# Introduction to cleanepi

# An overview

Data cleaning is a critical step of data analysis, especially considering the messy nature of real-world data, which often includes duplicates, errors, incomplete entries, irrelevant formats, etc. Addressing these issues is essential for producing accurate, reliable, and reproducible results. However, data cleaning can pose a substantial barrier in data analysis due to the time-consuming nature of the process.

{cleanepi} is an R package designed specifically to address this challenge by offering tools to clean, curate, and standardize datasets. Tailored specifically for epidemiological data and compatible with data frame-like structures, {cleanepi} offers a suite of functions designed to streamline common data cleaning tasks.

This vignette provides a comprehensive guide to the functionalities encapsulated within {cleanepi}. It provides users with detailed insights into each function’s purpose and practical usage, equipping them with the tools necessary to navigate and manipulate cluttered datasets effectively.

    library("cleanepi")

# General data cleaning tasks

The main function in {cleanepi} is `clean_data()` that can perform the following tasks:

1. Clean up column names and convert them to more readable formats. This includes many sub-tasks such as replacing a space, dot, or hyphen between two words with underscore; converting camel-cases to snake-cases; substituting foreign characters with their corresponding English characters; and splitting a long word into multiple short words by capital characters within, if any, and connecting them with underscores.
2. Find and remove duplicated rows across all or some specific columns.
3. Remove empty rows and columns as well as constant columns, i.e. columns with the same value across all rows.
4. Replace missing entries with `NA`.
5. Standardizes date formats and verifies that the sequence of date events follows the correct chronological order across columns for all rows in the input data.
6. Convert `character` columns into `Date` if the column actually contains values of type `Date` to some extent (default is at least 60% of the values are `Date`).
7. Detect and remove rows with subject ids that do not comply with the expected format.
8. Perform dictionary-based cleaning: replace keys in specific columns with their corresponding values stored in a data dictionary provided as a data frame, detect and replace misspelled values with their correct ones.
9. Convert numbers written in characters into numeric.
10. Calculate the time span between columns or values of type Date.
11. Convert numeric values into Date.

In addition, the package also has two surrogate functions:
1. `scan_data()`: scan the input data to determine the percent of missing, numeric, character, logical and date values in every character column of the input data frame.
2. `print_report()`: print the data cleaning report.

    # IMPORTING THE TEST DATASET
    test_data <- readRDS(
      system.file("extdata", "test_df.RDS", package = "cleanepi")
    )

study_id | event_name | country_code | country_name | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex
--- | --- | --- | --- | --- | --- | --- | ---
PS001P2 | day 0 | 2 | Gambia | 01/12/2020 | 06/01/1972 | Dec 01, 2020 | 1
PS002P2 | day 0 | 2 | Gambia | 28/01/2021 | 02/20/1952 | Jan 01, 2021 | 1
PS004P2-1 | day 0 | 2 | Gambia | 15/02/2021 | 06/15/1961 | Feb 11, 2021 | -99
PS003P2 | day 0 | 2 | Gambia | 11/02/2021 | 11/11/1947 | Feb 01, 2021 | 1
P0005P2 | day 0 | 2 | Gambia | 17/02/2021 | 09/26/2000 | Feb 16, 2021 | 2
PS006P2 | day 0 | 2 | Gambia | 17/02/2021 | -99 | May 02, 2021 | 2
PB500P2 | day 0 | 2 | Gambia | 28/02/2021 | 11/03/1989 | Feb 19, 2021 | 1
PS008P2 | day 0 | 2 | Gambia | 22/02/2021 | 10/05/1976 | Sep 20, 2021 | 2
PS010P2 | day 0 | 2 | Gambia | 02/03/2021 | 09/23/1991 | Feb 26, 2021 | 1
PS011P2 | day 0 | 2 | Gambia | 05/03/2021 | 02/08/1991 | Mar 03, 2021 | 2

    # SCAN THE DATA
    scan_result <- scan_data(test_data)

Field_names | missing | numeric | date | character | logical
--- | --- | --- | --- | --- | ---
study_id | 0 | 0.0 | 0.0 | 1 | 0
event_name | 0 | 0.0 | 0.0 | 1 | 0
country_name | 0 | 0.0 | 0.0 | 1 | 0
date.of.admission | 0 | 0.0 | 1.0 | 0 | 0
dateOfBirth | 0 | 0.1 | 0.9 | 0 | 0
date_first_pcr_positive_test | 0 | 0.0 | 1.0 | 0 | 0

In {cleanepi}, every cleaning operation is encapsulated within a module, with detailed descriptions provided in the package design vignette. The parameters required for the main functions of each module are outlined in the sections below.

The `clean_data()` function, described above, can take the following arguments:

1. data: a `data.frame` or `linelist`.
2. standardize_column_names: a list of parameters to be used for standardizing the column names. These arguments are the inputs for the `standardize_column_names()` function.
3. replace_missing_values: a list of parameters to be used when replacing the missing values by `NA`. Elements of this list are the inputs for the `replace_missing_values()` function.
4. remove_duplicates: a list with the arguments that define the columns and other parameters to be considered when looking for duplicates. They are the input values for the `remove_duplicates()` function.
5. remove_constants: a list with the parameters that define whether to remove constant data or not. The values are the input for the `remove_constants()` function.
6. standardize_dates: a list of parameters that will be used to standardize the date values from the input data. They represent the input values for the `standardize_dates()` function.
7. standardize_subject_ids: a list of parameters that are needed to check whether the subject IDs comply with the expected format. These arguments are the input values of the `check_subject_ids()`.
8. to_numeric: a list with the parameters needed to convert the specified columns into numeric. When provided, the parameters will be the input values for the `convert_to_numeric()` function.
9. dictionary: a data frame that will be used to substitute the current values in the specified columns with those in the dictionary. It is the main argument for the `clean_using_dictionary()` function.
10. check_date_sequence: a list of arguments to be used when determining whether the sequence of the target date events is respected across all rows of the input data. The elements of this list are the input for the `check_date_sequence()` function.

It is important to note that only cleaning operations that are explicitly defined will be performed.

The below code chunk shows how to define a set of cleaning operations that we want to perform on the input data.

