{"id":20630769,"url":"https://github.com/cluebbers/using_r_for_hpda","last_synced_at":"2025-08-10T04:16:44.808Z","repository":{"id":239907465,"uuid":"800955603","full_name":"cluebbers/Using_R_for_HPDA","owner":"cluebbers","description":"Exploring R for high-performance data analytics, including memory management, GPU computing, parallel processing, benchmarks, case studies, and comparisons with Python.","archived":false,"fork":false,"pushed_at":"2024-05-17T12:48:40.000Z","size":960,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-17T07:08:54.433Z","etag":null,"topics":["benchmarking","case-studies","data-science","gpu-computing","high-performance-data-analytics","memory-management","parallel-processing","python-comparison","r"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cluebbers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2024-05-15T10:17:43.000Z","updated_at":"2024-05-17T12:48:43.000Z","dependencies_parsed_at":"2024-05-17T13:52:51.879Z","dependency_job_id":"9a91bbad-0101-48d1-970f-665e9dd5f2f9","html_url":"https://github.com/cluebbers/Using_R_for_HPDA","commit_stats":null,"previous_names":["cluebbers/r_for_hpda","cluebbers/using_r_for_hpda"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cluebbers%2FUsing_R_for_HPDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cluebbers%2FUsing_R_for_HPDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cluebbers%2FUsing_R_for_HPDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cluebbers%2FUsing_R_for_HPDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cluebbers","download_url":"https://codeload.github.com/cluebbers/Using_R_for_HPDA/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242588421,"owners_count":20154203,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["benchmarking","case-studies","data-science","gpu-computing","high-performance-data-analytics","memory-management","parallel-processing","python-comparison","r"],"created_at":"2024-11-16T14:09:29.450Z","updated_at":"2025-03-08T17:56:26.515Z","avatar_url":"https://github.com/cluebbers.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Using R for High-Performance Data Analytics\n\n## Overview\n\nThis repository contains the seminar report and associated materials for the course \"Newest Trends in High-Performance Data Analytics\" at Georg-August-Universität Göttingen.\nThe report investigates the use of R in high-performance data analytics (HPDA), focusing on memory management, GPU computing, parallel processing, and benchmarking.\n\n## Repository Structure\n\n```\n├── README.md\n├── 2024-03-25_R_HPDA_Luebbers.pdf      # Detailed insights into leveraging R for high-performance data analytics\n├── NTHPDA.Rmd                          # R notebook containing example code and benchmarks\n```\n## Report Highlights\n\n- **Memory Management**: Techniques to optimize R's memory usage for handling large datasets.\n- **GPU Computing**: Utilizing GPU for accelerated computations with R packages.\n- **Parallel Processing**: Methods to perform parallel computations to speed up data processing tasks.\n- **Benchmarking**: Evaluating the performance of various R functions and comparing them with Python.\n- **Leveraging C++**: Enhancing R's performance by integrating C++ code.\n- **Computational Biology**: Using R for high-performance data analysis in genomics and bioinformatics.\n- **Comparative Analysis**: Evaluating R's performance against Python for various data processing tasks.\n\n## Code\n\nTo run the example scripts, you need to have R installed on your system along with the necessary packages.\nYou can install the required packages using the following commands:\n\n\n1. **Install the required packages**\n\n```R\ninstall.packages(c(\"forcats\", \"readr\", \"dplyr\", \"tidyr\", \"ggplot2\", \"tibble\", \"devtools\"))\n```\n\n2. **Download the data**\n\nThe data can be found here: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/natality/Nat2018us.zip\n\n3. **Knit the R Markdown file**\n\nOpen the `NTHPDA.Rmd` file in RStudio and click the \"Knit\" button to generate the HTML report. \nAlternatively, you can use the following command in your R console:\n\n```R\nrmarkdown::render(\"NTHPDA.Rmd\")\n```\n\n## Future Work\n\nMaking gpuR work :)\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Contact\nFor any questions or feedback, please contact Christopher L. Lübbers at c.luebbers@stud.uni-goettingen.de.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcluebbers%2Fusing_r_for_hpda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcluebbers%2Fusing_r_for_hpda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcluebbers%2Fusing_r_for_hpda/lists"}