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
• Technology Used
• Data Analysis
  • Reading JSON Data
  • Cleaning and Transforming Data
  • Analyzing Source Counts
  • Visualizing Source Distribution
• Interpreting News Source Counts
• How to Run the Analysis
• Example Output
• Author
• 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