dataquieR

minimal R version Pipeline Status Coverage CRAN-Version DOI CRAN-Downloads Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle license DOI DOI

The goal of dataquieR is to provide functions for assessing data quality issues in studies, that can be used alone or in a data quality pipeline. dataquieR also implements one generic pipeline producing flexdashboard based HTML5 reports.

See also

https://dataquality.qihs.uni-greifswald.de


Installation

You can install the released version of dataquieR from CRAN with:

install.packages("dataquieR")

The suggested packages can be directly installed by:

install.packages("dataquieR", dependencies = TRUE)

The developer version from GitLab.com can be installed using:

if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_gitlab("libreumg/dataquier")

For examples and additional documentation, please refer to our website.

Suggested packages

dataquieR reports can now use plotly if installed. That means that, in the final report, you can zoom in the figures and get information by hovering on the points, etc. To install plotly type:

install.packages("plotly")

To install all suggested packages, run:

prep_check_for_dataquieR_updates()

This command can also check for new beta releases of dataquieR from our own server, so not from CRAN:

prep_check_for_dataquieR_updates(beta = TRUE)

Hint If you are running dataquieR in an un-trusted setting, namely, inside a server application, please consider disabling the import of R-serialization files to prevent users from importing RData (or RDS or even R) files, that trigger code execution on your machine, see, e.g., Ivan Krylov’s blog for the reason:

# prevent rio from reading potentially code-containing files
options(rio.import.trust = FALSE)

If you do so, the example data won’t be loaded any more.

If you are using a version >= 2.0.0 of rio, this will be the default, so for running our examples, then, you’ll have to trust our files by using e.g. withr::with_options(list(rio.import.trust = FALSE), prep_get_data_frame("study_data")) for loading our example study data into the data-frame cache, initially and trusting our files loaded from

References

Funding – see also here