    # PARAMETERS FOR REPLACING MISSING VALUES WITH NA
    replace_missing_values <- list(target_columns = NULL, na_strings = "-99")

    # PARAMETERS FOR COLUMN NAMES STANDARDIZATION
    standardize_column_names <- list(keep = NULL, rename = NULL)

    # PARAMETERS FOR DUBLICATES DETECTION AND REMOVAL
    remove_duplicates <- list(target_columns = NULL)

    # PARAMETERS FOR STANDARDING DATES
    standardize_dates <- list(
      target_columns = NULL,
      error_tolerance = 0.4,
      format = NULL,
      timeframe = as.Date(c("1973-05-29", "2023-05-29")),
      orders = list(
        world_named_months = c("Ybd", "dby"),
        world_digit_months = c("dmy", "Ymd"),
        US_formats = c("Omdy", "YOmd")
      )
    )

    # PARAMETERS FOR STANDARDING SUBJECT ids
    standardize_subject_ids <- list(
      target_columns = "study_id",
      prefix = "PS",
      suffix = "P2",
      range = c(1, 100),
      nchar = 7
    )

    # CONVERT THE 'sex' COLUMN INTO NUMERIC
    to_numeric <- list(target_columns = "sex", lang = "en")

    # PARAMETERS FOR CONSTANT COLUMNS, EMPTY ROWS AND COLUMNS REMOVAL
    remove_constants <- list(cutoff = 1)

    # LAOD THE DATA DICTIONARY FOR DICTIONARY-BASED CLEANING
    dictionary <- readRDS(
      system.file("extdata", "test_dictionary.RDS", package = "cleanepi")
    )

    # CLEAN THE INPUT DATA FRAME
    cleaned_data <- clean_data(
      data = test_data,
      remove_constants = remove_constants,
      replace_missing_values = replace_missing_values,
      remove_duplicates = remove_duplicates,
      standardize_dates = standardize_dates,
      standardize_subject_ids = standardize_subject_ids,
      to_numeric = to_numeric,
      dictionary = dictionary
    )
    #> 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

## Using {cleanepi} functionalities with pipe operators

    # IMPORT THE INPUT DATASET
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # IMPORT THE DATA DICTIONARY
    test_dictionary <- readRDS(
      system.file("extdata", "test_dictionary.RDS", package = "cleanepi")
    )

    # SCAN THROUGH THE DATA
    scan_res <- scan_data(data)

    # PERFORM DATA CLEANING
    cleaned_data <- data %>%
      standardize_column_names(keep = NULL, rename = c(DOB = "dateOfBirth")) %>%
      replace_missing_values(target_columns = NULL, na_strings = "-99") %>%
      remove_constants(cutoff = 1.0) %>%
      remove_duplicates(target_columns = NULL) %>%
      standardize_dates(
        target_columns = NULL,
        error_tolerance = 0.4,
        format = NULL,
        timeframe = as.Date(c("1973-05-29", "2023-05-29"))
      ) %>%
      check_subject_ids(
        target_columns = "study_id",
        prefix = "PS",
        suffix = "P2",
        range = c(1L, 100L),
        nchar = 7L
      ) %>%
      convert_to_numeric(target_columns = "sex", lang = "en") %>%
      clean_using_dictionary(dictionary = test_dictionary)
    #> 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.

    # ADD THE DATA SCANNING RESULT TO THE REPORT
    cleaned_data <- add_to_report(
       x = cleaned_data,
       key = "scanning_result",
       value = scan_res
    )

## Printing the report

It returns the cleaned dataset. The report generated from the data cleaning operations is a `list object` that is attached to the cleaned data and can be accessed using the `attr()` or `attributes()` function. However, we strongly advice using the `print_report()` function to either display the entire report in your default browser in the form of an HTML file or only the report from a specific data cleaning operation. The function takes the following parameters:

1. data: the output object returned from the `clean_data()` or the main functions of each data cleaning modules.
2. print: a logical that specifies whether to open the report in your browser in the form of a HTML file or no. Default is `FALSE`.
3. output_file_name: a character used to specify the name of the report file, excluding any file extension. If no file name is supplied, one will be automatically generated with the format `cleanepi_report_YYMMDD_HHMMSS`.
4. format: a character with the file format of the report. Currently only `"html"` is supported.
5. what: a character with the name of the specific data cleaning report which would be displayed. The table below contains the possible values of the this argument.

keywords | goal
--- | ---
`incorrect_date_sequence` | To display rows with the incorrect date sequences
`colnames` | To display the column names before and after cleaning
`converted_into_numeric` | To display the names of the columns that that have
been converted into numeric |
`date_standardization` | To display rows in the cleaned data with date values
`misspelled_values` | To display the detected misspelled values
`removed_duplicates` | To display the duplicated rows that have been removed
`found_duplicates` | To display the duplicated rows
`constant_data` | To display the constant data i.e. constant columns, empty rows and columns
`missing_values_replaced_at` | To display the names of the columns where the missing value strings have been replaced with NA
`incorrect_subject_id` | To display the missing, duplicated and invalid subject subject IDs

that are outside of the specified time frame, and rows with date values that comply with multiple formats |

    # PRINT THE ENTIRE DATA CLEANING REPORT
    print_report(
      data = cleaned_data,
      what = NULL,
      print = TRUE,
      report_title = "{cleanepi} data cleaning report",
      output_file_name = NULL,
      format = "html"
    )

# Specific data cleaning tasks

{cleanepi} luckily provides users with the flexibility to call a specific function if they wish to perform that particular task individually. This approach allows users to have more control over the data cleaning process and to apply additional data cleaning functions as needed.

For example, some data cleaning operations, such as renaming columns, removing empty rows and columns, removing columns with the same values across all rows, and standardizing date columns, play a central role in standardizing the epidemiological data. These operations can be performed within the framework of the `clean_data()` function or alone by calling the relevant function.

This setup offers users both convenience and flexibility, as they can perform all the cleaning operations at once, or execute them individually according to their specific needs.

## Remove constant data

Some datasets contain constant columns (columns with the same values across all rows), and/or empty rows and columns (rows or columns where all values are missing i.e `NA`). The `remove_constants()` function can be used to remove such “noise”. The function takes the following arguments:

1. data: the input data frame or linelist,
2. cutoff: a numeric, between `0` and `1`, to be used when removing empty rows and columns. When provided, only rows and columns where the percent of missing data is greater than this cut-off will removed. Rows and columns with 100% missing values will be remove by default.

