."
  [1] "same_shape: countryEl-Salvador class was logical i set it to numeric."
  [1] "same_shape: countryEngland class was logical i set it to numeric."
  [1] "same_shape: countryFrance class was logical i set it to numeric."
  [1] "same_shape: countryGermany class was logical i set it to numeric."
  [1] "same_shape: countryGreece class was logical i set it to numeric."
  [1] "same_shape: countryGuatemala class was logical i set it to numeric."
  [1] "same_shape: countryHaiti class was logical i set it to numeric."
  [1] "same_shape: countryHoland-Netherlands class was logical i set it to numeric."
  [1] "same_shape: countryHonduras class was logical i set it to numeric."
  [1] "same_shape: countryHong class was logical i set it to numeric."
  [1] "same_shape: countryHungary class was logical i set it to numeric."
  [1] "same_shape: countryIndia class was logical i set it to numeric."
  [1] "same_shape: countryIran class was logical i set it to numeric."
  [1] "same_shape: countryIreland class was logical i set it to numeric."
  [1] "same_shape: countryItaly class was logical i set it to numeric."
  [1] "same_shape: countryJamaica class was logical i set it to numeric."
  [1] "same_shape: countryJapan class was logical i set it to numeric."
  [1] "same_shape: countryLaos class was logical i set it to numeric."
  [1] "same_shape: countryMexico class was logical i set it to numeric."
  [1] "same_shape: countryNicaragua class was logical i set it to numeric."
  [1] "same_shape: countryOutlying-US(Guam-USVI-etc) class was logical i set it to numeric."
  [1] "same_shape: countryPeru class was logical i set it to numeric."
  [1] "same_shape: countryPhilippines class was logical i set it to numeric."
  [1] "same_shape: countryPoland class was logical i set it to numeric."
  [1] "same_shape: countryPortugal class was logical i set it to numeric."
  [1] "same_shape: countryPuerto-Rico class was logical i set it to numeric."
  [1] "same_shape: countryScotland class was logical i set it to numeric."
  [1] "same_shape: countrySouth class was logical i set it to numeric."
  [1] "same_shape: countryTaiwan class was logical i set it to numeric."
  [1] "same_shape: countryThailand class was logical i set it to numeric."
  [1] "same_shape: countryTrinadad&Tobago class was logical i set it to numeric."
  [1] "same_shape: countryUnited-States class was logical i set it to numeric."
  [1] "same_shape: countryVietnam class was logical i set it to numeric."
  [1] "same_shape: countryYugoslavia class was logical i set it to numeric."
  [1] "same_shape: income>50K class was logical i set it to numeric."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "same_shape: verify that every column is present."
  [1] "same_shape: drop unwanted columns."
  [1] "same_shape: verify that every column is in the right type."
  [1] "same_shape: verify that every factor as the right number of levels."
  [1] "build_scales: I will compute scale on  1 numeric columns."
  [1] "build_scales: it took me: 0s to compute scale for 1 numeric columns."
  [1] "build_scales: I will compute scale on  1 numeric columns."
  [1] "build_scales: it took me: 0s to compute scale for 1 numeric columns."
  [1] "fast_scale: I will scale 1 numeric columns."
  [1] "fast_scale: it took me: 0s to scale 1 numeric columns."
  [1] "build_scales: I will compute scale on  1 numeric columns."
  [1] "build_scales: it took me: 0s to compute scale for 1 numeric columns."
  [1] "fast_scale: I will scale 1 numeric columns."
  [1] "fast_scale: it took me: 0s to scale 1 numeric columns."
  [1] "fast_scale: I will scale 1 numeric columns."
  [1] "fast_scale: it took me: 0s to unscale 1 numeric columns."
  [1] "build_scales: I will compute scale on  1 numeric columns."
  [1] "build_scales: it took me: 0s to compute scale for 1 numeric columns."
  [1] "set_col_as_numeric: I will set some columns as numeric"
  [1] "set_col_as_numeric: I am doing the column char_col_1."
  [1] "set_col_as_numeric: 0 NA have been created due to transformation to numeric."
  [1] "set_col_as_numeric: I am doing the column char_col_2."
  [1] "set_col_as_numeric: 0 NA have been created due to transformation to numeric."
  [1] "set_col_as_character: I will set some columns as character"
  [1] "set_col_as_character: I am doing the column numCol."
  [1] "set_col_as_character: I am doing the column factorCol."
  [1] "set_col_as_character: I am doing the column charcol."
  [1] "set_col_as_character: charcol is a character, i do nothing."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column date1."
  [1] "set_col_as_date:1 NA have been created due to transformation to Date."
  [1] "set_col_as_date: I am doing the column date2."
  [1] "set_col_as_date:1 NA have been created due to transformation to Date."
  [1] "set_col_as_date: it took me: 0.01s to transform 2 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column date2."
  [1] "set_col_as_date:1 NA have been created due to transformation to Date."
  [1] "set_col_as_date: it took me: 0s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column date1."
  [1] "set_col_as_date:1 NA have been created due to transformation to Date."
  [1] "set_col_as_date: it took me: 0.01s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column ID."
  [1] "set_col_as_date: it took me: 0s to transform 0 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column ID."
  [1] "set_col_as_date: Since i generated only NAs i set ID as it was before."
  [1] "set_col_as_date: it took me: 0.01s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column ID."
  [1] "set_col_as_date: ID doesn't seem to be a date, if it really is please provide format."
  [1] "set_col_as_date: it took me: 0s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column time."
  [1] "set_col_as_date: it took me: 0s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column time_stamp_s."
  [1] "set_col_as_date: it took me: 0s to transform 1 column(s) to Dates."
  [1] "set_col_as_date: I will set some columns as Date."
