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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") ! 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 no missing, no 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 no missing, no 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 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 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 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 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.! 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 no missing, no 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. ! 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()`. 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 no missing, no 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 no missing, no 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 no missing, no 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 no missing, no 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 no missing, no 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 no missing, no 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 0 | WARN 2 | SKIP 2 | PASS 340 ] ══ 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 340 ] > > proc.time() user system elapsed 38.25 11.20 49.51