{"id":31766136,"url":"https://github.com/ibmdecisionoptimization/doopl-r-sample","last_synced_at":"2026-02-15T08:33:25.570Z","repository":{"id":113738895,"uuid":"154680023","full_name":"IBMDecisionOptimization/doopl-R-sample","owner":"IBMDecisionOptimization","description":"Example showing how to use OPL from R.","archived":false,"fork":false,"pushed_at":"2018-10-25T13:58:48.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-10-10T00:31:18.647Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"AMPL","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/IBMDecisionOptimization.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md/LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-10-25T13:53:58.000Z","updated_at":"2018-10-25T13:58:50.000Z","dependencies_parsed_at":"2023-07-01T18:45:39.145Z","dependency_job_id":null,"html_url":"https://github.com/IBMDecisionOptimization/doopl-R-sample","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/IBMDecisionOptimization/doopl-R-sample","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBMDecisionOptimization%2Fdoopl-R-sample","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBMDecisionOptimization%2Fdoopl-R-sample/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBMDecisionOptimization%2Fdoopl-R-sample/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBMDecisionOptimization%2Fdoopl-R-sample/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IBMDecisionOptimization","download_url":"https://codeload.github.com/IBMDecisionOptimization/doopl-R-sample/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBMDecisionOptimization%2Fdoopl-R-sample/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29473718,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-15T06:58:05.414Z","status":"ssl_error","status_checked_at":"2026-02-15T06:58:05.085Z","response_time":118,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-10-10T00:29:07.831Z","updated_at":"2026-02-15T08:33:25.565Z","avatar_url":"https://github.com/IBMDecisionOptimization.png","language":"AMPL","funding_links":[],"categories":[],"sub_categories":[],"readme":"# doopl-R\n\nThis is an example on how to use OPL with the R framework and the reticulate library (open source library).\n\nIt shows how to run an OPL model with R dataframes as inputs, and R dataframes as solution outputs.\nIn this example, we use SQLite to retrieve data from a database.\n\nTo make it work, you need:\n   * R runtime\n   * a Python interpreter (2.7, 3.5 or 3.6)\n   * doopl library installed in your Python environment (https://pypi.org/project/doopl/)\n   * CPLEX Studio 128 runtime (Windows, Linux or Mac are the only supported platforms of doopl)\n   \nFirst, install doopl library in your Python interpreter: 'pip install doopl'. You alternatively download it and put it in your PYTHONPATH).\n\nSecond, set the environment path variable to point to the OPL runtime:\n   * on Mac OS, it will be something like DYLD_LIBRARY_PATH=/Applications/CPLEX_Studio128/opl/bin/x86-64_osx\n   * on Linux, it will be LD_LIBRARY_PATH\n   * on Windows, it will be PATH.\n   \nNow, you can run R and the carseq.r script.\n\nAll the doopl functionalities are available in R. You can find the doopl examples here: https://github.com/IBMDecisionOptimization/doopl-examples/tree/master/examples\n\nYou can access the reticulate documentation here: https://rstudio.github.io/reticulate/articles/calling_python.html where you will find all the R-Python conversions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibmdecisionoptimization%2Fdoopl-r-sample","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fibmdecisionoptimization%2Fdoopl-r-sample","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibmdecisionoptimization%2Fdoopl-r-sample/lists"}