Supporting data and tools for "Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA"


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Created: Oct 08, 2021 at 7:31 p.m.
Last updated: Sep 21, 2022 at 3:42 p.m.
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Abstract

The files provided here are the supporting data and code files for the analyses presented in "Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA", an article accepted in JWRPM (https://ascelibrary.org/journal/jwrmd5). The journal paper assessed how differences water consumption are reflected in terms of timing and distribution of end uses across residential properties. The article provides insights into the variability of indoor and outdoor residential water use at the household level from the analysis of four to 23 weeks of 4-second resolution water use data at 31 single family residential properties. The data were collected in the cities of Logan and Providence, Utah, USA between 2019 and 2021. The 4-second resolution data is publicly available on: http://www.hydroshare.org/resource/0b72cddfc51c45b188e0e6cd8927227e. Standardized monthly values for single family residents in both cities were used int he article and are publicly available on: http://www.hydroshare.org/resource/16c2d60eb6c34d6b95e5d4dbbb4653ef. The code and data included in this resource allows replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
41.7471°
East Longitude
-111.7883°
South Latitude
41.6953°
West Longitude
-111.8521°

Temporal

Start Date:
End Date:

Content

readme.md

The resource is organized as follows:

  • WeatherData contains weather data for the USU Environmental Observatory and Evans farm weather stations (in CSV format). These files were directly downloaded from the USU Utah Climate center website (https://climate.usu.edu/). All metadata about the weather data (e.g., units, type of instrument, equations) is available in the USR UCC website in the following links:
    1. USU Environmental Observatory
    2. Evans Farm
  • runtime.csv, and install.R are files needed to set up the mybinder environment where the other script files. (DataHandler.R and DataAnalysis.R) can be reproduced.
  • urls.csv has links to all the data used in the study.
  • DataHandler.R downloads all the required data from the HydroShare resources cited.
  • DataAnalysis.R reproduces the results presented in the paper

Instructions for Reproducing Results

The code files were tested on a MacBook Pro with a 8-Core Intel Core i9 2.3 GHz Processor with 16 GB of RAM Running under: macOS Monterey 12.0.1. with a high speed (191 Mbps download and 377 Mbps upload) internet connection.

1. To reproduce any of the results on mybinder, do the following:

  1. Click here: Binder to open a mybinder executable environment (This can take a few minutes).
  2. Select R Studio from the launcher and run the scripts in the following order
    • DataHandler.R (this script will download all the required data from the HydroShare resources cited). This file ran in less than 10 minutes using internet connection specified above.
    • DataAnalysis.R (this script will reproduce the results presented in the paper, generating the figures included in the article (it generates Figures 2, 3, 4, 5, 6, 8, 9, and 10) and in Appendix B following a similar work secuence. Tables and numbers reported in the article are printed to the console. This file ran in less than 7 minutes.

2. To reproduce any of the results presented in the article in your local machine, do the following:

  1. Download all the files in the ReproducibleCode folder. Keep the files together in the same folder to ensure the paths to the files remain correct.
  2. Open the R scripts (https://cran.r-project.org/) using R or R-Studio (https://rstudio.com/). Install the following packages before running the code: tidyverse, lubridate, ggpattern, cowplot, gridExtra, viridis, scales, RColorBrewer, remotes, ggpattern.
  3. Execute them in the following order: 1) DataHandler.R and 2) DataAnalysis.R.
  4. DataHandler.R will download all the required data from the HydroShare resources cited. This file ran in less than 7 minutes using the machine and internet connection specified above.
  5. DataAnalysis.R will reproduce the results presented in the paper, generating the figures included in the article (it generates Figures 2, 3, 4, 5, 6, 7, 8, 9, and 10) and in Appendix B following a similar work secuence. Tables and numbers reported in the article are printed to the console. This file ran in less than 3 minutes using the machine specified above.

The code provided in this resource was developed using R version 4.0.0. The following R packages are required for running the provided scripts:

  • tidyverse - Version 1.3.0. A collection of R packages designed for data science.
  • lubridate - Version 1.7.8. Functions for working with dates/times.
  • ggpattern - Version 0.2.0. Custom ggplot2 geoms which support filled areas with geometric and image-based patterns.
  • cowplot - Version 0.4.5. Miscellaneous functions to help customize 'ggplot2' plots.
  • gridExtra - Version 0.4.5. User-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.
  • viridis - Version 0.5.1. A library that provides color maps for R.
  • HSClientR - Version 0.3.1.9000. an API wrapper for HydroShare. (https://hsclientr.justinsingh.me/)
  • scales - Version 1.2.0. Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.
  • RColorBrewer - Provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org.
  • remotes - Version 2.4.2. Download and install R packages stored in 'GitHub', 'GitLab', 'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git' repositories.
  • broom - Version 1.0.0. takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy tibbles.

Related Resources

This resource is referenced by Camilo J. Bastidas Pacheco , Jeffery S. Horsburgh, Nour A. Attallah, 2021. Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA
The content of this resource references Bastidas Pacheco, C. J., N. Atallah, J. S. Horsburgh (2021). High Resolution Residential Water Use Data in Cache County, Utah, USA, HydroShare, http://www.hydroshare.org/resource/0b72cddfc51c45b188e0e6cd8927227e
The content of this resource references Bastidas Pacheco, C. J., J. S. Horsburgh (2021). Standarized Monthly Water Use Data for Logan and Providence Cities., HydroShare, http://www.hydroshare.org/resource/16c2d60eb6c34d6b95e5d4dbbb4653ef
The content of this resource is derived from http://www.hydroshare.org/resource/aaa7246437144f2390411ef9f2f4ebd0

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Cyberinfrastructure for Intelligent Water Supply (CIWS): Shrinking Big Data for Sustainable Urban Water 1552444

How to Cite

Bastidas Pacheco, C. J., J. S. Horsburgh (2022). Supporting data and tools for "Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA", HydroShare, http://www.hydroshare.org/resource/379d9e7037f04478a99d5aec22e841e6

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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