Running ‘testthat.R’ [20s/28s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)){
  +     library(testthat)
  +     library(dataPreparation)
  +     test_check("dataPreparation")
  + }
  Loading required package: lubridate
  
  Attaching package: 'lubridate'
  
  The following object is masked from 'package:base':
  
      date
  
  Loading required package: stringr
  Loading required package: Matrix
  Loading required package: progress
  dataPreparation 0.3.5
  Type dataPrepNews() to see new features/changes/bug fixes.
  [1] "aggregateByKey: I start to aggregate"
  Error in `[.data.table`(dataSet, , .(power.age = power(get(col)), min.age = min(get(col))),  : 
    SETLENGTH() cannot be applied to an ALTVEC object.
  ── 1. Error: (unknown) (@test_aggregate.R#18)  ─────────────────────────────────
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: aggregateByKey(copy(adult), key = "country", functions = c("power", "min"), verbose = verbose) at testthat/test_aggregate.R:18
  2: aggregateAcolumn(dataSet = dataSet[, c(key, col), with = FALSE], col = col, key = key, 
         unique_keys = unique_keys, name_separator = name_separator, functions = functions, 
         thresh = thresh)
  3: dcast.data.table(dataSet, formula = paste(key, col, sep = "~"), fun.aggregate = length, 
         value.var = col)
  4: dat[, eval(fun.call), by = c(varnames)]
  5: `[.data.table`(dat, , eval(fun.call), by = c(varnames))
  
  [1] "findAndTransformDates: It took me 1.47s to identify formats"
  [1] "findAndTransformDates: It took me 0.26s to transform 4 columns to a Date format."
  [1] "findAndTransformDates: It took me 0s to identify formats"
  [1] "findAndTransformDates: There are no dates to transform. (If i missed something please provide the date format in inputs or consider using setColAsDate to transform it)."
  [1] "identifyDates: column date1 seems to have an ambiguity, I try to solve it."
  [1] "identifyDates: column date1 seems to have an ambiguity, I try to solve it."
  ##################################
  ## Level 0: General description ##
  ##################################
  [1] "dataSet is a data.table-data.frame"
  [1] "dataSet contains 32561 rows and 24 cols."
  [1] "Columns are of the following classes:"
  
  character    factor   integer   numeric 
          6        10         6         2 
  #####################################
  ## Level 1: univariate description ##
  #####################################
  [1] "character variable date1 takes 366 different values."
  [1] "Summary for numeric variable date2"
       Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
  1.483e+09 1.491e+09 1.499e+09 1.499e+09 1.507e+09 1.515e+09      3294 
  [1] "character variable date3 takes 365 different values."
  [1] "character variable date4 takes 365 different values."
  [1] "character variable num1 takes 20151 different values."
  [1] "character variable num2 takes 20212 different values."
  [1] "constant only has 1 value."
  [1] "Summary for numeric variable constant"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
        1       1       1       1       1       1 
  [1] "Table of occurence for factor variable mail"
  