    # IMPORT THE INPUT DATA
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # INTRODUCE AN EMPTY COLUMN
    data$empty_column <- NA

    # remove the constant columns, empty rows and columns
    dat <- remove_constants(
      data = data,
      cutoff = 1
    )

study_id | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex
--- | --- | --- | --- | ---
PS001P2 | 01/12/2020 | 06/01/1972 | Dec 01, 2020 | 1
PS002P2 | 28/01/2021 | 02/20/1952 | Jan 01, 2021 | 1
PS004P2-1 | 15/02/2021 | 06/15/1961 | Feb 11, 2021 | -99
PS003P2 | 11/02/2021 | 11/11/1947 | Feb 01, 2021 | 1
P0005P2 | 17/02/2021 | 09/26/2000 | Feb 16, 2021 | 2
PS006P2 | 17/02/2021 | -99 | May 02, 2021 | 2
PB500P2 | 28/02/2021 | 11/03/1989 | Feb 19, 2021 | 1
PS008P2 | 22/02/2021 | 10/05/1976 | Sep 20, 2021 | 2
PS010P2 | 02/03/2021 | 09/23/1991 | Feb 26, 2021 | 1
PS011P2 | 05/03/2021 | 02/08/1991 | Mar 03, 2021 | 2

The `remove_constants()` function returns a dataset where all constant columns, empty rows and columns are iteratively removed. Note that when the first iteration of constant data removal results in a dataset with new empty rows and/or columns and constant columns, this process will be repeated several times until there is no more constant data. Rows and columns that were deleted at any iterations will be mentioned in the report object.

    print_report(dat, "constant_data")

iteration | empty_columns | empty_rows | constant_columns
--- | --- | --- | ---
1 | empty_column | NA | event_name, country_code, country_name

## Cleaning column names

The syntax used to name the columns of a dataset during its creation depends on many factors such as the language, the naming convention, etc. We provide, in {cleanepi}, the `standardize_column_names()` function to clean up column names and convert them to more intuitive formats. It performs many sub-tasks including: replacing a space, dot, or hyphen between two words with underscore; converting camel-cases to snake-cases; substituting foreign characters with their corresponding English characters; and splitting along word into multiple short words by capital characters within, if any, and connecting them with underscores. The function can take the following arguments:

1. data: the input data frame or linelist
2. keep: a vector of column names to maintain as they are. When dealing with a linelist, this can be set to `linelist_tags`, to maintain the tagged column names. The Default is `NULL`.
3. rename: a vector of column names to be renamed in the form of `new_name = "old_name"`. If not provided, all columns will undergo standardization.

    # IMPORT AND PRINT THE INITAL COLUMN NAMES
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))
    print(colnames(data))
    #> [1] "study_id"                     "event_name"
    #> [3] "country_code"                 "country_name"
    #> [5] "date.of.admission"            "dateOfBirth"
    #> [7] "date_first_pcr_positive_test" "sex"

    # KEEP 'date.of.admission' AS IS
    cleaned_data <- standardize_column_names(
      data = data,
      keep = "date.of.admission"
    )
    print(colnames(cleaned_data))
    #> [1] "study_id"                     "event_name"
    #> [3] "country_code"                 "country_name"
    #> [5] "date.of.admission"            "date_of_birth"
    #> [7] "date_first_pcr_positive_test" "sex"

    # KEEP 'date.of.admission' AS IS, BUT RENAME 'dateOfBirth' AND 'sex' TO
    # 'DOB' AND 'gender' RESPECTIVELY
    cleaned_data <- standardize_column_names(
      data = data,
      keep = "date.of.admission",
      rename = c(DOB = "dateOfBirth", gender = "sex")
    )

    # PRINT THE REPORT FOR THIS OPERATION
    print_report(cleaned_data, "colnames")

before | after
--- | ---
study_id | study_id
event_name | event_name
country_code | country_code
country_name | country_name
date.of.admission | date.of.admission
dateOfBirth | DOB
date_first_pcr_positive_test | date_first_pcr_positive_test
sex | gender

By providing the function with these parameters, the users can redefine the column names and get easy to work with column names. This enables a more comprehensive naming system that is tailored to the needs of the user.

## Replacing missing entries with `NA`

It is common to have missing values in an input dataset. By default, R expects missing values to be represented by `NA`. However, this is not always the case as some dataset can contain a specific character string that denotes the missing value. In the presence of such a scenario, users can call the `replace_missing_values()` function to substitute these missing values with `NA`. This will make the data suitable for any data science operations. The function takes the following arguments:

1. data: the input data frame or linelist.
2. target_columns: a vector of column names. If provided, the substitution of missing values will only be executed in those specified columns. When the input data is a `linelist` object, this parameter can be set to `linelist_tags` if you wish to replace missing values with `NA` on tagged columns only. The default value is `NULL` i.e. replace missing values across all columns.
3. na_strings: a vector of character strings that represents the missing values in the columns of interest. By default, it utilizes `cleanepi::common_na_strings`. However, if the missing values string in the columns of interest is not included in this predefined vector, it can be used as the value for this argument.

    # VISUALIZE THE PREDEFINED VECTOR OF MISSING CHARACTERS
    print(cleanepi::common_na_strings)
    #>  [1] "missing"       "NA"            "N A"           "N/A"
    #>  [5] "#N/A"          "NA "           " NA"           "N /A"
    #>  [9] "N / A"         " N / A"        "N / A "        "na"
    #> [13] "n a"           "n/a"           "na "           " na"
    #> [17] "n /a"          "n / a"         " a / a"        "n / a "
    #> [21] "NULL"          "null"          ""              "\\?"
    #> [25] "\\*"           "\\."           "not available" "Not Available"
    #> [29] "NOt available" "not avail"     "Not Avail"     "nan"
    #> [33] "NAN"           "not a number"  "Not A Number"

    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # REPLACE ALL OCCURENCES OF "-99" WITH NA IN THE "sex" COLUMN
    cleaned_data <- replace_missing_values(
      data = data,
      target_columns = "sex",
      na_strings = "-99"
    )

    # REPLACE ALL OCCURENCES OF "-99" WITH NA FROM ALL COLUMNS
    cleaned_data <- replace_missing_values(
      data = data,
      target_columns = NULL,
      na_strings = "-99"
    )

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "missing_values_replaced_at")
    #> [1] "dateOfBirth" "sex"

study_id | event_name | country_code | country_name | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex
--- | --- | --- | --- | --- | --- | --- | ---
PS001P2 | day 0 | 2 | Gambia | 01/12/2020 | 06/01/1972 | Dec 01, 2020 | 1
PS002P2 | day 0 | 2 | Gambia | 28/01/2021 | 02/20/1952 | Jan 01, 2021 | 1
PS004P2-1 | day 0 | 2 | Gambia | 15/02/2021 | 06/15/1961 | Feb 11, 2021 | NA
PS003P2 | day 0 | 2 | Gambia | 11/02/2021 | 11/11/1947 | Feb 01, 2021 | 1
P0005P2 | day 0 | 2 | Gambia | 17/02/2021 | 09/26/2000 | Feb 16, 2021 | 2
PS006P2 | day 0 | 2 | Gambia | 17/02/2021 | NA | May 02, 2021 | 2
PB500P2 | day 0 | 2 | Gambia | 28/02/2021 | 11/03/1989 | Feb 19, 2021 | 1
PS008P2 | day 0 | 2 | Gambia | 22/02/2021 | 10/05/1976 | Sep 20, 2021 | 2
PS010P2 | day 0 | 2 | Gambia | 02/03/2021 | 09/23/1991 | Feb 26, 2021 | 1
PS011P2 | day 0 | 2 | Gambia | 05/03/2021 | 02/08/1991 | Mar 03, 2021 | 2

## Standardizing Dates

The default date format in R is `YYYY-MM-DD` (the ISO8601 format). However, it is very common to encounter date values that are written differently from this. Also, there are cases where a column in a data frame contains both values of type `Date`, `character` or others.

