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Type 'q()' to quit R. > # 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") Found the following unrecognised column name: `fake_name`! Detected 2 incorrect date sequences at lines: "6, 8". i Enter `attr(dat, "report")[["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 `attr(dat, "report")[["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 i Checking subject IDs format ! Detected 3 invalid subject ids at lines: "3, 5, 7". 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 `attr(dat, "report")[["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 3 invalid subject ids at lines: "3, 5, 7". i You can use the `correct_subject_ids()` function to correct them. ! Found values that could also be of type in column: DOB. i It is possible to convert them into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from these columns (`data$target_column`). 'target_columns' must be provided.i Cleaning column names i Removing constant columns and empty rows i Removing duplicated rows i No duplicates were found. i Checking subject IDs format ! Found values that can also be of type in the following column: case_id. i They can be converted into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from the corresponding column (`data$target_column`). i No character column found from the input data. ! Found values that can also be of type in the following column: col1. i They can be converted into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from the corresponding column (`data$target_column`). ! Found values that can also be of type in the following column: col1. i They can be converted into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from the corresponding column (`data$target_column`). ! Found values that can also be of type in the following column: col. i They can be converted into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from the corresponding column (`data$target_column`). Replace column names already existsAssertion on',keep,'failed: usage of 'linelist_tags' is only reserved for 'linelist' type of data.Assertion on',keep or rename,'failed: Only the column names from the input data can be renamed or kept.Supplied incorrect target column name'fake_column_name' not found.! Found values that can also be of type in the following column: case_id. i They can be converted into using: `lubridate::as_date(x, origin = as.Date("1900-01-01"))` * where "x" represents here the vector of values from the corresponding column (`data$target_column`). ! Found 685.5 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 685.5 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 values that can also be of type in the following column: case_id. ℹ They can be converted into 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 685.5 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.! 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 `attr(dat, "report")[["duplicated_rows"]]` 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 `attr(dat, "report")[["duplicated_rows"]]` 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 `attr(dat, "report")[["duplicated_rows"]]` 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 `attr(dat, "report")[["duplicated_rows"]]` 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 3 invalid subject ids at lines: "3, 5, 7". ℹ You can use the `correct_subject_ids()` function to correct them. ! Constant data was removed after 2 iterations. i Enter `attr(dat, "report")[["constant_data"]]` for more information, where "dat" represents the object used to store the output from `remove_constants()`. Constant data was removed after 2 iterations.Could not detect the provided missing value character.Unexpect type in the value for argument end_date.i The target column will be standardized using the format: "%d/%m/%Y". i The target column will be standardized using the format: "%d/%m/%Y". Need to specify one format if all target columns have the same format. Provide one format per target column, otherwise.e! Detected 3 invalid subject ids at lines: "3, 5, 7". 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 3 invalid subject ids at lines: "3, 5, 7". i You can use the `correct_subject_ids()` function to correct them. Detected incorrect subject ids at lines: 3, 5, 7! Detected 3 invalid subject ids at lines: "3, 5, 7". i You can use the `correct_subject_ids()` function to correct them. i No incorrect subject id was detected. No incorrect subject id was detected.! Detected 1 invalid subject id at line: "3". i You can use the `correct_subject_ids()` function to correct it. ! Detected 2 invalid subject ids at lines: "3, 7". i You can use the `correct_subject_ids()` function to correct them. ! Detected 3 invalid subject ids at lines: "3, 5, 7". i You can use the `correct_subject_ids()` function to correct them. ! Detected 3 invalid subject ids at lines: "3, 5, 7". 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 3 invalid subject ids at lines: "3, 5, 7". i You can use the `correct_subject_ids()` function to correct them. ! Detected 3 invalid subject ids at lines: "3, 5, 7". 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 0 | WARN 2 | SKIP 2 | PASS 343 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test-print_report.R:43:3', 'test-print_report.R:59:3' [ FAIL 0 | WARN 2 | SKIP 2 | PASS 343 ] > > proc.time() user system elapsed 26.78 5.04 31.81