         caroline.caroline@aol.com      caroline.caroline@gmail.com 
                               200                              197 
  caroline.caroline@protonmail.com      caroline.caroline@yahoo.com 
                               203                              231 
          caroline.cynthia@aol.com       caroline.cynthia@gmail.com 
                               214                              203 
   caroline.cynthia@protonmail.com       caroline.cynthia@yahoo.com 
                               202                              195 
             caroline.jake@aol.com          caroline.jake@gmail.com 
                               199                              188 
      caroline.jake@protonmail.com          caroline.jake@yahoo.com 
                               189                              197 
            caroline.lucas@aol.com         caroline.lucas@gmail.com 
                               203                              188 
     caroline.lucas@protonmail.com         caroline.lucas@yahoo.com 
                               181                              247 
            caroline.marie@aol.com         caroline.marie@gmail.com 
                               201                              202 
     caroline.marie@protonmail.com         caroline.marie@yahoo.com 
                               201                              205 
           caroline.pierre@aol.com        caroline.pierre@gmail.com 
                               234                              202 
    caroline.pierre@protonmail.com        caroline.pierre@yahoo.com 
                               183                              196 
          cynthia.caroline@aol.com       cynthia.caroline@gmail.com 
                               194                              224 
   cynthia.caroline@protonmail.com       cynthia.caroline@yahoo.com 
                               199                              232 
           cynthia.cynthia@aol.com        cynthia.cynthia@gmail.com 
                               196                              201 
    cynthia.cynthia@protonmail.com        cynthia.cynthia@yahoo.com 
                               179                              235 
              cynthia.jake@aol.com           cynthia.jake@gmail.com 
                               194                              180 
       cynthia.jake@protonmail.com           cynthia.jake@yahoo.com 
                               213                              213 
             cynthia.lucas@aol.com          cynthia.lucas@gmail.com 
                               199                              230 
      cynthia.lucas@protonmail.com          cynthia.lucas@yahoo.com 
                               210                              206 
             cynthia.marie@aol.com          cynthia.marie@gmail.com 
                               205                              203 
      cynthia.marie@protonmail.com          cynthia.marie@yahoo.com 
                               195                              228 
            cynthia.pierre@aol.com         cynthia.pierre@gmail.com 
                               203                              209 
     cynthia.pierre@protonmail.com         cynthia.pierre@yahoo.com 
                               215                              189 
             jake.caroline@aol.com          jake.caroline@gmail.com 
                               210                              210 
      jake.caroline@protonmail.com          jake.caroline@yahoo.com 
                               189                              189 
              jake.cynthia@aol.com           jake.cynthia@gmail.com 
                               195                              209 
       jake.cynthia@protonmail.com           jake.cynthia@yahoo.com 
                               191                              209 
                 jake.jake@aol.com              jake.jake@gmail.com 
                               192                              191 
          jake.jake@protonmail.com              jake.jake@yahoo.com 
                               189                              212 
                jake.lucas@aol.com             jake.lucas@gmail.com 
                               212                              212 
         jake.lucas@protonmail.com             jake.lucas@yahoo.com 
                               217                              184 
                jake.marie@aol.com             jake.marie@gmail.com 
                               225                              207 
         jake.marie@protonmail.com             jake.marie@yahoo.com 
                               203                              223 
               jake.pierre@aol.com            jake.pierre@gmail.com 
                               193                              183 
        jake.pierre@protonmail.com            jake.pierre@yahoo.com 
                               212                              210 
            lucas.caroline@aol.com         lucas.caroline@gmail.com 
                               187                              191 
     lucas.caroline@protonmail.com         lucas.caroline@yahoo.com 
                               194                              226 
             lucas.cynthia@aol.com          lucas.cynthia@gmail.com 
                               182                              220 
      lucas.cynthia@protonmail.com          lucas.cynthia@yahoo.com 
                               187                              231 
                lucas.jake@aol.com             lucas.jake@gmail.com 
                               240                              199 
         lucas.jake@protonmail.com             lucas.jake@yahoo.com 
                               240                              196 
               lucas.lucas@aol.com            lucas.lucas@gmail.com 
                               168                              205 
        lucas.lucas@protonmail.com            lucas.lucas@yahoo.com 
                               174                              205 
               lucas.marie@aol.com            lucas.marie@gmail.com 
                               198                              220 
        lucas.marie@protonmail.com            lucas.marie@yahoo.com 
                               196                              219 
              lucas.pierre@aol.com           lucas.pierre@gmail.com 
                               201                              201 
       lucas.pierre@protonmail.com           lucas.pierre@yahoo.com 
                               193                              201 
            marie.caroline@aol.com         marie.caroline@gmail.com 
                               216                              202 
     marie.caroline@protonmail.com         marie.caroline@yahoo.com 
                               195                              217 
             marie.cynthia@aol.com          marie.cynthia@gmail.com 
                               180                              208 
      marie.cynthia@protonmail.com          marie.cynthia@yahoo.com 
                               205                              206 
                marie.jake@aol.com             marie.jake@gmail.com 
                               229                              207 
         marie.jake@protonmail.com             marie.jake@yahoo.com 
                               182                              205 
               marie.lucas@aol.com            marie.lucas@gmail.com 
                               205                              203 
        marie.lucas@protonmail.com            marie.lucas@yahoo.com 
                               183                              188 
               marie.marie@aol.com            marie.marie@gmail.com 
                               191                              211 
        marie.marie@protonmail.com            marie.marie@yahoo.com 
                               205                              217 
              marie.pierre@aol.com           marie.pierre@gmail.com 
                               194                              198 
       marie.pierre@protonmail.com           marie.pierre@yahoo.com 
                               204                              206 
           pierre.caroline@aol.com        pierre.caroline@gmail.com 
                               200                              187 
    pierre.caroline@protonmail.com        pierre.caroline@yahoo.com 
                               188                              204 
            pierre.cynthia@aol.com         pierre.cynthia@gmail.com 
                               229                              197 
     pierre.cynthia@protonmail.com         pierre.cynthia@yahoo.com 
                               182                              202 
               pierre.jake@aol.com            pierre.jake@gmail.com 
                               200                              182 
        pierre.jake@protonmail.com            pierre.jake@yahoo.com 
                               213                              207 
              pierre.lucas@aol.com           pierre.lucas@gmail.com 
                               203                              217 
       pierre.lucas@protonmail.com           pierre.lucas@yahoo.com 
                               196                              217 
              pierre.marie@aol.com           pierre.marie@gmail.com 
                               209                              185 
       pierre.marie@protonmail.com           pierre.marie@yahoo.com 
                               201                              210 
             pierre.pierre@aol.com          pierre.pierre@gmail.com 
                               210                              221 
      pierre.pierre@protonmail.com          pierre.pierre@yahoo.com 
                               184                              200 
  [1] "character variable num3 takes 20151 different values."
  [1] "Summary for numeric variable age"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    17.00   28.00   37.00   38.58   48.00   90.00 
  [1] "Table of occurence for factor variable type_employer"
  
                 ?      Federal-gov        Local-gov     Never-worked 
              1836              960             2093                7 
           Private     Self-emp-inc Self-emp-not-inc        State-gov 
             22696             1116             2541             1298 
       Without-pay 
                14 
  [1] "Summary for numeric variable fnlwgt"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    12285  117827  178356  189778  237051 1484705 
  [1] "Table of occurence for factor variable education"
  
          10th         11th         12th      1st-4th      5th-6th      7th-8th 
           933         1175          433          168          333          646 
           9th   Assoc-acdm    Assoc-voc    Bachelors    Doctorate      HS-grad 
           514         1067         1382         5355          413        10501 
       Masters    Preschool  Prof-school Some-college 
          1723           51          576         7291 
  [1] "Summary for numeric variable education_num"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     1.00    9.00   10.00   10.08   12.00   16.00 
  [1] "Table of occurence for factor variable marital"
  
               Divorced     Married-AF-spouse    Married-civ-spouse 
                   4443                    23                 14976 
  Married-spouse-absent         Never-married             Separated 
                    418                 10683                  1025 
                Widowed 
                    993 
  [1] "Table of occurence for factor variable occupation"
  
                  ?      Adm-clerical      Armed-Forces      Craft-repair 
               1843              3770                 9              4099 
    Exec-managerial   Farming-fishing Handlers-cleaners Machine-op-inspct 
               4066               994              1370              2002 
      Other-service   Priv-house-serv    Prof-specialty   Protective-serv 
               3295               149              4140               649 
              Sales      Tech-support  Transport-moving 
               3650               928              1597 
  [1] "Table of occurence for factor variable relationship"
  
         Husband  Not-in-family Other-relative      Own-child      Unmarried 
           13193           8305            981           5068           3446 
            Wife 
            1568 
  [1] "Table of occurence for factor variable race"
  
  Amer-Indian-Eskimo Asian-Pac-Islander              Black              Other 
                 311               1039               3124                271 
               White 
               27816 
  [1] "Table of occurence for factor variable sex"
  
