Running ‘spelling.R’ [0s/0s]
  Running ‘testthat.R’ [26s/32s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/tests.html
  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
  > 
  > library("testthat") # nolint
  > library("cleanepi") # nolint
  > 
  > test_check("cleanepi")
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  Found the following unrecognised column name:
                             `fake_name`! Detected 2 incorrect date sequences at lines: "6, 8".
  i Enter `print_report(data = dat, "incorrect_date_sequence")` to access them, where "dat" is the object used to store the output from this operation.
  Detected 2 incorrect date sequences at
                             lines: `6, 8`Insufficient number of columns to compare.i Found the following unrecognised column name: fake_name.
  ! Detected 2 incorrect date sequences at lines: "6, 8".
  i Enter `print_report(data = dat, "incorrect_date_sequence")` to access them, where "dat" is the object used to store the output from this operation.
  No incorrect date sequence was detected.i Cleaning column names
  i Removing constant columns and empty rows
  i Removing duplicated rows
  i No duplicates were found.
  i Cleaning column names
  i Replacing missing values with NA
  i Removing constant columns and empty rows
  i Removing duplicated rows
  i No duplicates were found.
  i Standardizing Date columns
  ! Detected 8 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  i Checking subject IDs format
  ! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  i Converting the following  column into numeric: sex
  i Performing dictionary-based cleaning
  i Checking whether date sequences are respected
  ! Detected 2 incorrect date sequences at lines: "6, 8".
  i Enter `print_report(data = dat, "incorrect_date_sequence")` to access them, where "dat" is the object used to store the output from this operation.
  i No duplicates were found.
  ! Detected 8 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Found <numeric> values that could also be of type <Date> in column: DOB.
  i It is possible to convert them into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from these columns (`data$target_column`).
  'target_columns' must be provided.i Cleaning column names
  i Removing constant columns and empty rows
  i Removing duplicated rows
  i No duplicates were found.
  i Checking subject IDs format
  ! Found <numeric> values that can also be of type <Date> in the following column: case_id.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  i No character column found from the input data.
  ! Found <numeric> values that can also be of type <Date> in the following column: col1.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  ! Found <numeric> values that can also be of type <Date> in the following column: col1.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  ! Found <numeric> values that can also be of type <Date> in the following column: col.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  Replace column names already existsAssertion on',keep,'failed: usage of 'linelist_tags'
                              is only reserved for 'linelist' type of data.Assertion on',keep or rename,'failed: Only the
                             column names from the input data can be renamed or
                             kept.Supplied incorrect target column name'fake_column_name' not found.! Found <numeric> values that can also be of type <Date> in the following column: case_id.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  ! Found 817.5 numeric values in gender.
  i Please consider the following options:
  * Converting characters into numeric
  * Replacing the numeric values by "NA" using the `replace_missing_values()` function.
  i The following column will be converted into numeric: age.
  Found `3750` numeric values in `test`. Consider
                             converting characters into numeric or replacing the
                             numeric values by `NA` using the
                             `replace_missing_values()` function.! Found 817.5 numeric values in gender.
  i Please consider the following options:
  * Converting characters into numeric
  * Replacing the numeric values by "NA" using the `replace_missing_values()` function.
  i The following column will be converted into numeric: age.
  ! Found <numeric> values that can also be of type <Date> in the following column: case_id.
  i They can be converted into <Date> using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))`
  * where "x" represents here the vector of values from the corresponding column (`data$target_column`).
  ! Found 817.5 numeric values in gender.
  i Please consider the following options:
  * Converting characters into numeric
  * Replacing the numeric values by "NA" using the `replace_missing_values()` function.
  i The following column will be converted into numeric: age.
  Found `3750` numeric values in `test`. Consider
                             converting characters into numeric or replacing the
                             numeric values by `NA` using the
                             `replace_missing_values()` function.i The following column will be converted into numeric: age.
  target_columns not specified and could not be
                             identified from scan_data() function.! mane matched equally multiple words in the wordlist
  i Using the first matched word in the wordlist.
  ! Cannot replace "femme" present in column gender but not defined in the dictionary.
  i You can either:
  * correct the misspelled option from the input data, or
  * add it to the dictionary using the `add_to_dictionary()` function.
  Can not replace the following values found in column  `gender` but not defined in the dictionary: `femme`.i You can either:
  * correct the misspelled option from the input data, or
  * add it to the dictionary using the `add_to_dictionary()` function.
  ! Found 57 duplicated rows in the dataset.
  i Use `print_report(dat, "found_duplicates")` to access them, where "dat" is the object used to store the output from this operation.
  ! Found 57 duplicated rows in the dataset.
  i Use `print_report(dat, "found_duplicates")` to access them, where "dat" is the object used to store the output from this operation.