The `standardize_dates()` function provides a comprehensive set of options for converting date columns into a specified format and handling various scenarios, such as different date formats and mixed data types in a column.

Entries which cannot be processed result in `NA`. An error threshold can be used to define the maximum number of resulting `NA` (i.e. entries without an identified date) that can be tolerated. If this threshold is exceeded, the initial values will be returned.

The function expects the following arguments:

1. data: A data frame or linelist (required).
2. target_columns: A vector of the names of the columns to be converted (optional). When not provided, the function will attempt to detect date columns and perform the conversion if the conditions above are met.
3. format: A format of the values in the specified columns (optional). If not provided, the function will attempt to infer the format.
4. timeframe: The expected time frame within which the date values should fall. Values outside of this range will be set to `NA` (optional).
5. error_tolerance: The maximum percentage of `NA` values (non date values) that can be allowed in a converted column. Default is 40% i.e. `0.4`.
6. orders: A character vector or list of codes for fine-grained parsing of dates. It is used for parsing of mixed dates. If a list is supplied, that list will be used for successive tries in parsing. Default is:

    orders <- list(
      quarter_partial_dates = c("Y", "Ym", "Yq"),
      world_digit_months = c("ymd", "ydm", "dmy", "mdy", "myd", "dym", "Ymd", "Ydm",
                             "dmY", "mdY", "mYd", "dYm"),
      world_named_months = c("dby", "dyb", "bdy", "byd", "ybd", "ydb", "dbY", "dYb",
                             "bdY", "bYd", "Ybd", "Ydb"),
      us_format = c("Omdy", "YOmd")
    )

> ⚠️ The `error_tolerance` must be used with caution. When it is set, and the percentage of non-date values (NA i.e. values that were not converted into date) in a character column is greater than this threshold, the column will be returned as it is. The values outside of this timeframe can be accessed from the report object via its `date_standardization` element.

> The value for the `orders` argument can be modified to suit the user’s needs. Other date formats can be specified too. For instance, if you want to prioritize American-style dates with numeric months, you can switch the second and third elements of the default orders as shown below:

    # GIVE PRIORITY TO AMERICAN-STYLE DATES
    us_ord <- orders[c(1L, 3L, 2L)]

    # ADD A FORMAT WITH HOURS TO THE EXISTING orders
    # THIS WILL ALLOW FOR THE CONVERSION OF VALUES SUCH AS "2014_04_05_23:15:43"
    # WHEN THEY APPEAR IN THE TARGET COLUMNS.
    orders$ymdhms <- c("Ymdhms", "Ymdhm")

This function provides users with the flexibility to standardize date columns in their dataset according to specified requirements, including `format`, `timeframe`, and `error tolerance` for conversion from character to date columns.

    # STANDARDIZE VALUES IN THE 'date.of.admission' COLUMN
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))
    head(data$date_first_pcr_positive_test)
    #> [1] "Dec 01, 2020" "Jan 01, 2021" "Feb 11, 2021" "Feb 01, 2021" "Feb 16, 2021"
    #> [6] "May 02, 2021"

    cleaned_data <- standardize_dates(
      data = test_data,
      target_columns = "date.of.admission",
      format = NULL,
      timeframe = as.Date(c("2021-01-01", "2021-12-01")),
      error_tolerance = 0.4,
      orders = list(
        world_named_months = c("Ybd", "dby"),
        world_digit_months = c("dmy", "Ymd"),
        US_formats = c("Omdy", "YOmd")
      )
    )
    #> ! Detected 4 values that comply with multiple formats and 1 value that is
    #>   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.

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "date_standardization")
    #> $multi_format_dates
    #>               field idx world_named_months world_digit_months US_formats
    #> 1 date.of.admission   1               <NA>         2020-12-01 2020-01-12
    #> 2 date.of.admission   4               <NA>         2021-02-11 2021-11-02
    #> 3 date.of.admission   9               <NA>         2021-03-02 2021-02-03
    #> 4 date.of.admission  10               <NA>         2021-03-05 2021-05-03
    #>
    #> $out_of_range_dates
    #>   idx            column original_value
    #> 1   1 date.of.admission     01/12/2020

This function returns the input dataset where the (specified) columns are converted into Date if the conditions are met. The report made from this operation contains:

- A data frame with the columns where date values were standardized,
- A data frame with the values that fall outside of the specified timeframe,
- A data frame featuring date values that can comply with more than one specified format.

    # STANDARDIZE VALUES IN ALL COLUMNS
    cleaned_data <- standardize_dates(
      data = data,
      target_columns = NULL,
      format = NULL,
      timeframe = NULL,
      error_tolerance = 0.4,
      orders = list(
        world_named_months = c("Ybd", "dby"),
        world_digit_months = c("dmy", "Ymd"),
        US_formats = c("Omdy", "YOmd")
      )
    )
    #> ! 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.
    #> ! Found <numeric> values that could also be of type <Date> in column:
    #>   dateOfBirth.
    #> 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`).

    # PRINT THE REPORT
    print_report(cleaned_data, "date_standardization")
    #> $multi_format_dates
    #>               field idx world_named_months world_digit_months US_formats
    #> 1 date.of.admission   1               <NA>         2020-12-01 2020-01-12
    #> 2 date.of.admission   4               <NA>         2021-02-11 2021-11-02
    #> 3 date.of.admission   9               <NA>         2021-03-02 2021-02-03
    #> 4 date.of.admission  10               <NA>         2021-03-05 2021-05-03
    #> 5       dateOfBirth   1               <NA>         1972-01-06 1972-06-01
    #> 6       dateOfBirth   7               <NA>         1989-03-11 1989-11-03
    #> 7       dateOfBirth   8               <NA>         1976-05-10 1976-10-05
    #> 8       dateOfBirth  10               <NA>         1991-08-02 1991-02-08