  Female   Male 
   10771  21790 
  [1] "Summary for numeric variable capital_gain"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
        0       0       0    1078       0   99999 
  [1] "Summary for numeric variable capital_loss"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0.0     0.0     0.0    87.3     0.0  4356.0 
  [1] "Summary for numeric variable hr_per_week"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     1.00   40.00   40.00   40.44   45.00   99.00 
  [1] "Table of occurence for factor variable country"
  
                           ?                   Cambodia 
                         583                         19 
                      Canada                      China 
                         121                         75 
                    Columbia                       Cuba 
                          59                         95 
          Dominican-Republic                    Ecuador 
                          70                         28 
                 El-Salvador                    England 
                         106                         90 
                      France                    Germany 
                          29                        137 
                      Greece                  Guatemala 
                          29                         64 
                       Haiti         Holand-Netherlands 
                          44                          1 
                    Honduras                       Hong 
                          13                         20 
                     Hungary                      India 
                          13                        100 
                        Iran                    Ireland 
                          43                         24 
                       Italy                    Jamaica 
                          73                         81 
                       Japan                       Laos 
                          62                         18 
                      Mexico                  Nicaragua 
                         643                         34 
  Outlying-US(Guam-USVI-etc)                       Peru 
                          14                         31 
                 Philippines                     Poland 
                         198                         60 
                    Portugal                Puerto-Rico 
                          37                        114 
                    Scotland                      South 
                          12                         80 
                      Taiwan                   Thailand 
                          51                         18 
             Trinadad&Tobago              United-States 
                          19                      29170 
                     Vietnam                 Yugoslavia 
                          67                         16 
  [1] "Table of occurence for factor variable income"
  
  <=50K  >50K 
  24720  7841 
  ##################################
  ## Level 0: General description ##
  ##################################
  [1] "dataSet is a data.table-data.frame"
  [1] "dataSet contains 2 rows and 3 cols."
  [1] "Columns are of the following classes:"
  
     Date integer logical 
        1       1       1 
  #####################################
  ## Level 1: univariate description ##
  #####################################
  [1] "col1 has all unique values."
  [1] "Summary for numeric variable col1"
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     1.00    1.25    1.50    1.50    1.75    2.00 
  [1] "col2 has all unique values."
  [1] "Table of occurence for logical variable col2"
  
  FALSE  TRUE 
      1     1 
  [1] "col3 only has 1 value."
  [1] "Summary for date variable col3"
          Min.      1st Qu.       Median         Mean      3rd Qu.         Max. 
  "2018-05-07" "2018-05-07" "2018-05-07" "2018-05-07" "2018-05-07" "2018-05-07" 
  [1] "fastDiscretization: I will build splits for 1 numeric columns using, equal_width method."
  [1] "fastDiscretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fastDiscretization: I will build splits for 1 numeric columns using, equal_freq method."
  [1] "fastDiscretization: it took me: 0s to build splits for 1 numeric columns."
  [1] "fastDiscretization: I will build splits for 8 numeric columns using, equal_freq method."
  [1] "equal_freq_splits: constant can't provide 10 equal freq bins; instead you will have 2 bins."
  [1] "equal_freq_splits: education_num can't provide 10 equal freq bins; instead you will have 6 bins."
  [1] "equal_freq_splits: capital_gain can't provide 10 equal freq bins; instead you will have 2 bins."
  [1] "equal_freq_splits: capital_loss can't provide 10 equal freq bins; instead you will have 2 bins."
  [1] "equal_freq_splits: hr_per_week can't provide 10 equal freq bins; instead you will have 6 bins."
  [1] "fastDiscretization: it took me: 0.01s to build splits for 8 numeric columns."
  [1] "fastDiscretization: I will build splits for 8 numeric columns using, equal_width method."
  [1] "equal_width_splits: constant can't provide 10 equal width bins; instead you will have 0 bins."
  [1] "fastDiscretization: column constant seems to be constant, I do nothing."
  [1] "fastDiscretization: it took me: 0s to build splits for 7 numeric columns."
  [1] "education"
  [1] "fastDiscretization: education aren't columns of types numeric i do nothing for those variables."
  [1] "fastDiscretization: I will build splits for 0 numeric columns using, equal_width method."
  [1] "fastDiscretization: it took me: 0s to build splits for 0 numeric columns."
  [1] "fastDiscretization: I will build splits for 6 numeric columns using, equal_width method."
  [1] "fastDiscretization: it took me: 0s to build splits for 6 numeric columns."
  [1] "fastDiscretization: I will discretize 6 numeric columns using, bins."
  [1] "fastDiscretization: it took me: 1.97s to transform 6 numeric columns into, binarised columns."
  [1] "equal_width_splits: dataSet can't provide 10 equal width bins; instead you will have 0 bins."
  [1] "equal_freq_splits: dataSet can't provide 10 equal freq bins; instead you will have 2 bins."
  [1] "unFactor: I will identify variable that are factor but shouldn't be."
  [1] "unFactor: I unfactor false_factor."
  [1] "unFactor: It took me 0.22s to unfactor 1 column(s)."
  [1] "unFactor: I will identify variable that are factor but shouldn't be."
  [1] "unFactor: I unfactor true_factor."
  [1] "unFactor: It took me 0.23s to unfactor 1 column(s)."
  [1] "fastFilterVariables: I check for constant columns."
  [1] "fastFilterVariables: I delete 1 constant column(s) in dataSet."
  [1] "fastFilterVariables: I check for columns in double."
  [1] "fastFilterVariables: I delete 1 column(s) that are in double in dataSet."
  [1] "fastFilterVariables: I check for columns that are bijections of another column."
  [1] "fastFilterVariables: I delete 3 column(s) that are bijections of another column in dataSet."
  [1] "fastFilterVariables: I check for columns that are included in another column."
  [1] "fastFilterVariables: I delete 1 column(s) that are bijections of another column in dataSet."
  ── 2. Error: (unknown) (@test_generateFromCharacter.R#9)  ──────────────────────
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: generateFromCharacter(messy_adult, cols = "mail") at testthat/test_generateFromCharacter.R:9
  2: dataSet[, `:=`(c(new_col), .N), by = col]
  3: `[.data.table`(dataSet, , `:=`(c(new_col), .N), by = col)
  