  [1] "set_col_as_date: I am doing the column time_stamp_ms."
  [1] "set_col_as_date: it took me: 0s to transform 1 column(s) to Dates."
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: I am doing the column col."
  [1] "set_col_as_factor: it took me: 0s to transform 1 column(s) to factor."
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: I am doing the column col."
  [1] "set_col_as_factor: it took me: 0s to transform 1 column(s) to factor."
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: I am doing the column col."
  [1] "set_col_as_factor: col has more than 2 values, i don't transform it."
  [1] "set_col_as_factor: it took me: 0s to transform 0 column(s) to factor."
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: it took me: 0s to transform 0 column(s) to factor."
  [1] "shape_set: Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "shape_set: Previous distribution of column types:"
  col_class_init
   factor integer 
        9       6 
  [1] "shape_set: Current distribution of column types:"
  col_class_end
   factor integer 
        9       6 
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: it took me: 0s to transform 0 column(s) to factor."
  [1] "shape_set: Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "shape_set: Previous distribution of column types:"
  col_class_init
   factor integer 
        9       6 
  [1] "shape_set: Current distribution of column types:"
  col_class_end
   factor integer 
        9       6 
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: it took me: 0s to transform 0 column(s) to factor."
  [1] "shape_set: Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "shape_set: Previous distribution of column types:"
  col_class_init
   factor integer 
        9       6 
  [1] "shape_set: Current distribution of column types:"
  col_class_end
   factor integer 
        9       6 
  [1] "set_col_as_factor: I will set some columns to factor."
  [1] "set_col_as_factor: it took me: 0s to transform 0 column(s) to factor."
  [1] "shape_set: Transforming logical into binaries.\n"
  [1] "shape_set: Previous distribution of column types:"
  col_class_init
  logical 
        1 
  [1] "shape_set: Current distribution of column types:"
  col_class_end
  integer 
        1 
  [1] "which_are_constant: constantCol is constant."
  [1] "which_are_constant: it took me 0s to identify 1 constant column(s)"
  [1] "which_are_in_double: it took me 0s to identify 2 column(s) to drop."
  [1] "which_are_in_double: it took me 0s to identify 1 column(s) to drop."
  [1] "which_are_in_double: it took me 0s to identify 1 column(s) to drop."
  [1] "which_are_in_double: it took me 0s to identify 0 column(s) to drop."
  [1] "which_are_bijection: it took me 0.02s to identify 1 column(s) to drop."
  [1] "which_are_bijection: education is a bijection of education_num. I put it in drop list."
  [1] "which_are_bijection: it took me 0.02s to identify 1 column(s) to drop."
  [1] "which_are_bijection: it took me 0s to identify 0 column(s) to drop."
  [1] "which_are_included: education is included in column education_num."
  [1] "which_are_included: education_num is included in column education."
  [1] "which_are_included: are_50_or_more is included in column age."
  [1] "which_are_included: constant is included in column sex."
  [1] "which_are_included: sex is included in column fnlwgt."
  [1] "which_are_included: income is included in column id."
  [1] "which_are_included: race is included in column fnlwgt."
  [1] "which_are_included: relationship is included in column id."
  [1] "which_are_included: type_employer is included in column fnlwgt."
  [1] "which_are_included: marital is included in column id."
  [1] "which_are_included: occupation is included in column id."
  [1] "which_are_included: education is included in column education_num."
  [1] "which_are_included: education_num is included in column id."
  [1] "which_are_included: capital_gain is included in column fnlwgt."
  [1] "which_are_included: capital_loss is included in column fnlwgt."
  [1] "which_are_included: country is included in column fnlwgt."
  [1] "which_are_included: hr_per_week is included in column id."
  [1] "which_are_included: age is included in column id."
  [1] "which_are_included: mail is included in column id."
  [1] "which_are_included: date2 is included in column id."
  [1] "which_are_included: date1 is included in column id."
  [1] "which_are_included: date3 is included in column date4."
  [1] "which_are_included: date4 is included in column id."
  [1] "which_are_included: num1 is included in column num3."
  [1] "which_are_included: num3 is included in column id."
  [1] "which_are_included: num2 is included in column id."
  [1] "which_are_included: fnlwgt is included in column id."
  [1] "which_are_included: constant is included in column sex."
  [1] "which_are_included: sex is included in column fnlwgt."
  [1] "which_are_included: income is included in column id."
  [1] "which_are_included: race is included in column fnlwgt."
  [1] "which_are_included: relationship is included in column id."
  [1] "which_are_included: type_employer is included in column fnlwgt."
  [1] "which_are_included: marital is included in column id."
  [1] "which_are_included: occupation is included in column id."
  [1] "which_are_included: education is included in column education_num."
  [1] "which_are_included: education_num is included in column id."
  [1] "which_are_included: capital_gain is included in column fnlwgt."
  [1] "which_are_included: capital_loss is included in column fnlwgt."
  [1] "which_are_included: country is included in column fnlwgt."
  [1] "which_are_included: hr_per_week is included in column id."
  [1] "which_are_included: age is included in column id."
  [1] "which_are_included: mail is included in column id."
  [1] "which_are_included: date2 is included in column id."
  [1] "which_are_included: date1 is included in column id."
  [1] "which_are_included: date3 is included in column date4."
  [1] "which_are_included: date4 is included in column id."
  [1] "which_are_included: num1 is included in column num3."
  [1] "which_are_included: num3 is included in column id."
  [1] "which_are_included: num2 is included in column id."
  [1] "which_are_included: fnlwgt is included in column id."
  [ FAIL 0 | WARN 0 | SKIP 0 | PASS 331 ]
  > 
  > proc.time()
     user  system elapsed 
    12.51    0.98   13.50