Validating your data in R

confirming the quality of your datasets

Quality work meme: https://giphy.com/gifs/moodman-quality-nice-work-VhWVAa7rUtT3xKX6Cd

Data validation is a crucial aspect of any data analysis process. It involves checking for errors, inconsistencies, and other issues that may compromise the quality of the data. There are various packages available for data validation in R.

One of the packages that stands out is {pointblank}, which provides a flexible and straightforward toolkit for data quality assessment in R. It comes with a range of built-in validators that can be used to check for different types of errors and inconsistencies in data, including missing values, outliers, and invalid data types.

GitHub - rich-iannone/pointblank: Data quality assessment and metadata reporting for data frames…

What’s nice about pointblank is that it can be easily integrated into existing data analysis workflows, allowing users to automate the data validation process. Another feature that sets it apart from other R validation packages is its ability to produce detailed reports of data validation results. The reports can provide useful information on the validation rules that were applied, the data that failed validation, and reasons for the failure.

Here’s a short example of data validation with pointblank applied on a COVID-19 testing dataset from the {medicaldata} R package. The Github repo link is included below the Carbon code image. Feel free to run it on your own and tinker with the code. Note that the table scan will take a few minutes to run.

https://github.com/parmsam/covid-19-data-validation/blob/main/pointblank-validation.R

In conclusion, pointblank provides a flexible and straightforward solution for data validation and can be easily integrated into existing data analysis workflows. Moreover, the package produces detailed reports of data validation results, making it easy for users to identify and fix errors and inconsistencies in their data. Whether you are a data analyst or a researcher, pointblank is a valuable tool to have in your toolkit for data quality assessment in R. I encourage you to visit the github repo for the package and official pkgdown site learn more.

Other packages

There are also other packages that can be used for data validation which are also worth checking out:

GitHub - data-cleaning/validate: Professional data validation for the R environment

Automatic Data Validation and Reporting

GitHub - ropensci/assertr: Assertive programming for R analysis pipelines