  [1] "generateFactorFromDate: I will create a factor column from each date column."
  [1] "generateFactorFromDate: It took me 0.28s to transform 2 column(s)."
  [1] "generateFactorFromDate: I will create a factor column from each date column."
  [1] "generateFactorFromDate: It took me 0.34s to transform 4 column(s)."
  [1] "generateDateDiffs: I will generate difference between dates."
  [1] "generateDateDiffs: It took me 0s to create 3 column(s)."
  ── 3. Error: (unknown) (@test_generateFromFactor.R#8)  ─────────────────────────
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: generateFromFactor(messy_adult, cols = "type_employer") at testthat/test_generateFromFactor.R:8
  2: dataSet[, `:=`(c(new_col), .N), by = col]
  3: `[.data.table`(dataSet, , `:=`(c(new_col), .N), by = col)
  
  [1] "asucgzr"
  [1] "real_cols: asucgzr aren't columns of the table, i do nothing for those variables"
  [1] "education"
  [1] "real_cols: education aren't columns of types numeric or integer i do nothing for those variables."
  [1] "education"
  [1] "real_cols: education aren't columns of types numeric i do nothing for those variables."
  [1] "findAndTransformNumerics: It took me 0.19s to identify 2 numerics column(s), i will set them as numerics"
  [1] "setColAsNumeric: I will set some columns as numeric"
  [1] "setColAsNumeric: I am doing the column col1."
  [1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
  [1] "setColAsNumeric: I will set some columns as numeric"
  [1] "setColAsNumeric: I am doing the column col2."
  [1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
  [1] "findAndTransformNumerics: It took me 0s to transform 2 column(s) to a numeric format."
  [1] "findAndTransformNumerics: It took me 0.31s to identify 0 numerics column(s), i will set them as numerics"
  [1] "findAndTransformNumerics: There are no numerics to transform.(If i missed something consider using setColAsNumeric to transform it)"
  V 0.3.5
  =======
  - New features:
  	- New features in existing functions:
  		- *findAndTransFormDates* now as an *ambiguities* parameter, IGNORE to work as before, WARN to check for ambiguities and print them, SOLVE to try to solve ambiguities on more lines.
  		- *one_hot_encoder* now uses a *build_encoding* functions to be able to build same encoding on train and on test.
  		- *aggregateByKey* is now way faster on numerics. But it changed the way it gets input functions.
  		- *fastScale* now as a *way* parameter which allow you to either scale or unscale. Unscaling numeric values can be very usefull for most post-model analysis.
  		- *setColAsDate* now accept multiple formats in a single call.
  	- New functions:
  		- *build_encoding* build a list of encoding to be used by *one_hot_encoder*, it also has a parameter *min_frequency* to control that rare values doesn't result in new columns.
  		- Previously private function *identifyDates* is now exported. To be able to perform same transformation on train and on test.
  		- Adding *dataPrepNews* function to open NEWS file (inspired from rfNews() of randomForest package)
  		
  - Bug fixes:
  	- *findAndTransFormDates*: bug fixed: user formats weren't used.
  	- *identifyDates*: some formats where tested but would never work. They have been removed.
  	
  - Refactoring: 
  	- Unit test partly reviewed to be more readable and more efficient. Unit test time as been divided by 3.
  	- Improving input control for more robust functions
  	
  WARNING:
  - *one_hot_encoder* now requires you to run *build_encoding* first.
  - *aggregateByKey* now require functions to be passed by character name
  		
  This version is making (as much as possible) transformation reproducible on train and test set. This is to prepare future pipeline feature.
  
  V 0.3.4
  ========
  - Improvement of function 
  	- *whichAreBijection*: It is 2 to 15 time faster than previous version.
  	- *whichAreIncluded*: It is a bit faster.
  - Bug fixes:
  	- *generateFactorFromDate*: default value was missing. Fixed.
  - New features:
  	- New features in existing functions:
  		- *fastFilterVariables* has a new parameter (level) to choose which types of filtering to perform
  		
  WARNING:
  - *whichAreIncluded*: in case of bijection (col1 is a bijection of col2), they are both included in the other, but the choice of the one to drop might have changed in this version.
  
  V 0.3.3
  ========
  - New features:
  	- New features in existing functions:
  		- *findAndTransFormDates* now recognize date character even if there are multiple separator in date (ex: "2016, Jan-26").
  		- *findAndTransFormDates* now recognize date character even if there are leading and tailing white spaces.
  		
  WARNING:
  - *date3* column in *messy_adult* data set has changed in order to illustrate the recognition of date character even if there are leading and/or trailing white spaces.
  - *date4* column in *messy_adult* data set has changed in order to illustrate the recognition of date character even if there are multiple separator.
  
  V 0.3.2
  ========
  - Change URLs to meet CRAN requirement
  
  v 0.3.1
  =======
  - Fix bug in Latex documentation
  
  v 0.3
  =====
  - New features:
  	- New features in existing functions:
  		- *findAndTransFormDates* now recognize date character even if "0" are not present in month or day part and month as lower strings.
  		- *findAndTransFormDates* and *setColAsDate* now work with *factors*.
  	- New functions:
  		- *fastDiscretization*: to perform equal freq or equal width discretization on a data set using *data.table* power.
  		- *fastScale*: to perform scaling on a data set using *data.table* power.
  		- *one_hot_encoder*: to perform one_hot encoding on a data set using *data.table* power.
  	- New documentation:
  		- A new vignette to illustrate how to build a correct *train* and *test* set unising data preparation
  - Minor changes in log (in particular regarding progress bars and typos)
  	- Due to dependencies issues with *tcltk*, we stop using it and start using *progress*
  - Refactoring: 
  	- Private function *real_cols* take more importance to control that columns have the correct types and handling "auto" value.
  	- Making code faster: some functions are up to **30% faster**
  	- Review unit testing to be faster
  	- Unit test evolution to be more readable
  
