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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.
cleanepi can be installed from CRAN using
install.packages("cleanepi")The latest development version of cleanepi can be installed from GitHub.
if (!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/cleanepi")
library(cleanepi)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_dates <- 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)
remove_cte <- list(cutoff = 1)
standardize_col_names <- list(keep = "date.of.admission",
rename = c(DOB = "dateOfBirth"))
to_numeric <- list(target_columns = "sex",
lang = "en")
params <- list(
standardize_column_names = standardize_col_names,
remove_constants = remove_cte,
replace_missing_values = use_na,
remove_duplicates = remove_duplicates,
standardize_dates = standardize_dates,
standardize_subject_ids = standardize_subject_ids,
to_numeric = to_numeric,
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: 3, 5, 7
#> Use the correct_subject_ids() function to adjust them.
#> converting sex en 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)browseVignettes("cleanepi")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 are welcome via pull requests.
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.
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_.
#> doi:10.5281/zenodo.6532786 <https://doi.org/10.5281/zenodo.6532786>,
#> <https://epiverse-trace.github.io/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},
#> doi = {10.5281/zenodo.6532786},
#> url = {https://epiverse-trace.github.io/cleanepi/},
#> }These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.