Package: cleanepi 1.1.2.9000

Bubacarr Bah

cleanepi: Clean and Standardize Epidemiological Data

Cleaning and standardizing tabular data package, tailored specifically for curating epidemiological data. It streamlines various data cleaning tasks that are typically expected when working with datasets in epidemiology. It returns the processed data in the same format, and generates a comprehensive report detailing the outcomes of each cleaning task.

Authors:Karim Mané [aut], Thibaut Jombart [ctb], Abdoelnaser Degoot [aut], Bankolé Ahadzie [aut], Nuredin Mohammed [aut], Bubacarr Bah [aut, cre], Hugo Gruson [ctb, rev], Pratik R. Gupte [rev], James M. Azam [rev], Joshua W. Lambert [rev, ctb], Chris Hartgerink [rev], Andree Valle-Campos [rev, ctb], London School of Hygiene and Tropical Medicine, LSHTM [cph], data.org [fnd]

cleanepi_1.1.2.9000.tar.gz
cleanepi_1.1.2.9000.zip(r-4.7)cleanepi_1.1.2.9000.zip(r-4.6)cleanepi_1.1.2.9000.zip(r-4.5)
cleanepi_1.1.2.9000.tgz(r-4.6-any)cleanepi_1.1.2.9000.tgz(r-4.5-any)
cleanepi_1.1.2.9000.tar.gz(r-4.7-any)cleanepi_1.1.2.9000.tar.gz(r-4.6-any)
cleanepi_1.1.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cleanepi/json (API)

# Install 'cleanepi' in R:
install.packages('cleanepi', repos = c('https://epiverse-trace.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/epiverse-trace/cleanepi/issues

Pkgdown/docs site:https://epiverse-trace.github.io

Datasets:

On CRAN:

Conda:

data-cleaningepidemiologyepiverse

7.45 score 12 stars 49 scripts 413 downloads 21 exports 40 dependencies

Last updated from:6fb22d1aa5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK179
source / vignettesOK190
linux-release-x86_64OK168
macos-release-arm64OK99
macos-oldrel-arm64OK93
windows-develOK112
windows-releaseOK121
windows-oldrelOK119
wasm-releaseOK158

Exports:%>%add_to_dictionaryadd_to_reportcheck_date_sequencecheck_subject_idsclean_dataclean_using_dictionaryconvert_numeric_to_dateconvert_to_numericcorrect_misspelled_valuescorrect_subject_idsfind_duplicatesget_default_paramsprint_reportremove_constantsremove_duplicatesreplace_missing_valuesscan_datastandardize_column_namesstandardize_datestimespan

Dependencies:backportsbitbit64checkmateclicliprcpp11crayondplyrforcatsgenericsgluehmsjanitorlifecyclelinelistlubridatemagrittrmatchmakernumberizepillarpkgconfigprettyunitsprogresspurrrR6readrrlangsnakecasestringistringrtibbletidyrtidyselecttimechangetzdbutf8vctrsvroomwithr

Introduction to cleanepi
An overview | General data cleaning tasks | Using {cleanepi} functionalities with pipe operators | Printing the report | Specific data cleaning tasks | Remove constant data | Cleaning column names | Replacing missing entries with NA | Standardizing Dates | Standardizing subject IDs | Detecting incorrect, duplicated, and missing subject IDs | Correct wrong subject ids | Checking date sequence | Converting character columns into numeric | Converting numeric values into date | Finding duplicated rows | Removing duplicates | Dictionary based data substituting | Correct misspelled values | Calculating time span in different time scales (“years”, “months”, “weeks”, or “days”)
Rendered fromcleanepi.Rmdusingknitr::rmarkdown

Last update: 2025-07-15
Started: 2023-03-07

Package Design vignette for {cleanepi}
Concept and motivation | Design decisions | Scope | Input | Output | Modules in | 1. Standardization of column names | 2. Removal of empty rows and columns and constant columns | 3. Detection and removal of duplicates | 4. Replacement of missing values with NA | 5. Standardization of date values | 6. Standardization of subject IDs | 7. Dictionary based substitution | 8. Conversion of values when necessary | 9. Verification of the sequence of date-events | 10. Transformation of selected columns | Surrogate functions | Related packages | Dependencies | Contribute
Rendered fromdesign_principle.Rmdusingknitr::rmarkdown

Last update: 2025-07-08
Started: 2024-01-20

Readme and manuals

Help Manual

Help pageTopics
Add an element to the data dictionaryadd_to_dictionary
Add an element to the report objectadd_to_report
Checks whether the order in a sequence of date events is chronological. order.check_date_sequence
Check whether the subject IDs comply with the expected format. When incorrect IDs are found, the function sends a warning and the user can call the 'correct_subject_ids' function to correct them.check_subject_ids
Clean and standardize dataclean_data
Perform dictionary-based cleaningclean_using_dictionary
Common strings representing missing valuescommon_na_strings
Convert numeric to dateconvert_numeric_to_date
Convert columns into numericconvert_to_numeric
Correct misspelled values by using approximate string matching techniques to compare them against the expected values.correct_misspelled_values
Correct the wrong subject IDs based on the user-provided values.correct_subject_ids
Identify and return duplicated rows in a data frame or linelist.find_duplicates
Set and return 'clean_data' default parametersget_default_params
Generate report from data cleaning operationsprint_report
Remove constant data, including empty rows, empty columns, and columns with constant values.remove_constants
Remove duplicatesremove_duplicates
Replace missing values with 'NA'replace_missing_values
Scan through a data frame and return the proportion of 'missing', 'numeric', 'Date', 'character', 'logical' values.scan_data
Standardize column names of a data frame or line liststandardize_column_names
Standardize date variablesstandardize_dates
Calculate time span between datestimespan