  WARNING:
  - *date1* column in *messy_adult* data set has changed in order to illustrate the recognition of date character even if "0" are not present in month or day part.
  
  
  v 0.2
  =====
  - Improving unit testing and code coverage
  - Improving documentation
  - Solving minor bug in date conversion and in which functions
  - New features: 
  	- New functions:
  		- *unFactor* to unfactor columns, when reading wasn't performed in expected way.
  		- *sameShape* to make ure that train and test set have exactly the same shape.
  		- generate new columns from existing columns (generate functions)
  			- generate factor from dates: *generateFactorFromDate*
  			- diffDates becomes *generateDateDiffs* (for better name understanding).
  			- generate numerics and booleans from character of fators (using *generateFromFactor* and *generateFromCharacter*)
  			
  		- *setColAsFactor* a function to make multiple columns as factor and controling number of unique elements
  		
  	- New features in existing functions:	
  		- which functions: add *keep_cols* argument to make sure that they are not dropped
  		- fastFilterVariables: *verbose* can be T/F or 0, 1, 2 in order to control level of verbosity
  		- *findAndTransFormDates* and *setColAsDates* now recognize and accept timestamp.
  
  WARNING:
  - If you were using *diffDates*, it is now called *generateDateDiffs*
  - *date2* column in *messy_adult* data set have changed in order to illustrate new timestamp features
  - *setColAsFactorOrLogical* doesn't exist anymore: it as been splitted between *setColAsFactor* and *generateFromCat*
  - Considering all those changes: *shapeSet* and *prepareSet* don't give the same result anymore.
  
  
  v 0.1: release on CRAN July 2017
  ================================
  [1] "prepareSet: step one: correcting mistakes."
  [1] "fastFilterVariables: I check for constant columns."
  [1] "fastFilterVariables: I check for columns in double."
  [1] "fastFilterVariables: I check for columns that are bijections of another column."
  [1] "fastFilterVariables: I delete 1 column(s) that are bijections of another column in dataSet."
  [1] "unFactor: I will identify variable that are factor but shouldn't be."
  [1] "unFactor: I unfactor education."
  [1] "unFactor: I unfactor occupation."
  [1] "unFactor: I unfactor country."
  [1] "unFactor: It took me 0.18s to unfactor 3 column(s)."
  [1] "findAndTransformNumerics: It took me 0.2s to identify 0 numerics column(s), i will set them as numerics"
  [1] "findAndTransformNumerics: There are no numerics to transform.(If i missed something consider using setColAsNumeric to transform it)"
  [1] "findAndTransformDates: It took me 0.93s to identify formats"
  [1] "findAndTransformDates: There are no dates to transform. (If i missed something please provide the date format in inputs or consider using setColAsDate to transform it)."
  [1] "prepareSet: step two: transforming dataSet."
  [1] "generateDateDiffs: I will generate difference between dates."
  [1] "generateDateDiffs: It took me 0s to create 0 column(s)."
  [1] "generateFactorFromDate: I will create a factor column from each date column."
  [1] "generateFactorFromDate: It took me 0.31s to transform 0 column(s)."
  ── 4. Error: (unknown) (@test_prepareSet.R#7)  ─────────────────────────────────
  SETLENGTH() cannot be applied to an ALTVEC object.
  1: prepareSet(adult, key = "country", digits = 2, n_unfactor = 10, verbose = verbose, 
         functions = c("mean", "max")) at testthat/test_prepareSet.R:7
  2: generateFromCharacter(result, cols = character_cols, drop = TRUE, name_separator = args[["name_separator"]], 
         verbose = verbose)
  3: dataSet[, `:=`(c(new_col), .N), by = col]
  4: `[.data.table`(dataSet, , `:=`(c(new_col), .N), by = col)
  
