dataquieR
This page provides introductory texts on the creation of data quality reports using R.
As shown in the picture below, creating reports requires, the appropriate setup of study data and metadata. An example is provided within the advanced report section.
The first option is to use the dq_report()
function
which requires only two objects to create a default report:
dq_report(study_data, meta_data)
In addition to this command only a few further lines of code are necessary to load the respective data into the working environment. The illustration below shows how sparse a respective R file can be (left upper panel of R-Studio):
You can open a reduced example report generated by
dq_report()
here
The code shown in the illustration is given here:
# --------------------------------------------------------------------------------------------------
# D A T A Q U A L I T Y I N E P I D E M I O L O G I C A L R E S E R A C H
#
# == dataquieR
#
# dq_report() eases the generation of data quality reports as it calls automatically functions of
# dataquieR
#
# Installation/Further Information -----------------------------------------------------------------
#
# Please see our website:
# https://dataquality.ship-med.uni-greifswald.de/
#
# install from CRAN using
install.packages("dataquieR")
# Alternatively, you may install the development version as described
# on https://dataquality.ship-med.uni-greifswald.de/DownloadR.html
# load the package
library(dataquieR)
# data ---------------------------------------------------------------------------------------------
# Study of Health in Pomerania
sd1 <- readRDS(system.file("extdata", "ship.RDS", package = "dataquieR"))
summary(sd1)
# Metadata
md1 <- readRDS(system.file("extdata", "ship_meta.RDS", package = "dataquieR"))
# dq_report() - a crude approach -------------------------------------------------------------------
my_dq_report <- dq_report(study_data = sd1,
meta_data = md1,
label_col = LABEL)
# check the results
my_dq_report
Of course, the function dq_report()
can manage further
arguments and settings. However, to gain insides into the data this
sparse version is a good start and may serve as the fundament to tailor
more specific reports.