https://github.com/fizzymo/beyondbackend.data-analysis-and-interpretation
Project focused on analyzing and interpreting news article data.
https://github.com/fizzymo/beyondbackend.data-analysis-and-interpretation
bar-plot csv-data data-analytics data-visualization json-parsing news-api r replit
Last synced: 5 months ago
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Project focused on analyzing and interpreting news article data.
- Host: GitHub
- URL: https://github.com/fizzymo/beyondbackend.data-analysis-and-interpretation
- Owner: FizzyMo
- License: mit
- Created: 2024-08-08T18:12:22.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-31T09:42:23.000Z (7 months ago)
- Last Synced: 2025-03-31T10:35:55.828Z (7 months ago)
- Topics: bar-plot, csv-data, data-analytics, data-visualization, json-parsing, news-api, r, replit
- Language: R
- Homepage:
- Size: 1.61 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# News Article Data Analysis and Interpretation
The 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.
## Table of Contents
- [Project Structure](#project-structure)
- [Technology Used](#technology-used)
- [Data Analysis](#data-analysis)
- [Reading JSON Data](#reading-json-data)
- [Cleaning and Transforming Data](#cleaning-and-transforming-data)
- [Analyzing Source Counts](#source-counts)
- [Visualizing Source Distribution](#visualizing-sources)
- [Interpreting News Source Counts](#interpreting-news-source-counts)
- [How to Run the Analysis](#how-to-run)
- [Example Output](#example-output)
- [Author](#author)
- [Contact](#contact)
## Project Structure
```
project-root
├── data
│ ├── articles.json
│ └── source_counts.csv
├── video
│ └── console.log.gif
├── images
│ └── analyzing.source.png
│ └── clean_transform.data.png
│ └── reading_data.png
│ └── visual_source.png
├── outputs
│ └── Rplots.pdf
├── main.r
└── README.md
```
* `data/articles.json`: JSON file containing the raw news articles data.
* `data/source_counts.csv`: CSV file with the count of articles from each source.
* `video/console.log.gif`: GIF showing the console log of the R script execution.
* `outputs/Rplots.pdf`: PDF file containing the bar plot visualizing the number of articles by source.
* `main.r`: R script for data analysis and visualization.
## Technology Used
* **R:** For data analysis and visualization.
* **Replit:** The development environment where the program was written and executed.
## Data Analysis
### Reading JSON Data
The analysis begins by reading the news articles data from a JSON file using the `jsonlite` package in R.
### Cleaning and Transforming Data
The 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.
### Analyzing Source Counts
The source counts are calculated, and the data is saved to a CSV file for further analysis and interpretation.
### Visualizing Source Distribution
A bar plot is generated to visualize the number of articles from each source. The plot is saved as a PDF in the `outputs` directory.
## Interpreting News Source Counts
The 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.
## How to Run the Analysis
1. Clone the repository: `git clone https://github.com/yourusername/project-name.git`
`cd project-name`
2. Run the R script:
`Rscript main.r`
3. Review the output files:
- `data/source_counts.csv` for the article counts per source.
- `outputs/Rplots.pdf` bar plot visualizing the source counts.
## Example Output
Example console output
## Author
**Carisa Saenz-Videtto**
## Contact
carisasaenz@gmail.com