Report: Challenges and Usability of Scraped Data
1. Difficulties Encountered in the Scraping Process
The process of scraping foreign exchange reserve data from Wikipedia using rvest was successful, but it presented several challenges:

Duplicated Columns:

The scraped table contained multiple columns with identical or similar names (e.g., UN Geoscheme, Forex reserves excluding gold).
This created ambiguity in selecting the correct columns for analysis.
Formatting Issues:

The data in the reserves column was formatted as strings with commas, requiring preprocessing to convert it to numeric format for visualization.
Some rows contained missing or non-numeric values, which had to be filtered out.
HTML Table Structure:

The structure of the HTML table on the Wikipedia page included nested headers, which contributed to duplicated column names and required additional inspection to correctly identify and clean the data.
Dynamic Nature of Web Content:

The table's structure or contents could change if Wikipedia is updated, potentially breaking the script in the future.
2. Usability of the Scraped Data
Despite the challenges, the data is highly usable for analysis after cleaning:

Strengths:

The scraped table provides comprehensive information, including country names, regional classifications, and reserve amounts.
The data is in a tabular format, making it straightforward to process and visualize after cleaning.
Limitations:

The presence of duplicated and non-numeric columns required significant preprocessing to make the data usable.
Missing values or inconsistent formatting in the reserves column could lead to data loss during cleaning.
3. Suggestions for Improvement
To make the scraping process more robust and efficient:

Automate Column Selection:

Use column indexing dynamically to select only the relevant columns (e.g., Countries and a single Forex reserves excluding gold column), avoiding hardcoding column names.
Handle Formatting Issues:

Automate the conversion of reserves data to numeric format while removing commas and filtering out non-numeric values
Error Handling for Missing Data:

Implement checks to handle rows with missing values or unexpected formatting gracefully.
Future-Proofing

Regularly verify the script against the webpage, as Wikipedia content can change. Alternatively, explore using APIs or other structured data sources where possible.
Alternative Sources:

For critical analyses, cross-check the scraped data with official reports or databases to ensure accuracy and completeness.