  ! Found 57 duplicated rows in the dataset.
  i Use `print_report(dat, "found_duplicates")` to access them, where "dat" is the object used to store the output from this operation.
  ! Found 57 duplicated rows in the dataset.
  i Use `print_report(dat, "found_duplicates")` to access them, where "dat" is the object used to store the output from this operation.
  i No duplicates were found.
  Found 57 duplicated rows in the dataset.i No duplicates were found.
  ! Detected 8 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  Saving _problems/test-remove_constants-25.R
  ! Constant data was removed after 2 iterations.
  i Enter `print_report(dat, "constant_data")` for more information, where "dat" represents the object used to store the output from `remove_constants()`.
  Saving _problems/test-remove_constants-72.R
  Saving _problems/test-remove_constants-108.R
  Constant data was removed after 2 iterations.Could not detect the provided missing value character.! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  Unexpect type in the value for argument end_date.! Detected no values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 8 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  i The target  column will be standardized using the format: "%d/%m/%Y".
  ! Detected no values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  i The target  column will be standardized using the format: "%d/%m/%Y".
  ! Detected no values that comply with multiple formats and 3 values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected 4 values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  ! Detected no values that comply with multiple formats and no values that are outside of the specified time frame.
  i Enter `print_report(data = dat, "date_standardization")` to access them, where "dat" is the object used to store the output from this operation.
  Need to specify one format if all target columns have the
      same format. Provide one format per target column, otherwise.e! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  Detected incorrect subject ids at lines: 3, 5, 7Assertion on',data,'failed: input data frame must be
                   provided.Assertion on',id_column_name,'failed: Missing value not
                   allowed for 'id_column_name'.Assertion on',id_column_name,'failed: Must be a character of
                   length 1.Assertion on',nchar,'failed: template sample IDs format
                   must be provided.Found 2 duplicated rows in the subject IDs.Detected incorrect subject ids at lines: 3, 5, 7i No incorrect subject id was detected.
  No incorrect subject id was detected.! Detected 0 missing, 0 duplicated, and 1 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct it.
  ! Detected 0 missing, 0 duplicated, and 2 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  ! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  ! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  All subject ids in the correction table should be part of the
                   subject ids column of the input data.Column in 'correction_table' must be named as 'from' and
                   'to'Missing values found in study_id column at in lines: 7! Detected 0 missing, 0 duplicated, and 3 incorrect subject IDs.
  i Enter `print_report(data = dat, "incorrect_subject_id")` to access them, where "dat" is the object used to store the output from this operation.
  i You can use the `correct_subject_ids()` function to correct them.
  Assertion on',target_columns,'failed: all specified target
                   columns will be ignored because they are either empty or
                   constant.Assertion on',keep,'failed: usage of 'linelist_tags'
                   is only reserved for 'linelist' type of data.Some specified column names indices are out of bound.[ FAIL 3 | WARN 3 | SKIP 2 | PASS 322 ]
  
  ══ Skipped tests (2) ═══════════════════════════════════════════════════════════
  • On CRAN (2): 'test-print_report.R:43:3', 'test-print_report.R:59:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-remove_constants.R:25:3'): remove_constants works ──────────────
  Error in `print_report(dat, "constant_data")`: x The reactable package is required for printing the report.
  Backtrace:
      ▆
   1. └─cleanepi::print_report(dat, "constant_data") at test-remove_constants.R:25:3
   2.   └─cli::cli_abort(c(x = tr_("The {.pkg reactable} package is required for printing the report.")))
   3.     └─rlang::abort(...)
  ── Error ('test-remove_constants.R:72:3'): remove_constants works ──────────────
  Error in `print_report(dat, "constant_data")`: x The reactable package is required for printing the report.
  Backtrace:
      ▆
   1. └─cleanepi::print_report(dat, "constant_data") at test-remove_constants.R:72:3
   2.   └─cli::cli_abort(c(x = tr_("The {.pkg reactable} package is required for printing the report.")))
   3.     └─rlang::abort(...)
  ── Error ('test-remove_constants.R:108:3'): remove_constants works as expected ──
  Error in `print_report(dat, "constant_data")`: x The reactable package is required for printing the report.
  Backtrace:
      ▆
   1. └─cleanepi::print_report(dat, "constant_data") at test-remove_constants.R:108:3
   2.   └─cli::cli_abort(c(x = tr_("The {.pkg reactable} package is required for printing the report.")))
   3.     └─rlang::abort(...)
  
  [ FAIL 3 | WARN 3 | SKIP 2 | PASS 322 ]
  Error:
  ! Test failures.
  Execution halted