Data cleaning for statistical purpose

validate

Professional data validation for the R environment

useR2019_tutorial

Tutorial for useR2019

validatedb

Validate on a table in a DB, using dbplyr

errorlocate

Find and replace erroneous fields in data using validation rules

editrules

R package for handling, checking and enforcing data rules

validatetools

book

Resources for Statistical Data Cleaning with Applications in R

deductive

Methods for deductive data correction and imputation

dcmodify

Modify data records using separately defined modification rules

uRos2018_tutorial

Data-Cleaning tutorial for Use of R in Official Statistics

useR2021_tutorial

Materials for the useR!2021 tutorial on data validation

ISM2020_tutorial

Data Cleaning for Official Statistics

ValidatPoC

Rules and data for the PoC of the ESSnet on Validation

ValidatReport

Standard validation report structure for the ESS

dcmodifydb

Deterministic, documented correction rules on a database

validatesuggest

Generate validation rules from data

validatereport

Create attractive validation reports, export validation results to ESS json reporting standard

dcmodifydt

dcmodify on a data.table

dirtyharry

Make your data dirty

IW2024

Course material for the International Week

lintools

Tools for manipulating systems of linear (in)equalities

EESW2019_tutorial

Materials for the short course at the European Establishment Statistics Workshop 2019

drat

Beta versions of data-cleaning packages

Madrid2019

Slides and exercises for Mark's visit to the Complutense University of Madrid and INE

uRos2019_tutorial

Tutorial materials for uRos 2019

datacleaning

Load and install multiple R packages for data cleaning

deriverules

graphics

Some graphics and logos to use in presentations

useR2020_tutorial

Materials for the useR2020 tutorial on Statistical Data Cleaning with R

useR2022_tutorial

Material for the proposed tutorial for useR!2022