%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname recipes %global packver 1.1.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.1.0 Release: 1%{?dist}%{?buildtag} Summary: Preprocessing and Feature Engineering Steps for Modeling License: MIT + file LICENSE URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.6 Requires: R-core >= 3.6 BuildArch: noarch BuildRequires: R-CRAN-lubridate >= 1.8.0 BuildRequires: R-CRAN-hardhat >= 1.4.0 BuildRequires: R-CRAN-tidyselect >= 1.2.0 BuildRequires: R-CRAN-dplyr >= 1.1.0 BuildRequires: R-CRAN-rlang >= 1.1.0 BuildRequires: R-CRAN-lifecycle >= 1.0.3 BuildRequires: R-CRAN-purrr >= 1.0.0 BuildRequires: R-CRAN-tidyr >= 1.0.0 BuildRequires: R-CRAN-ipred >= 0.9.12 BuildRequires: R-CRAN-clock >= 0.6.1 BuildRequires: R-CRAN-vctrs >= 0.5.0 BuildRequires: R-CRAN-generics >= 0.1.2 BuildRequires: R-CRAN-cli BuildRequires: R-CRAN-glue BuildRequires: R-CRAN-gower BuildRequires: R-CRAN-magrittr BuildRequires: R-CRAN-Matrix BuildRequires: R-stats BuildRequires: R-CRAN-tibble BuildRequires: R-CRAN-timeDate BuildRequires: R-utils BuildRequires: R-CRAN-withr Requires: R-CRAN-lubridate >= 1.8.0 Requires: R-CRAN-hardhat >= 1.4.0 Requires: R-CRAN-tidyselect >= 1.2.0 Requires: R-CRAN-dplyr >= 1.1.0 Requires: R-CRAN-rlang >= 1.1.0 Requires: R-CRAN-lifecycle >= 1.0.3 Requires: R-CRAN-purrr >= 1.0.0 Requires: R-CRAN-tidyr >= 1.0.0 Requires: R-CRAN-ipred >= 0.9.12 Requires: R-CRAN-clock >= 0.6.1 Requires: R-CRAN-vctrs >= 0.5.0 Requires: R-CRAN-generics >= 0.1.2 Requires: R-CRAN-cli Requires: R-CRAN-glue Requires: R-CRAN-gower Requires: R-CRAN-magrittr Requires: R-CRAN-Matrix Requires: R-stats Requires: R-CRAN-tibble Requires: R-CRAN-timeDate Requires: R-utils Requires: R-CRAN-withr %description A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models. %prep %setup -q -c -n %{packname} # fix end of executable files find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; # prevent binary stripping [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true [ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \ sed -i 's@-g0@@g' {} \; || true # don't allow local prefix in executable scripts find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \; %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 # remove buildroot from installed files find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}