study_id | event_name | country_code | country_name | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex
--- | --- | --- | --- | --- | --- | --- | ---
PS001P2 | day 0 | 2 | Gambia | 2020-12-01 | 1972-01-06 | 2020-12-01 | 1
PS002P2 | day 0 | 2 | Gambia | 2021-01-28 | 1952-02-20 | 2021-01-01 | 1
PS004P2-1 | day 0 | 2 | Gambia | 2021-02-15 | 1961-06-15 | 2021-02-11 | -99
PS003P2 | day 0 | 2 | Gambia | 2021-02-11 | 1947-11-11 | 2021-02-01 | 1
P0005P2 | day 0 | 2 | Gambia | 2021-02-17 | 2000-09-26 | 2021-02-16 | 2
PS006P2 | day 0 | 2 | Gambia | 2021-02-17 | NA | 2021-05-02 | 2
PB500P2 | day 0 | 2 | Gambia | 2021-02-28 | 1989-03-11 | 2021-02-19 | 1
PS008P2 | day 0 | 2 | Gambia | 2021-02-22 | 1976-05-10 | 2021-09-20 | 2
PS010P2 | day 0 | 2 | Gambia | 2021-03-02 | 1991-09-23 | 2021-02-26 | 1
PS011P2 | day 0 | 2 | Gambia | 2021-03-05 | 1991-08-02 | 2021-03-03 | 2

## Standardizing subject IDs

### Detecting incorrect, duplicated, and missing subject IDs

The `check_subject_ids()` function is designed to identify rows from the input dataset where the IDs don’t comply with the expected subject IDs format. It expects the following parameters:

1. data: A data frame or linelist (required).
2. target_columns: The name of the column containing the subject IDs in the dataset (required).
3. nchar: The expected number of character in the subject ids (optional).
4. prefix: A string. If subject IDs have a specific prefix, it is used as a value for this argument. This is optional and can be omitted if there is no prefix.
5. suffix: A string. If subject IDs have a specific suffix, it is used as a value for this argument. It can be ignored otherwise.
6. range: A vector of two elements. If there is an expected range of numbers within the subject IDs, define it using this parameter. It is optional and can be omitted if there is no specific range.

By providing these parameters, the function becomes a versatile tool for data cleaning, ensuring that the user is alerted on the presence of unexpected, missing and duplicated subject IDs. When using the function, make sure to tailor the parameters according to the specific requirements of your dataset and the expected characteristics of the subject IDs.

    data <-  readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # MAKE FIRST AND LAST SUBJECT IDS THE SAME
    data$study_id[10] <- data$study_id[1]

    # SET SUBJECT ID NUMBER 9 TO NA
    data$study_id[9] <- NA

    # DETECT INCORRECT SUBJECT IDs
    cleaned_data <- check_subject_ids(
      data = data,
      target_columns = "study_id",
      prefix = "PS",
      suffix = "P2",
      range = c(1L, 100L),
      nchar = 7L
    )
    #> ! Detected 1 missing, 2 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.

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "incorrect_subject_id")
    #> $idx_missing_ids
    #> [1] "9"
    #>
    #> $duplicated_ids
    #> # A tibble: 2 × 3
    #> # Groups:   study_id [1]
    #>   row_id group_id study_id
    #>    <int>    <int> <chr>
    #> 1      1        1 PS001P2
    #> 2     10        1 PS001P2
    #>
    #> $invalid_subject_ids
    #>   idx       ids
    #> 1   3 PS004P2-1
    #> 2   5   P0005P2
    #> 3   7   PB500P2

The `check_subject_ids()` function returns the input dataset and send a warming when it finds some incorrect ids.

In addition to detecting undesirable subject IDs, the function will also look for missing and duplicated IDs. As the result of this, the report made from this operation can contain two extra elements: missing_ids (a vector of row indexes where there is a missing IDs) and duplicated_ids (a data frame of rows with the duplicated IDs).

### Correct wrong subject ids

After the detection of the incorrect subject ids using the `check_subject_ids()`, use the `correct_subject_ids()` to replace non complying ids with the correct ones. The function requires a data frame with the following two columns:

1. from: a column with the incorrect subject ids,
2. to: a column with the values to be used to substitute the incorrect ids.

    # IMPORT THE INPUT DATA
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # GENERATE THE CORRECTION TABLE
    correction_table <- data.frame(
      from = c("P0005P2", "PB500P2", "PS004P2-1"),
      to = c("PB005P2", "PB050P2", "PS004P2"),
      stringsAsFactors = FALSE
    )

    # PERFORM THE CORRECTION
    dat <- correct_subject_ids(
      data = data,
      target_columns = "study_id",
      correction_table = correction_table
    )

## Checking date sequence

The `check_date_sequence()` function verifies the order of sequences in date event columns within a dataset. It ensures that the values in the specified date columns follow the desired chronological order. Here are the arguments accepted by the function:

1. data: A data frame or linelist (required).
2. target_columns: A vector containing the names of the date columns of interest. These columns should be listed in the expected order of occurrence that reflects the chronological sequence of events. For example, `target_columns = c("date_of_infection", "date_of_admission", "date_of_death")`.

By utilizing these arguments, the `check_date_sequence()` function facilitates the validation of date sequences within a dataset, ensuring data integrity and accuracy for further analysis. Additionally, it offers flexibility by allowing users to choose whether to remove rows with incorrect sequences or store them for further examination in the report object.

    # IMPORT THE DATA AND STANDARDIZE THE TARGET DATE COLUMNS
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi")) %>%
      standardize_dates(
        target_columns = c("date_first_pcr_positive_test", "date.of.admission")
      )
    #> ! 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.

    # DETECT ROWS WITH INCORRECT DATE SEQUENCE
    cleaned_data <- check_date_sequence(
      data = data,
      target_columns = c("date_first_pcr_positive_test", "date.of.admission")
    )
    #> ! 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.

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "incorrect_date_sequence")

| date_first_pcr_positive_test | date.of.admission | row_id
--- | --- | --- | ---
6 | 2021-05-02 | 2021-02-17 | 6
8 | 2021-09-20 | 2021-02-22 | 8

The `check_date_sequence()` function returns the input dataset, augmented with an attributes named as `incorrect_date_sequence` if there are rows with incorrect date sequences. This attribute highlights any discrepancies found in the date sequences, enabling users to take appropriate actions.

## Converting character columns into numeric

In certain scenarios, the input data contains columns where the numbers are written in letters. For instance, the ages of a study participants can be written in letters. Similarly, a column can contain values written in both numbers and letters (an age column where some values are written in numbers and others in letters). The `convert_to_numeric()` function offers the framework to convert all numbers written in letters from a given column into numeric. It makes call of the main function from the {numberize} package and takes the following arguments:

1. data: A data frame or linelist (required).
2. target_columns: A vector containing the names of the columns of interest. When dealing with a linelist, this can be set to `linelist_tags` if the tagged columns are the one to be converted into numeric. When `target_columns = NULL`, the function uses the output form the `scan_data()` function to identify the columns where the proportion of numeric values is at least twice as the percentage of character values. Those columns will be the columns of interest and the character values in them will be converted into numeric.
   Note that any string in such column that can not be converted into numeric will be set to `NA` in the resulting data.
3. lang: A character string with the language in which the letters are written. Currently one of `"en", "fr", or "es"` for English, French or Spanish respectively.

