Package: mmaqshiny 1.0.0

Adithi R. Upadhya

mmaqshiny: Explore Air Quality Mobile-Monitoring Data

Mobile-monitoring or sensors on a mobile platform, is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualization of air-quality parameters helps detect localized areas of high air pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). 'mmaqshiny' is for analysing, visualizing and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, followed by a loading correction as suggested by Ban-Weiss et al., (2009) <doi:10.1021/es8021039>. For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file.

Authors:Adithi R. Upadhya [aut, cre, cph], Pratyush Agrawal [dtc], Sreekanth Vakacherla [aui, ctb], Meenakshi Kushwaha [aut, cph]

mmaqshiny_1.0.0.tar.gz
mmaqshiny_1.0.0.zip(r-4.5)mmaqshiny_1.0.0.zip(r-4.4)mmaqshiny_1.0.0.zip(r-4.3)
mmaqshiny_1.0.0.tgz(r-4.4-any)mmaqshiny_1.0.0.tgz(r-4.3-any)
mmaqshiny_1.0.0.tar.gz(r-4.5-noble)mmaqshiny_1.0.0.tar.gz(r-4.4-noble)
mmaqshiny_1.0.0.tgz(r-4.4-emscripten)mmaqshiny_1.0.0.tgz(r-4.3-emscripten)
mmaqshiny.pdf |mmaqshiny.html
mmaqshiny/json (API)

# Install 'mmaqshiny' in R:
install.packages('mmaqshiny', repos = c('https://meenakshi-kushwaha.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/meenakshi-kushwaha/mmaqshiny/issues

On CRAN:

3.70 score 5 stars 4 scripts 195 downloads 1 exports 132 dependencies

Last updated 2 years agofrom:3984c50aae. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winNOTENov 02 2024
R-4.5-linuxNOTENov 02 2024
R-4.4-winNOTENov 02 2024
R-4.4-macNOTENov 02 2024
R-4.3-winNOTENov 02 2024
R-4.3-macNOTENov 02 2024

Exports:mmaqshiny_run

Dependencies:askpassbackportsbase64encbitbit64bitopsblobbroombslibcachemCairocallrcaToolscellrangerclicliprcolorspacecommonmarkconflictedcpp11crayoncrosstalkcurldata.tableDBIdbplyrdigestdplyrDTdtplyrevaluatefansifarverfastmapfontawesomeforcatsfsgarglegenericsggplot2gluegoogledrivegooglesheets4gtablehavenhighrhmshtmltoolshtmlwidgetshttpuvhttridsisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleafletleaflet.providerslifecyclelubridatemagrittrMASSMatrixmemoisemgcvmimemodelrmunsellnlmeopensslpillarpkgconfigplotlypngprettyunitsprocessxprogresspromisespspurrrR6raggrappdirsrasterRColorBrewerRcppreadrreadxlrematchrematch2reprexrlangrmarkdownrstudioapirvestsassscalesselectrshinyshinyjssourcetoolsspstringistringrsyssystemfontsterratextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunXMLxml2xtablextsyamlzoo