  [1] "sameShape: verify that every column is present."
  [1] "sameShape: columns date1, date2, date3, date4, num1, num2, constant, mail, num3 are missing, I create them."
  [1] "sameShape: drop unwanted columns."
  [1] "sameShape: the folowing columns are in dataSet but not in referenceSet: I drop them: "
  [1] "age"
  [1] "sameShape: verify that every column is in the right type."
  [1] "sameShape: education class was factor i set it to weirdClass2."
  [1] "sameShape: date1 class was logical i set it to c(\"POSIXct\", \"POSIXt\")."
  [1] "sameShape: date2 class was logical i set it to c(\"POSIXct\", \"POSIXt\")."
  [1] "sameShape: date3 class was logical i set it to c(\"POSIXct\", \"POSIXt\")."
  [1] "sameShape: date4 class was logical i set it to c(\"POSIXct\", \"POSIXt\")."
  [1] "sameShape: num1 class was logical i set it to numeric."
  [1] "sameShape: num2 class was logical i set it to numeric."
  [1] "sameShape: constant class was logical i set it to numeric."
  [1] "sameShape: mail class was logical i set it to factor."
  [1] "sameShape: num3 class was logical i set it to numeric."
  [1] "sameShape: verify that every factor as the right number of levels."
  [1] "sameShape: mail class had different levels than in referenceSet I change it."
  [1] "setColAsFactor: age has more than 10 values, i don't transform it."
  [1] "setColAsFactor: fnlwgt has more than 10 values, i don't transform it."
  [1] "setColAsFactor: education_num has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_gain has more than 10 values, i don't transform it."
  [1] "setColAsFactor: hr_per_week has more than 10 values, i don't transform it."
  [1] "sameShape: verify that every column is present."
  [1] "sameShape: drop unwanted columns."
  [1] "sameShape: verify that every column is in the right type."
  [1] "sameShape: capital_loss class was factor i set it to integer."
  [1] "sameShape: verify that every factor as the right number of levels."
  [1] "sameShape: verify that every column is present."
  [1] "sameShape: 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] "sameShape: drop unwanted columns."
  [1] "sameShape: the folowing columns are in dataSet but not in referenceSet: I drop them: "
   [1] "type_employer" "education"     "marital"       "occupation"   
   [5] "relationship"  "race"          "sex"           "capital_loss" 
   [9] "country"       "income"       
  [1] "sameShape: verify that every column is in the right type."
  [1] "sameShape: age class was integer i set it to numeric."
  [1] "sameShape: fnlwgt class was integer i set it to numeric."
  [1] "sameShape: education_num class was integer i set it to numeric."
  [1] "sameShape: capital_gain class was integer i set it to numeric."
  [1] "sameShape: hr_per_week class was integer i set it to numeric."
  [1] "sameShape: type_employer? class was logical i set it to numeric."
  [1] "sameShape: type_employerFederal-gov class was logical i set it to numeric."
  [1] "sameShape: type_employerLocal-gov class was logical i set it to numeric."
  [1] "sameShape: type_employerNever-worked class was logical i set it to numeric."
  [1] "sameShape: type_employerPrivate class was logical i set it to numeric."
  [1] "sameShape: type_employerSelf-emp-inc class was logical i set it to numeric."
  [1] "sameShape: type_employerSelf-emp-not-inc class was logical i set it to numeric."
  [1] "sameShape: type_employerState-gov class was logical i set it to numeric."
  [1] "sameShape: type_employerWithout-pay class was logical i set it to numeric."
  [1] "sameShape: education11th class was logical i set it to numeric."
  [1] "sameShape: education12th class was logical i set it to numeric."
  [1] "sameShape: education1st-4th class was logical i set it to numeric."
  [1] "sameShape: education5th-6th class was logical i set it to numeric."
  [1] "sameShape: education7th-8th class was logical i set it to numeric."
  [1] "sameShape: education9th class was logical i set it to numeric."
  [1] "sameShape: educationAssoc-acdm class was logical i set it to numeric."
  [1] "sameShape: educationAssoc-voc class was logical i set it to numeric."
  [1] "sameShape: educationBachelors class was logical i set it to numeric."
  [1] "sameShape: educationDoctorate class was logical i set it to numeric."
  [1] "sameShape: educationHS-grad class was logical i set it to numeric."
  [1] "sameShape: educationMasters class was logical i set it to numeric."
  [1] "sameShape: educationPreschool class was logical i set it to numeric."
  [1] "sameShape: educationProf-school class was logical i set it to numeric."
  [1] "sameShape: educationSome-college class was logical i set it to numeric."
  [1] "sameShape: maritalMarried-AF-spouse class was logical i set it to numeric."
  [1] "sameShape: maritalMarried-civ-spouse class was logical i set it to numeric."
  [1] "sameShape: maritalMarried-spouse-absent class was logical i set it to numeric."
  [1] "sameShape: maritalNever-married class was logical i set it to numeric."
  [1] "sameShape: maritalSeparated class was logical i set it to numeric."
  [1] "sameShape: maritalWidowed class was logical i set it to numeric."
  [1] "sameShape: occupationAdm-clerical class was logical i set it to numeric."
  [1] "sameShape: occupationArmed-Forces class was logical i set it to numeric."
  [1] "sameShape: occupationCraft-repair class was logical i set it to numeric."
  [1] "sameShape: occupationExec-managerial class was logical i set it to numeric."
  [1] "sameShape: occupationFarming-fishing class was logical i set it to numeric."
  [1] "sameShape: occupationHandlers-cleaners class was logical i set it to numeric."
  [1] "sameShape: occupationMachine-op-inspct class was logical i set it to numeric."
  [1] "sameShape: occupationOther-service class was logical i set it to numeric."
  [1] "sameShape: occupationPriv-house-serv class was logical i set it to numeric."
  [1] "sameShape: occupationProf-specialty class was logical i set it to numeric."
  [1] "sameShape: occupationProtective-serv class was logical i set it to numeric."
  [1] "sameShape: occupationSales class was logical i set it to numeric."
  [1] "sameShape: occupationTech-support class was logical i set it to numeric."
  [1] "sameShape: occupationTransport-moving class was logical i set it to numeric."
  [1] "sameShape: relationshipNot-in-family class was logical i set it to numeric."
  [1] "sameShape: relationshipOther-relative class was logical i set it to numeric."
  [1] "sameShape: relationshipOwn-child class was logical i set it to numeric."
  [1] "sameShape: relationshipUnmarried class was logical i set it to numeric."
  [1] "sameShape: relationshipWife class was logical i set it to numeric."
  [1] "sameShape: raceAsian-Pac-Islander class was logical i set it to numeric."
  [1] "sameShape: raceBlack class was logical i set it to numeric."
  [1] "sameShape: raceOther class was logical i set it to numeric."
  [1] "sameShape: raceWhite class was logical i set it to numeric."
  [1] "sameShape: sexMale class was logical i set it to numeric."
