CovidWasteWatch
is an R package for analysis and visualization of
COVID-19 wastewater viral signal and variant frequency data. Using
user-provided pre-processed data, this package aims to provide users
with various statistical backgrounds a simple and efficient way to
observe trends in COVID-19 viral signal levels and variant frequencies
over time. CovidWasteWatch
was developed using
R version 4.3.1 (2023-06-16 ucrt)
,
Platform: x86_64-w64-mingw32/x64 (64-bit)
, and
Running under: Windows 11 x64 (build 22621)
.
To install the latest version of the package:
require("devtools")
devtools::install_github("viv-wang/CovidWasteWatch", build_vignettes = TRUE)
library("CovidWasteWatch")
To run the Shiny app:
runCovidWasteWatch()
To see an overview of the package:
ls("package:CovidWasteWatch")
data(package = "CovidWasteWatch")
browseVignettes("CovidWasteWatch")
CovidWasteWatch
provides 4 functions:
ViralSignal()
loads and extracts relevant data from a CSV file containing COVID-19 viral signal data over time, producing a statistical and graphical (line plot) overview of the data.VarBreakdown()
loads and extracts relevant data from a CSV file containing COVID-19 viral variant proportion data over time, producing a statistical and graphical (stacked bar plot) overview of the data.PlotViralSignal()
plots viral signal data from a given dataframe as a line plot, with dots as markers for each data point.PlotVarBreakdown()
plots viral variant breakdown data from a given dataframe as a stacked bar plot, where each stacked bar represents the variant breakdown at one time point.
For a tutorial demonstrating these functions, please see the vignettes. The package also contains two sample datasets, one for each COVID-19 data type, which are described in the data documentation and the introductory vignette.
An overview of CovidWasteWatch
is illustrated below.
The author of this package is Vivian Wang, who wrote the functions and
Shiny app implementation. The data overview functions ViralSignal()
and VarBreakdown()
useR.utils
to read the input CSV files, and
dplyr
to arrange the loaded datasets. The plotting functions
PlotViralSignal()
and PlotVarBreakdown()
utilize ggplot2
to create
graphical output from the processed data. runCovidWasteWatch
and
app.R
used Shiny
to provide an interactive app for this package.
Attali, D., Edwards, T. (2021). shinyalert: Easily Create Pretty Popup Messages (Modals) in ‘Shiny’. https://github.com/daattali/shinyalert
Bengtsson, H. (2022). R.utils: Various Programming Utilities. https://henrikbengtsson.github.io/R.utils/
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., Borges, B. (2023). shiny: Web Application Framework for R. https://github.com/rstudio/shiny, https://shiny.posit.co/
Grolemund, G. (2015). Learn Shiny - Video Tutorials. https://shiny.rstudio.com/tutorial/
Health Canada (2023). COVID-19 epidemiology update: Testing and variants. https://health-infobase.canada.ca/covid-19/testing-variants.html
Public Health Ontario (2023). COVID-19 Wastewater Surveillance in Ontario. https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/COVID-19-Data-Surveillance/Wastewater
R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Silva, A. (2022) TestingPackage: An Example R Package For BCB410H. https://github.com/anjalisilva/TestingPackage
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4. https://ggplot2.tidyverse.org
Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. https://dplyr.tidyverse.org
This package was developed as part of an assessment for 2023 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. CovidWasteWatch welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues. Many thanks to those who provided feedback to improve this package.