    # CONVERT THE 'age' COLUMN IN THE TEST LINELIST DATA
    dat <- readRDS(system.file("extdata", "messy_data.RDS", package = "cleanepi"))
    head(dat$age, 10L)
    #>  [1] "37"           "seventy-four" "17"           "3"            "37"
    #>  [6] NA             "6"            "26"           "eleven"       "44"

    cleaned_data <- convert_to_numeric(
      data = dat,
      target_columns = "age",
      lang = "en"
    )
    head(cleaned_data$age, 10L)
    #>  [1] 37 74 17  3 37 NA  6 26 11 44

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "converted_into_numeric")
    #> [1] "age"

## Converting numeric values into date

Some columns in a data frame might contain numeric values that represents the number of days elapsed between two events. For instance, the recruitment day of individuals in a study can be stored as a numeric column where the numeric values are the count of days between when they are recruited and when there were admitted in the hospital. The actual dates when the individuals were recruited can be retrieved using the `convert_numeric_to_date()` function. This function can take the following parameters:

1. data: the input data frame or linelist.
2. target_columns: a vector or a comma-separated list of columns names to be converted from numeric to date. When the input data is a `linelist` object, this parameter can be set to `linelist_tags` if tagged variables are the target columns.
3. ref_date: a reference date
4. forward: a Boolean that indicates whether the counts started after the reference date (`TRUE`) or not (`FALSE`). The default is `TRUE`.

    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi")) %>%
      standardize_dates(target_columns = "date.of.admission")
    #> ! 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.

    # CREATE THE RECRUITMENT DATE COLUMNS
    data$recruitment_date <- sample(20:50, nrow(data), replace = FALSE)

    # RETRIVE THE DATE INDIVIDUALS WERE RECRUITED FROM THEIR DATE OF ADMISSION
    dat <- convert_numeric_to_date(
      data = data,
      target_columns = "recruitment_date",
      ref_date = "date.of.admission",
      forward = TRUE
    )

    # RETRIVE THE DATE INDIVIDUALS WERE RECRUITED FROM 2019-10-13
    dat <- convert_numeric_to_date(
      data = data,
      target_columns = "recruitment_date",
      ref_date = as.Date("2019-10-13"),
      forward = FALSE
    )

The function returns the input data where the values in the target columns(s) are converted into Date. This enables the usage of {cleanepi}’s functions that operates on Date columns as well as the powerful functions that can be used to manipulate Dates from the {base} R or {lubridate} packages.

## Finding duplicated rows

The `find_duplicates()` function serves the purpose of identifying duplicated rows within a given dataset. It accepts the following parameters:

1. data: The input data frame or linelist.
2. target_columns: A vector containing either column names or column indices from which duplicated rows will be identified. If `NULL` is passed, duplicates will be detected across all columns of the dataset. Notably, if the input dataset is a `linelist` object, `target_columns` can be set to `linelist_tags` specifically to identify duplicates across the tagged variables only.

By leveraging the `find_duplicates()` function with appropriate parameters, users can efficiently pinpoint duplicated rows within their datasets, either across all columns or selectively across tagged variables in a `linelist` object.

    # IMPORT A `linelist` DATA
    data <- readRDS(
      system.file("extdata", "test_linelist.RDS", package = "cleanepi")
    )

    # SHOW THE TAGGED VARIABLES
    linelist::tags(data)
    #> $date_onset
    #> [1] "dt_onset"
    #>
    #> $date_reporting
    #> [1] "dt_report"
    #>
    #> $gender
    #> [1] "sex"
    #>
    #> $outcome
    #> [1] "outcome"

    # FIND DUPLICATES ACROSS ALL COLUMNS EXCEPT THE SUBJECT ids COLUMN
    all_columns <- names(data)
    target_columns <- all_columns[all_columns != "id"]
    dups <- find_duplicates(
      data = data,
      target_columns = target_columns
    )
    #> ℹ No duplicates were found.

    # FIND DUPLICATES ACROSS TAGGED VARIABLES
    dups <- find_duplicates(
      data = data,
      target_columns = "linelist_tags"
    )
    #> ! 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.

Upon execution, the `find_duplicates()` function identifies all duplicated rows either based on all columns or those specified, and stores them in the report, where in addition to the existing columns, it appends two extra columns to the dataset:

1. `row_id`: Contains indices of the duplicated rows from the original input dataset.
2. `group_id`: Contains unique identifiers assigned to each duplicated group, which is defined as a set of rows sharing identical values in the designated columns of interest.

By including these extra columns, users gain insights into the specific rows identified as duplicates and their corresponding group identifiers, enabling efficient analysis and management of duplicated data within the dataset. When duplicates are found, the report from this operation will contain the following elements:

- duplicated_rows: A data frame with the detected duplicates.
- duplicates_checked_from: A vector of column names from which duplicates were identified.

    # PRINT THE REPORT FROM DUPLICATES FINDING
    print_report(dups, "found_duplicates")
    #> $duplicated_rows
    #> # A tibble: 57 × 6
    #> # Groups:   dt_onset, dt_report, sex, outcome [23]
    #>    row_id group_id dt_onset   dt_report  sex   outcome
    #>     <int>    <int> <date>     <date>     <fct> <fct>
    #>  1     26        1 2015-05-21 2015-06-03 M     Alive
    #>  2     33        1 2015-05-21 2015-06-03 M     Alive
    #>  3     55        2 2015-05-30 2015-06-06 M     Alive
    #>  4     62        2 2015-05-30 2015-06-06 M     Alive
    #>  5     23        3 2015-05-31 2015-06-02 M     Dead
    #>  6     24        3 2015-05-31 2015-06-02 M     Dead
    #>  7     99        4 2015-05-31 2015-06-09 M     Alive
    #>  8    105        4 2015-05-31 2015-06-09 M     Alive
    #>  9     27        5 2015-06-01 2015-06-03 M     Alive
    #> 10     31        5 2015-06-01 2015-06-03 M     Alive
    #> # ℹ 47 more rows
    #>
    #> $duplicates_checked_from
    #> [1] "dt_onset"  "dt_report" "sex"       "outcome"

## Removing duplicates

To eliminate duplicated rows from a dataset, the `remove_duplicates()` function can be employed. This function internally utilizes the `find_duplicates()` function and expects the following parameters:

1. data: A data frame or linelist from which duplicated rows will be removed.
2. target_columns: A vector containing either column names or indices specifying the columns from which duplicated rows will be identified. If set to `NULL`, the function will remove duplicates across all columns. If the input dataset is a `linelist` object, setting this parameter to `linelist_tags` will identify and remove duplicates across the tagged variables only.