  [1] "sameShape: capital_loss1408 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1564 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1573 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1719 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1762 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1887 class was logical i set it to numeric."
  [1] "sameShape: capital_loss1902 class was logical i set it to numeric."
  [1] "sameShape: capital_loss2042 class was logical i set it to numeric."
  [1] "sameShape: capital_loss2179 class was logical i set it to numeric."
  [1] "sameShape: countryCambodia class was logical i set it to numeric."
  [1] "sameShape: countryCanada class was logical i set it to numeric."
  [1] "sameShape: countryChina class was logical i set it to numeric."
  [1] "sameShape: countryColumbia class was logical i set it to numeric."
  [1] "sameShape: countryCuba class was logical i set it to numeric."
  [1] "sameShape: countryDominican-Republic class was logical i set it to numeric."
  [1] "sameShape: countryEcuador class was logical i set it to numeric."
  [1] "sameShape: countryEl-Salvador class was logical i set it to numeric."
  [1] "sameShape: countryEngland class was logical i set it to numeric."
  [1] "sameShape: countryFrance class was logical i set it to numeric."
  [1] "sameShape: countryGermany class was logical i set it to numeric."
  [1] "sameShape: countryGreece class was logical i set it to numeric."
  [1] "sameShape: countryGuatemala class was logical i set it to numeric."
  [1] "sameShape: countryHaiti class was logical i set it to numeric."
  [1] "sameShape: countryHoland-Netherlands class was logical i set it to numeric."
  [1] "sameShape: countryHonduras class was logical i set it to numeric."
  [1] "sameShape: countryHong class was logical i set it to numeric."
  [1] "sameShape: countryHungary class was logical i set it to numeric."
  [1] "sameShape: countryIndia class was logical i set it to numeric."
  [1] "sameShape: countryIran class was logical i set it to numeric."
  [1] "sameShape: countryIreland class was logical i set it to numeric."
  [1] "sameShape: countryItaly class was logical i set it to numeric."
  [1] "sameShape: countryJamaica class was logical i set it to numeric."
  [1] "sameShape: countryJapan class was logical i set it to numeric."
  [1] "sameShape: countryLaos class was logical i set it to numeric."
  [1] "sameShape: countryMexico class was logical i set it to numeric."
  [1] "sameShape: countryNicaragua class was logical i set it to numeric."
  [1] "sameShape: countryOutlying-US(Guam-USVI-etc) class was logical i set it to numeric."
  [1] "sameShape: countryPeru class was logical i set it to numeric."
  [1] "sameShape: countryPhilippines class was logical i set it to numeric."
  [1] "sameShape: countryPoland class was logical i set it to numeric."
  [1] "sameShape: countryPortugal class was logical i set it to numeric."
  [1] "sameShape: countryPuerto-Rico class was logical i set it to numeric."
  [1] "sameShape: countryScotland class was logical i set it to numeric."
  [1] "sameShape: countrySouth class was logical i set it to numeric."
  [1] "sameShape: countryTaiwan class was logical i set it to numeric."
  [1] "sameShape: countryThailand class was logical i set it to numeric."
  [1] "sameShape: countryTrinadad&Tobago class was logical i set it to numeric."
  [1] "sameShape: countryUnited-States class was logical i set it to numeric."
  [1] "sameShape: countryVietnam class was logical i set it to numeric."
  [1] "sameShape: countryYugoslavia class was logical i set it to numeric."
  [1] "sameShape: income>50K class was logical i set it to numeric."
  [1] "sameShape: verify that every factor as the right number of levels."
  [1] "build_scales: I will compute scale on  6 numeric columns."
  [1] "build_scales: it took me: 0.01s to compute scale for 6 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] "fastScale: I will scale 1 numeric columns."
  [1] "fastScale: it took me: 0s to scale 1 numeric columns."
  [1] "fastScale: I will scale 1 numeric columns."
  [1] "fastScale: it took me: 0s to unscale 1 numeric columns."
  [1] "build_scales: I will compute scale on  6 numeric columns."
  [1] "build_scales: it took me: 0.03s to compute scale for 6 numeric columns."
  [1] "setColAsNumeric: I will set some columns as numeric"
  [1] "setColAsNumeric: I am doing the column charCol1."
  [1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
  [1] "setColAsNumeric: I am doing the column charCol2."
  [1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
  [1] "setColAsCharacter: I will set some columns as character"
  [1] "setColAsCharacter: I am doing the column numCol."
  [1] "setColAsCharacter: I am doing the column factorCol."
  [1] "setColAsCharacter: I am doing the column charcol."
  [1] "setColAsCharacter: charcol is a character, i do nothing."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column date2."
  [1] "setColAsDate:1 NA have been created due to transformation to Date."
  [1] "setColAsDate: it took me: 0.02s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column date2."
  [1] "setColAsDate:1 NA have been created due to transformation to Date."
  [1] "setColAsDate: it took me: 0s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column date1."
  [1] "setColAsDate:1 NA have been created due to transformation to Date."
  [1] "setColAsDate: it took me: 0.03s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column ID."
  [1] "setColAsDate: it took me: 0s to transform 0 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column ID."
  [1] "setColAsDate: ID doesn't seem to be a date, if it really is please provide format."
  [1] "setColAsDate: it took me: 0.02s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column ID."
  [1] "setColAsDate: Since i generated only NAs i set ID as it was before."
  [1] "setColAsDate: it took me: 0s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column time."
  [1] "setColAsDate: it took me: 0s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column time_stamp_s."
  [1] "setColAsDate: it took me: 0s to transform 1 column(s) to Dates."
  [1] "setColAsDate: I will set some columns as Date."
  [1] "setColAsDate: I am doing the column time_stamp_ms."
  [1] "setColAsDate: it took me: 0s to transform 1 column(s) to Dates."
  [1] "setColAsFactor: I will set some columns to factor."
  [1] "setColAsFactor: I am doing the column education."
  [1] "setColAsFactor: it took me: 0.05s to transform 1 column(s) to factor."
  [1] "setColAsFactor: I will set some columns to factor."
  [1] "setColAsFactor: I am doing the column education."
  [1] "setColAsFactor: it took me: 0s to transform 0 column(s) to factor."
  [1] "setColAsFactor: I will set some columns to factor."
  [1] "setColAsFactor: I am doing the column education."
  [1] "setColAsFactor: education has more than 2 values, i don't transform it."
  [1] "setColAsFactor: it took me: 0s to transform 0 column(s) to factor."
  [1] "setColAsFactor: I will set some columns to factor."
