cleanepi: Clean and standardize epidemiological data

Installation

install.packages("cleanepi")

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

Quick start

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

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

# SCAN THROUGH THE DATAscan_res<-cleanepi::scan_data(test_data)

# DEFINING THE CLEANING PARAMETERSreplace_missing_values<-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")),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_constants<-list(cutoff=1)standardize_column_names<-list(keep="date.of.admission",rename= c(DOB="dateOfBirth"))to_numeric<-list(target_columns="sex",lang="en")

# PERFORMING THE DATA CLEANINGcleaned_data<- clean_data(data=test_data,standardize_column_names=standardize_column_names,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=test_dictionary,check_date_sequence=NULL)#> 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

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

# DISPLAY THE DATA CLEANING REPORTprint_report(cleaned_data,print=TRUE)

Vignette

browseVignettes("cleanepi")

Lifecycle

Contributions

Code of Conduct

Citing this package

citation("cleanepi")#> To cite package 'cleanepi' in publications use:#>#>   Mané K, Degoot A, Ahadzie B, Mohammed N, Bah B (2025). _cleanepi:#>   Clean and Standardize Epidemiological Data_.#>   doi:10.5281/zenodo.11473985#>   <https://doi.org/10.5281/zenodo.11473985>,#>   <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 Abdoelnaser Degoot and Bankolé Ahadzie and Nuredin Mohammed and Bubacarr Bah},#>     year = {2025},#>     doi = {10.5281/zenodo.11473985},#>     url = {https://epiverse-trace.github.io/cleanepi/},#>   }