Clean and Standardize Epidemiological Data • cleanepi

cleanepi is an R package designed for cleaning, curating, and standardizing epidemiological data. It streamlines various data cleaning tasks that are typically expected when working with datasets in epidemiology.

Key functionalities of cleanepi include:

Removing irregularities: It removes duplicated and empty rows and columns, as well as columns with constant values.
Handling missing values: It replaces missing values with the standard NA format, ensuring consistency and ease of analysis.
Ensuring data integrity: It ensures the uniqueness of uniquely identified columns, thus maintaining data integrity and preventing duplicates.
Date conversion: It offers functionality to convert character columns to Date format under specific conditions, enhancing data uniformity and facilitating temporal analysis. It also offers conversion of numeric values written in letters into numbers.
Standardizing entries: It can standardize column entries into specified formats, promoting consistency across the dataset.
Time span calculation: It calculates the time span between two elements of type Date, providing valuable demographic insights for epidemiological analysis.

cleanepi operates on data frames or similar structures like tibbles, as well as linelist objects commonly used in epidemiological research. It returns the processed data in the same format, ensuring seamless integration into existing workflows. Additionally, it generates a comprehensive report detailing the outcomes of each cleaning task.

cleanepi is developed by the Epiverse-TRACE team at the Medical Research Council The Gambia unit at the London School of Hygiene and Tropical Medicine.

Installation

The latest development version of cleanepi can be installed from GitHub.

if (!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/cleanepi")
library(cleanepi)

Quick start

The main function in cleanepi is clean_data(), which internally makes call of almost all standard data cleaning functions, such as removal of empty and duplicated rows and columns, replacement of missing values, etc. However, each function can also be called independently to perform a specific task. This mechanism is explained in details in the vignette. Below is typical example of how to use the clean_data() function.

# READING IN 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

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

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

# DEFINING THE CLEANING PARAMETERS
use_na                  <- list(target_columns = NULL, na_strings = "-99")
remove_duplicates       <- list(target_columns   = NULL)
standardize_date        <- list(target_columns  = NULL,
                                error_tolerance = 0.4,
                                format          = NULL,
                                timeframe       = as.Date(c("1973-05-29",
                                                            "2023-05-29")),
                                modern_excel    = TRUE,
                                orders          = list(
                                  world_named_months = c("Ybd", "dby"),
                                  world_digit_months = c("dmy", "Ymd"),
                                  US_formats         = c("Omdy", "YOmd")
                                ))
standardize_subject_ids <- list(target_columns = "study_id",
                                prefix         = "PS",
                                suffix         = "P2",
                                range          = c(1, 100),
                                nchar          = 7)
standardize_col_names   <- list(keep = NULL, rename = NULL)
remove_cte              <- list(cutoff = 1)
standardize_col_names   <- list(keep   = "date.of.admission",
                                rename = "dateOfBirth = DOB")

params <- list(
  standardize_column_names = standardize_col_names,
  remove_constant          = remove_cte,
  replace_missing_values   = use_na, 
  remove_duplicates        = remove_duplicates,
  standardize_date         = standardize_date,
  standardize_subject_ids  = standardize_subject_ids,
  to_numeric               = "sex",
  dictionary               = test_dictionary
)

# PERFORMING THE DATA CLEANING
cleaned_data <- clean_data(
  data   = test_data,
  params = params
)
#> 
#> cleaning column names
#> replacing missing values with NA
#> removing the constant columns, empty rows and columns
#> removing duplicated rows
#> standardising date columns
#> checking subject IDs format
#> Warning: Detected incorrect subject ids at lines: 5, 7, 3
#> Use the correct_subject_ids() function to adjust them.
#> 
#> converting sex into numeric
#> performing dictionary-based cleaning

study_id	date.of.admission	DOB	date_first_pcr_positive_test	sex
PS001P2	2020-12-01	06/01/1972	2020-12-01	male
PS002P2	2021-01-28	02/20/1952	2021-01-01	male
PS004P2-1	2021-02-15	06/15/1961	2021-02-11	NA
PS003P2	2021-02-11	11/11/1947	2021-02-01	male
P0005P2	2021-02-17	09/26/2000	2021-02-16	female
PS006P2	2021-02-17	NA	2021-05-02	female
PB500P2	2021-02-28	11/03/1989	2021-02-19	male
PS008P2	2021-02-22	10/05/1976	2021-09-20	female
PS010P2	2021-03-02	09/23/1991	2021-02-26	male
PS011P2	2021-03-05	02/08/1991	2021-03-03	female

# EXTRACT THE DATA CLEANING REPORT
report <- attr(cleaned_data, "report")

# DISPLAY THE DATA CLEANING REPORT
print_report(report)

Vignette

browseVignettes("cleanepi")

Lifecycle

This package is currently an experimental, as defined by the RECON software lifecycle. This means that it is functional, but interfaces and functionalities may change over time, testing and documentation may be lacking.

Contributions

Contributions are welcome via pull requests.

Code of Conduct

Please note that the cleanepi project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Citing this package

citation("cleanepi")
#> To cite package 'cleanepi' in publications use:
#> 
#>   Mané K, Bah B, Ahadzie B, Mohammed N, Degoot A (2024). _cleanepi:
#>   Clean and Standardize Epidemiological Data_.
#>   <https://epiverse-trace.github.io/cleanepi,https://github.com/epiverse-trace/cleanepi>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {cleanepi: Clean and Standardize Epidemiological Data},
#>     author = {Karim Mané and Bubacarr Bah and Bankolé Ahadzie and Nuredin Mohammed and Abdoelnaser Degoot},
#>     year = {2024},
#>     url = {https://epiverse-trace.github.io/cleanepi,
#> https://github.com/epiverse-trace/cleanepi},
#>   }

cleanepi: Clean and standardize epidemiological data