  [1] "setColAsFactor: it took me: 0s to transform 0 column(s) to factor."
  [1] "Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "setColAsFactor: age has more than 10 values, i don't transform it."
  [1] "setColAsFactor: fnlwgt has more than 10 values, i don't transform it."
  [1] "setColAsFactor: education_num has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_gain has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_loss has more than 10 values, i don't transform it."
  [1] "setColAsFactor: hr_per_week has more than 10 values, i don't transform it."
  [1] "Transforming logical into binaries.\n"
  [1] "Previous distribution of column types:"
  col_class_init
   factor integer logical 
        9       6       1 
  [1] "Current distribution of column types:"
  col_class_end
   factor integer 
        9       7 
  [1] "setColAsFactor: I will set some columns to factor."
  [1] "setColAsFactor: it took me: 0s to transform 0 column(s) to factor."
  [1] "Transforming numerical variables into factors when length(unique(col)) <= 10."
  [1] "setColAsFactor: age has more than 10 values, i don't transform it."
  [1] "setColAsFactor: fnlwgt has more than 10 values, i don't transform it."
  [1] "setColAsFactor: education_num has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_gain has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_loss has more than 10 values, i don't transform it."
  [1] "setColAsFactor: hr_per_week has more than 10 values, i don't transform it."
  [1] "Transforming logical into binaries.\n"
  [1] "Previous distribution of column types:"
  col_class_init
   factor integer logical 
        9       6       1 
  [1] "Current distribution of column types:"
  col_class_end
   factor integer 
        9       7 
  [1] "setColAsFactor: age has more than 10 values, i don't transform it."
  [1] "setColAsFactor: fnlwgt has more than 10 values, i don't transform it."
  [1] "setColAsFactor: education_num has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_gain has more than 10 values, i don't transform it."
  [1] "setColAsFactor: capital_loss has more than 10 values, i don't transform it."
  [1] "setColAsFactor: hr_per_week has more than 10 values, i don't transform it."
  [1] "whichAreConstant: constantCol is constant."
  [1] "whichAreConstant: it took me 0s to identify 1 constant column(s)"
  [1] "whichAreInDouble: V2 is exactly equal to V1. I put it in drop list."
  [1] "whichAreInDouble: V3 is exactly equal to V1. I put it in drop list."
  [1] "whichAreInDouble: it took me 0.24s to identify 2 column(s) to drop."
  [1] "whichAreInDouble: V3 is exactly equal to V1. I put it in drop list."
  [1] "whichAreInDouble: it took me 0.27s to identify 1 column(s) to drop."
  [1] "whichAreInDouble: V2 is exactly equal to V1. I put it in drop list."
  [1] "whichAreInDouble: it took me 0.34s to identify 1 column(s) to drop."
  [1] "whichAreBijection: education_num is a bijection of education. I put it in drop list."
  [1] "whichAreBijection: it took me 0.25s to identify 1 column(s) to drop."
  [1] "whichAreBijection: education is a bijection of education_num. I put it in drop list."
  [1] "whichAreBijection: it took me 0.21s to identify 1 column(s) to drop."
  [1] "whichAreIncluded: constant is included in column sex."
  [1] "whichAreIncluded: are50OrMore is included in column age."
  [1] "whichAreIncluded: education is included in column education_num."
  [1] "whichAreIncluded: date3 is included in column date4."
  [1] "whichAreIncluded: num1 is included in column num3."
  [1] "whichAreIncluded: constant is included in column sex."
  [1] "whichAreIncluded: are50OrMore is included in column age."
  [1] "whichAreIncluded: education_num is included in column education."
  [1] "whichAreIncluded: date3 is included in column date4."
  [1] "whichAreIncluded: num1 is included in column num3."
  [1] "whichAreIncluded: constant is included in column sex."
  [1] "whichAreIncluded: sex is included in column id."
  [1] "whichAreIncluded: income is included in column id."
  [1] "whichAreIncluded: race is included in column id."
  [1] "whichAreIncluded: relationship is included in column id."
  [1] "whichAreIncluded: marital is included in column id."
  [1] "whichAreIncluded: type_employer is included in column id."
  [1] "whichAreIncluded: occupation is included in column id."
  [1] "whichAreIncluded: education is included in column education_num."
  [1] "whichAreIncluded: education_num is included in column id."
  [1] "whichAreIncluded: country is included in column id."
  [1] "whichAreIncluded: age is included in column id."
  [1] "whichAreIncluded: capital_loss is included in column id."
  [1] "whichAreIncluded: hr_per_week is included in column id."
  [1] "whichAreIncluded: capital_gain is included in column id."
  [1] "whichAreIncluded: mail is included in column id."
  [1] "whichAreIncluded: date3 is included in column date4."
  [1] "whichAreIncluded: date4 is included in column id."
  [1] "whichAreIncluded: date1 is included in column id."
  [1] "whichAreIncluded: date2 is included in column id."
  [1] "whichAreIncluded: num1 is included in column num3."
  [1] "whichAreIncluded: num3 is included in column id."
  [1] "whichAreIncluded: num2 is included in column id."
  [1] "whichAreIncluded: fnlwgt is included in column id."
  [1] "whichAreIncluded: constant is included in column sex."
  [1] "whichAreIncluded: sex is included in column id."
  [1] "whichAreIncluded: income is included in column id."
  [1] "whichAreIncluded: race is included in column id."
  [1] "whichAreIncluded: relationship is included in column id."
  [1] "whichAreIncluded: marital is included in column id."
  [1] "whichAreIncluded: type_employer is included in column id."
  [1] "whichAreIncluded: occupation is included in column id."
  [1] "whichAreIncluded: education is included in column education_num."
  [1] "whichAreIncluded: education_num is included in column id."
  [1] "whichAreIncluded: country is included in column id."
  [1] "whichAreIncluded: age is included in column id."
  [1] "whichAreIncluded: capital_loss is included in column id."
  [1] "whichAreIncluded: hr_per_week is included in column id."
  [1] "whichAreIncluded: capital_gain is included in column id."
  [1] "whichAreIncluded: mail is included in column id."
  [1] "whichAreIncluded: date3 is included in column date4."
  [1] "whichAreIncluded: date4 is included in column id."
  [1] "whichAreIncluded: date1 is included in column id."
  [1] "whichAreIncluded: date2 is included in column id."
  [1] "whichAreIncluded: num1 is included in column num3."
  [1] "whichAreIncluded: num3 is included in column id."
  [1] "whichAreIncluded: num2 is included in column id."
  [1] "whichAreIncluded: fnlwgt is included in column id."
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 244 SKIPPED: 0 FAILED: 4
  1. Error: (unknown) (@test_aggregate.R#18) 
  2. Error: (unknown) (@test_generateFromCharacter.R#9) 
  3. Error: (unknown) (@test_generateFromFactor.R#8) 
  4. Error: (unknown) (@test_prepareSet.R#7) 
  
  Error: testthat unit tests failed
  Execution halted