The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
PERK is an R package containing a tool (Shiny App) to
predict and visualize environmental concentration and risk using
pharmaceutical prescription data collected at fine spatial
resolution.
The tool helps users to input their measured concentration, to compare the predicted (PEC) and measured concentrations (MEC) of the pharmaceuticals by means of the PEC/MEC ratio, to establish whether the predicted equations used tend to underestimate or overestimate measured values.
It provides a consistent interactive user interface in a familiar dashboard layout, enabling users to visualise predicted values and compare with their measured values without any hassles. Users can download data and graphs generated using the tool in .csv or publication ready images.
Although the main purpose of the R package is to be installed and used locally, you can give it a quick online test run to determine whether it’s right for you before installing it. With just 3 commands, you can install it and begin using it if you like what you see:
install.packages('PERK')
library('PERK')
run_app()For an introduction to the package, step-by-step instructions on getting started, and more information on the different ways you can use the package PERK-Walkthrough tutorial.
You can install the development version of PERK from
GitHub through following command.
install.packages("remotes")
library(remotes)
install_github("jkkishore85/PERK")Please cite this article from this Preprint if the package helps you with your work in any way that justifies it.
This work is a part of the Wastewater Fingerprinting for Public Health Assessment (ENTRUST) project funded by Wessex Water and EPSRC IAA (grant no. EP/R51164X/1).
We accept no liability for any errors in the data or its publication here. Use this data at your own risk.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.