{"id":27874191,"url":"https://github.com/fizzymo/beyondbackend.data-analysis-and-interpretation","last_synced_at":"2025-05-05T01:31:48.934Z","repository":{"id":258159914,"uuid":"839984548","full_name":"FizzyMo/BeyondBackend.Data-Analysis-and-Interpretation","owner":"FizzyMo","description":"Project focused on analyzing and interpreting news article data.","archived":false,"fork":false,"pushed_at":"2025-03-31T09:42:23.000Z","size":1692,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-31T10:35:55.828Z","etag":null,"topics":["bar-plot","csv-data","data-analytics","data-visualization","json-parsing","news-api","r","replit"],"latest_commit_sha":null,"homepage":"","language":"R","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/FizzyMo.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":"2024-08-08T18:12:22.000Z","updated_at":"2025-03-31T09:42:27.000Z","dependencies_parsed_at":"2024-10-18T06:34:45.998Z","dependency_job_id":null,"html_url":"https://github.com/FizzyMo/BeyondBackend.Data-Analysis-and-Interpretation","commit_stats":null,"previous_names":["fizzymo/beyondbackend.data-analysis-and-interpretation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FizzyMo%2FBeyondBackend.Data-Analysis-and-Interpretation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FizzyMo%2FBeyondBackend.Data-Analysis-and-Interpretation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FizzyMo%2FBeyondBackend.Data-Analysis-and-Interpretation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FizzyMo%2FBeyondBackend.Data-Analysis-and-Interpretation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/FizzyMo","download_url":"https://codeload.github.com/FizzyMo/BeyondBackend.Data-Analysis-and-Interpretation/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252423211,"owners_count":21745561,"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":["bar-plot","csv-data","data-analytics","data-visualization","json-parsing","news-api","r","replit"],"created_at":"2025-05-05T01:31:34.046Z","updated_at":"2025-05-05T01:31:48.911Z","avatar_url":"https://github.com/FizzyMo.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Header](https://github.com/user-attachments/assets/a039ce3a-a652-4d0a-be3e-b0ee4b55c83c)\n\n# News Article Data Analysis and Interpretation\nThe goal of this project is to analyze and interpret news article data using R. The project is divided into two components: data analysis and data interpretation.\n\n## Table of Contents\n\n- [Project Structure](#project-structure)\n- [Technology Used](#technology-used)\n- [Data Analysis](#data-analysis)\n  - [Reading JSON Data](#reading-json-data)\n  - [Cleaning and Transforming Data](#cleaning-and-transforming-data)\n  - [Analyzing Source Counts](#source-counts)\n  - [Visualizing Source Distribution](#visualizing-sources)\n- [Interpreting News Source Counts](#interpreting-news-source-counts)\n- [How to Run the Analysis](#how-to-run)\n- [Example Output](#example-output)\n- [Author](#author)\n- [Contact](#contact)\n\n## Project Structure\n\n```\nproject-root\n├── data\n│   ├── articles.json\n│   └── source_counts.csv\n├── video\n│   └── console.log.gif\n├── images\n│   └── analyzing.source.png\n│   └── clean_transform.data.png\n│   └── reading_data.png\n│   └── visual_source.png\n├── outputs\n│   └── Rplots.pdf\n├── main.r\n└── README.md\n```\n* `data/articles.json`: JSON file containing the raw news articles data.\n* `data/source_counts.csv`: CSV file with the count of articles from each source.\n* `video/console.log.gif`: GIF showing the console log of the R script execution.\n* `outputs/Rplots.pdf`: PDF file containing the bar plot visualizing the number of articles by source.\n* `main.r`: R script for data analysis and visualization.\n\n## Technology Used\n\n* **R:** For data analysis and visualization.\n* **Replit:** The development environment where the program was written and executed.\n\n## Data Analysis\n### Reading JSON Data\nThe analysis begins by reading the news articles data from a JSON file using the `jsonlite` package in R.\n\n![](images/reading.data.png)\n### Cleaning and Transforming Data\nThe articles are converted into a data frame, and initial exploratory data analysis is performed to check for missing values and understand the structure of the data.\n\n![](images/clean_transform.data.png)\n### Analyzing Source Counts\nThe source counts are calculated, and the data is saved to a CSV file for further analysis and interpretation.\n\n![](images/analyzing.source.png)\n\n### Visualizing Source Distribution\nA bar plot is generated to visualize the number of articles from each source. The plot is saved as a PDF in the `outputs` directory.\n\n![](images/visual.source.png)\n\n## Interpreting News Source Counts\nThe analysis provides insights into the distribution of news articles across different sources. By examining the `source_counts.csv` and the `Rplots.pdf`, you can interpret which sources contribute most heavily to the dataset and explore potential biases or trends.\n\n## How to Run the Analysis\n1. Clone the repository: `git clone https://github.com/yourusername/project-name.git`\n`cd project-name`\n2. Run the R script:\n`Rscript main.r`\n3. Review the output files:\n   - `data/source_counts.csv` for the article counts per source.\n   - `outputs/Rplots.pdf` bar plot visualizing the source counts.\n\n## Example Output\nExample console output\u003cbr\u003e\n![](video/console.log.gif)\n\n## Author\n**Carisa Saenz-Videtto**\n\n## Contact\ncarisasaenz@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffizzymo%2Fbeyondbackend.data-analysis-and-interpretation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffizzymo%2Fbeyondbackend.data-analysis-and-interpretation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffizzymo%2Fbeyondbackend.data-analysis-and-interpretation/lists"}