    # REMOVE DUPLICATE ACROSS TAGGED COLUMNS ONLY.
    data <- readRDS(
      system.file("extdata", "test_linelist.RDS", package = "cleanepi")
    )

    cleaned_data <- remove_duplicates(
      data = readRDS(
        system.file("extdata", "test_linelist.RDS", package = "cleanepi")
      ),
      target_columns = "linelist_tags"
    )
    #> ! 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.

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "removed_duplicates")

row_id | dt_onset | dt_report | sex | outcome
--- | --- | --- | --- | ---
33 | 2015-05-21 | 2015-06-03 | M | Alive
62 | 2015-05-30 | 2015-06-06 | M | Alive
24 | 2015-05-31 | 2015-06-02 | M | Dead
105 | 2015-05-31 | 2015-06-09 | M | Alive
31 | 2015-06-01 | 2015-06-03 | M | Alive
60 | 2015-06-01 | 2015-06-06 | F | Alive
73 | 2015-06-01 | 2015-06-07 | F | Alive
78 | 2015-06-01 | 2015-06-07 | F | Alive
82 | 2015-06-01 | 2015-06-07 | F | Alive
85 | 2015-06-01 | 2015-06-07 | F | Alive
51 | 2015-06-02 | 2015-06-06 | F | Dead
58 | 2015-06-02 | 2015-06-06 | M | Alive
61 | 2015-06-02 | 2015-06-06 | M | Alive
122 | 2015-06-02 | 2015-06-10 | F | Alive
63 | 2015-06-03 | 2015-06-06 | F | Alive
71 | 2015-06-04 | 2015-06-07 | F | Alive
79 | 2015-06-04 | 2015-06-07 | F | Alive
87 | 2015-06-04 | 2015-06-07 | F | Alive
108 | 2015-06-04 | 2015-06-09 | F | Alive
107 | 2015-06-06 | 2015-06-09 | F | Alive
104 | 2015-06-06 | 2015-06-09 | M | Alive
133 | 2015-06-08 | 2015-06-12 | M | Alive
135 | 2015-06-08 | 2015-06-12 | M | Alive
138 | 2015-06-10 | 2015-06-12 | M | Alive
49 | NA | 2015-06-06 | M | Alive
80 | NA | 2015-06-07 | M | Alive
95 | NA | 2015-06-08 | M | Alive
121 | NA | 2015-06-10 | M | Alive
161 | NA | 2015-06-16 | F | Alive
157 | NA | 2015-06-16 | M | Alive
158 | NA | 2015-06-16 | M | Alive
159 | NA | 2015-06-16 | M | Alive
160 | NA | 2015-06-16 | M | Alive
162 | NA | 2015-06-16 | M | Alive

Upon execution, the `remove_duplicates()` function returns the input dataset without duplicated rows removed (if found). The details about the duplicates removal operation are stored in the report object that is attached to the output object. When duplicates are found, this report will contain the following element:

- removed_duplicates: A data frame with the duplicated rows that have been removed.

By examining these elements within the report, users gain insights into the specific duplicated rows, those that were removed, and the columns used to identify the duplicates, thus facilitating transparency and documentation of the duplicates removal process.

## Dictionary based data substituting

The `clean_using_dictionary()` function offers a convenient way to replace the options in a data frame or linelist with their corresponding values stored in a data dictionary. The function expects the following arguments:

1. data: The input data frame or linelist that contains the options to be replaced.
2. dictionary: The data dictionary in a form of a data frame that contains the complete labels for these options. The structure of this data dictionary file should adhere to the standards expected by the {matchmaker} package, as the `clean_using_dictionary()` function relies on functions from this package.

options | values | grp | orders
--- | --- | --- | ---
1 | male | sex | 1
2 | female | sex | 2

    # READING IN THE DATA
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))

    # PERFORM THE DICTIONARY-BASED SUBSTITUTION
    cleaned_data <- clean_using_dictionary(
      data = data,
      dictionary = dictionary
    )
    #> ! Cannot replace "-99" present in column sex 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.

    # PRINT THE REPORT FROM THIS OPERATION
    print_report(cleaned_data, "misspelled_values")

idx | column | value
--- | --- | ---
3 | sex | -99

The `add_to_dictionary()` function is a useful tool for expanding the coverage of a data dictionary by defining options that are present in the input data but not originally included in the dictionary. This function enables users to dynamically update the dictionary to accommodate new values encountered in the dataset. In addition to the current data dictionary the function takes the arguments defined below:

option, value, grp, order: the values for the options to be added in the data dictionary. The example below shows how this function is used.

By employing the `add_to_dictionary()` function, users can ensure that the data dictionary remains comprehensive and aligned with the evolving nature of the input dataset, thereby enhancing the accuracy and completeness of data interpretation and analysis. In the example below, we add `-99` to our test data dictionary, `test_dictionary`.

    # ADD THE EXTRA OPTION TO THE DICTIONARY
    dictionary <- add_to_dictionary(
      dictionary = dictionary,
      option = "-99",
      value = "unknow",
      grp = "sex",
      order = NULL
    )

options | values | grp | orders
--- | --- | --- | ---
1 | male | sex | 1
2 | female | sex | 2
-99 | unknow | sex | 3

    # PERFORM THE DICTIONARY-BASED SUBSTITUTION
    cleaned_data <- clean_using_dictionary(
      data = data,
      dictionary = dictionary
    )

study_id | event_name | country_code | country_name | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex
--- | --- | --- | --- | --- | --- | --- | ---
PS001P2 | day 0 | 2 | Gambia | 01/12/2020 | 06/01/1972 | Dec 01, 2020 | male
PS002P2 | day 0 | 2 | Gambia | 28/01/2021 | 02/20/1952 | Jan 01, 2021 | male
PS004P2-1 | day 0 | 2 | Gambia | 15/02/2021 | 06/15/1961 | Feb 11, 2021 | unknow
PS003P2 | day 0 | 2 | Gambia | 11/02/2021 | 11/11/1947 | Feb 01, 2021 | male
P0005P2 | day 0 | 2 | Gambia | 17/02/2021 | 09/26/2000 | Feb 16, 2021 | female
PS006P2 | day 0 | 2 | Gambia | 17/02/2021 | -99 | May 02, 2021 | female
PB500P2 | day 0 | 2 | Gambia | 28/02/2021 | 11/03/1989 | Feb 19, 2021 | male
PS008P2 | day 0 | 2 | Gambia | 22/02/2021 | 10/05/1976 | Sep 20, 2021 | female
PS010P2 | day 0 | 2 | Gambia | 02/03/2021 | 09/23/1991 | Feb 26, 2021 | male
PS011P2 | day 0 | 2 | Gambia | 05/03/2021 | 02/08/1991 | Mar 03, 2021 | female

