Package: cleanepi 1.1.0.9000

Karim Mané

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

cleanepi_1.1.0.9000.tar.gz
cleanepi_1.1.0.9000.zip(r-4.7-any)cleanepi_1.1.0.9000.zip(r-4.6-any)cleanepi_1.1.0.9000.zip(r-4.5-any)
cleanepi_1.1.0.9000.tgz(r-4.6-any)cleanepi_1.1.0.9000.tgz(r-4.5-any)
cleanepi_1.1.0.9000.tar.gz(r-4.7-any)cleanepi_1.1.0.9000.tar.gz(r-4.6-any)
cleanepi_1.1.0.9000.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-connect.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

6.59 score 13 stars 50 scripts 476 downloads 20 exports 40 dependencies

Last updated from:c6a72fb041. Checks:9 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK205
source / vignettesOK169
linux-releaseOK167
macos-releaseOK112
macos-oldrelOK130
windows-develOK135
windows-releaseOK107
windows-oldrelOK111
wasm-releaseOK113

Exports:%>%add_to_dictionaryadd_to_reportcheck_date_sequencecheck_subject_idsclean_dataclean_using_dictionaryconvert_numeric_to_dateconvert_to_numericcorrect_subject_idsfind_duplicatesget_default_paramsprint_reportremove_constantsremove_duplicatesreplace_missing_valuesscan_datastandardize_column_namesstandardize_datestimespan

Dependencies:backportsbitbit64checkmateclicliprcpp11crayondplyrforcatsgenericsgluehmsjanitorlifecyclelinelistlubridatemagrittrmatchmakernumberizepillarpkgconfigprettyunitsprogresspurrrR6readrrlangsnakecasestringistringrtibbletidyrtidyselecttimechangetzdbutf8vctrsvroomwithr

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: 2025-03-14
Started: 2024-01-20

Introduction to cleanepi
An overview | General data cleaning tasks | 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 | Calculating time span in different time scales (“years”, “months”, “weeks”, or “days”) | Using {cleanepi} functionalities with pipe operators | Printing the report
Rendered fromcleanepi.Rmdusingknitr::rmarkdown

Last update: 2025-02-26
Started: 2023-03-07