File R-mmaqshiny.spec of Package R-mmaqshiny
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%global packname mmaqshiny
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 1.0.0
Release: 0
Summary: Explore Air-Quality Mobile-Monitoring Data
Group: Development/Libraries/Other
License: MIT + file LICENSE
URL: http://cran.r-project.org/web/packages/%{packname}
Source: mmaqshiny_1.0.0.tar.gz
Requires: R-base
Requires: R-htmltools
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# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse}
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Suggests: R-testthat
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Suggests: R-shinytest
%description
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 visualisation 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, visualising
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.
%prep
%setup -q -c -n %{packname}
# the next line is needed, because we build without --clean in between two packages
rm -rf ~/.R
%build
%install
mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
%fdupes -s %{buildroot}%{rlibdir}
#%%check
#%%{_bindir}/R CMD check %%{packname}
%files
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/images
%{rlibdir}/%{packname}/shiny
%changelog
