This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Thursday, July 6 • 5:50pm - 5:55pm
Using an R package as platform for harmonized cleaning of data from RTI MicroPEM air quality sensors

Sign up or log in to save this to your schedule and see who's attending!

Feedback form is now closed.
Keywords: package, reproducibility, science, quality control, personal monitoring
Webpages: http://www.masalmon.eu/rtimicropem/
RTI MicroPEM is a small particulate matter personal exposure monitor, increasingly used in developed and developing countries. Each measurement session produces a csv file which includes a header with information on instrument settings and a table of thousands of observations of time-varying variables such as particulate matter concentration, relative humidity. Files need to be processed for 1) generating a format suitable for further analysis and 2) cleaning the data to deal with the instruments shortcomings. Currently, this is not done in a harmonized and transparent way. Our package pre-processes the data and converts them into a format that allows the integration the rich set of data manipulation and visualization functionalities that the tidyverse provides.
We made our software open-source for better reproducibility, easier involvement of new contributors and free use, particularly in developing countries. We applied the package in a research project for a large number of measurements. The functionalities of our package are three-fold: allowing conversion of files, empowering easy data quality checks, and supporting reproducible data cleaning through documentation of current workflows.
For inspection of individual files, the package has a R6 class where each object represents one MicroPEM file, with summary and plot methods including interactivity thanks to rbokeh. The package also contains a Shiny app for exploration by non-experienced R users. The Shiny app includes a tab with tuneable alarms, e.g. “Nephelometer slope was not 3” which empowered rapid checks after a day on the field. For later stages of a study after a bunch of files has been collected, the package supports the creation of a measurements and a settings data.frames from all files in a directory. We exemplify data cleaning processes, in particular the framework used for the CHAI project, in a vignette, in a transparency effort.
The package is currently available on Github. Since air pollution sensors that would output csvy (csv file with yaml frontmatter) instead of weird csv; and produce ready-to-use data are currently unavailable, rtimicropem can be an example of how to use an R package as a central place for best practices, thus fostering reproducibility and harmonization of data cleaning across studies. We also hope it can trigger more use of R in the fields of epidemiology and exposure science.


Thursday July 6, 2017 5:50pm - 5:55pm
2.01 Wild Gallery

Attendees (38)