## Correct misspelled values

The `add_to_dictionary()` function is not suitable in the presence of a large number of misspelled values. As such, users can use the `correct_misspelled_values()` function to fix misspelled values across multiple columns without needing/updating the data dictionary. The function takes the following arguments:

1. data: The input data frame or linelist
2. target_columns: A vector of target columns across which to perform the spelling checks
3. wordlist: A vector of characters with the words to match to the detected
   misspelled values
4. max_distance: An `integer` that represents the maximum distance allowed for detecting a spelling mistakes from the `wordlist`. The distance is the generalized Levenshtein edit distance. Default is `1`.
5. confirm: A logical used to determine whether to show the user a menu of spelling corrections. If `TRUE` and using R interactively then the user will have the option to review the proposed spelling corrections.

    df <- data.frame(
      case_type = c("confirmed", "confermed", "probable", "susspected"),
      outcome = c("died", "recoverd", "did", "recovered"),
      stringsAsFactors = FALSE
    )
    df

    correct_misspelled_values(
      data = df,
      target_columns = c("case_type", "outcome"),
      wordlist = c("confirmed", "probable", "suspected", "died", "recovered"),
      confirm = FALSE
    )

## Calculating time span in different time scales (“years”, “months”, “weeks”, or “days”)

The `timespan()` function computes the time span between two elements of type Date. The resulting time span can be expressed in “years”, “months”, “weeks”, or “days”, depending on the user-specified unit. The functions can take the following arguments:

1. data: The input dataset (required).
2. target_column: A string with the name of the target column (require). The values in this column are expected to be in the form of `Ymd` i.e. `2024-01-31`. The time span will be calculated between these values and the `end_date` defined below.
3. end_date: It can be either a character that is the name of another column of type Date from the input data or a vector of Date values or a single Date value (required). This should also be in the ISO8601 format (“2024-01-31”) and its default value is today’s date `Sys.Date()`.
4. span_unit: This parameter determines the unit in which the time span is expressed (required). It can be calculated in “years”, “months”, “weeks”, or “days”. By default, the time span is calculated in “years” if this parameter is not provided.
5. span_column_name: A string for the name of the column added to the input data. The default is `span`.
6. span_remainder_unit: A parameter used to determine the unit in which the remainder of the time span calculation will be returned. The possible units are: “days” or “weeks” or “months”. By default, the function returns decimal values i.e. `span_remainder_unit = NULL`.

With these arguments, the function offers flexibility in determining the time span in different units. It facilitates various analytics tasks where the time span computation is a necessary component, providing users with the ability to customize the output according to their specific requirements.

    # IMPORT DATA, REPLACE MISSING VALUES WITH 'NA' & STANDARDIZE DATES
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi")) %>%
      replace_missing_values(
        target_columns = "dateOfBirth",
        na_strings = "-99"
      ) %>%
      standardize_dates(
        target_columns = "dateOfBirth",
        error_tolerance = 0.0,
        format = "%m/%d/%Y" # nolint: nonportable_path_linter.
      )
    #> ℹ The target  column will be standardized using the format: "%m/%d/%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.

    # CALCULATE INDIVIDUAL AGE IN YEARS FROM THE 'dateOfBirth' COLUMN AND SEND THE
    # REMAINDER IN MONTHS
    cleaned_data <- timespan(
      data = data,
      target_column = "dateOfBirth",
      end_date = Sys.Date(),
      span_unit = "years",
      span_column_name = "age_in_years",
      span_remainder_unit = "months"
    )

    # CALCULATE THE TIME SPAN IN DAYS BETWEEN INDIVIDUALS DATE OF ADMISSION AND THE
    # DAY THEY TESTED POSITIVE
    data <- readRDS(system.file("extdata", "test_df.RDS", package = "cleanepi"))
    cleaned_data <- data %>%
      replace_missing_values(
        target_columns = "dateOfBirth",
        na_strings = "-99"
      ) %>%
      standardize_dates(
        target_columns = c("date_first_pcr_positive_test", "date.of.admission"),
        error_tolerance = 0.0,
        format = NULL
      ) %>%
      timespan(
        target_column = "date_first_pcr_positive_test",
        end_date = "date.of.admission",
        span_unit = "days",
        span_column_name = "elapsed_days",
        span_remainder_unit = NULL
      )
    #> ! 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.

The `timespan()` function augments the input dataset by adding one or two extra columns containing age-related information. These additional columns are as follows:

1. Calculated time span in the specified scale: Contains the calculated time span in the specified unit (“years”, “months”, “weeks”, or “days”).

2. Remainder from the time span calculation: Indicates the remaining number of “days” or “weeks” or “months” after calculating the time span, representing the fractional part of the time span calculation. This column is included if needed, and provides additional granularity in the time span representation.

study_id | event_name | country_code | country_name | date.of.admission | dateOfBirth | date_first_pcr_positive_test | sex | elapsed_days
--- | --- | --- | --- | --- | --- | --- | --- | ---
PS001P2 | day 0 | 2 | Gambia | 2020-12-01 | 06/01/1972 | 2020-12-01 | 1 | 0
PS002P2 | day 0 | 2 | Gambia | 2021-01-28 | 02/20/1952 | 2021-01-01 | 1 | 27
PS004P2-1 | day 0 | 2 | Gambia | 2021-02-15 | 06/15/1961 | 2021-02-11 | -99 | 4
PS003P2 | day 0 | 2 | Gambia | 2021-02-11 | 11/11/1947 | 2021-02-01 | 1 | 10
P0005P2 | day 0 | 2 | Gambia | 2021-02-17 | 09/26/2000 | 2021-02-16 | 2 | 1
PS006P2 | day 0 | 2 | Gambia | 2021-02-17 | NA | 2021-05-02 | 2 | -74
PB500P2 | day 0 | 2 | Gambia | 2021-02-28 | 11/03/1989 | 2021-02-19 | 1 | 9
PS008P2 | day 0 | 2 | Gambia | 2021-02-22 | 10/05/1976 | 2021-09-20 | 2 | -210
PS010P2 | day 0 | 2 | Gambia | 2021-03-02 | 09/23/1991 | 2021-02-26 | 1 | 4
PS011P2 | day 0 | 2 | Gambia | 2021-03-05 | 02/08/1991 | 2021-03-03 | 2 | 2