Sentiment Analysis of R Package Names

This project aims to analyze public sentiment towards various R package names by examining social media posts, blog comments, and forum discussions. The goal is to identify how the names of these packages are perceived within the community, potentially influencing future naming conventions or marketing strategies for R packages.

Objectives:

• Data Collection: Gather textual data from multiple online sources where R users discuss package utility, issues, and experiences.
• Sentiment Analysis: Use natural language processing (NLP) techniques to determine the sentiment (positive, negative, neutral) associated with mentions of specific R package names.
• Insight Generation: Identify patterns and trends in sentiment that may influence how packages are received by the community.

Methodology:

• Data Sources: Twitter, Stack Overflow, Reddit, and personal blogs where R is discussed.
  Tools and Technologies:
• R: To perform data scraping and sentiment analysis, using packages like rtweet, tm, tidytext, and possibly a machine learning package such as caret for more advanced analysis.
• Sentiment Analysis Model: Utilize pre-trained models available through packages like syuzhet or sentimentr, or develop a custom model trained on a labeled dataset specific to the technical language of R users.

Challenges:

• Data Variety and Volume: Managing and processing large volumes of data from diverse sources.
• Accuracy: Ensuring the sentiment analysis accurately interprets the technical language used in R discussions, which might not conform to conventional sentiment models.

Impact:

The findings could provide valuable feedback to R package developers, helping them understand how the names of their packages might impact their adoption and use. Additionally, it could foster a deeper connection between developers and the user community by aligning package names more closely with user expectations and preferences.

NYC Restaurant Health and Location Mapping

This project utilizes R to develop an interactive map that visualizes the locations and health inspection statuses of New York City restaurants. The objective is to provide an easy-to-use visual tool that highlights restaurant health grades across various neighborhoods, aiding potential diners, health inspectors, and business analysts in their assessments and decisions.

Objectives:

• Interactive Mapping: Create an interactive map displaying restaurants’ locations coupled with their latest health inspection grades.
• Insightful Analysis: Extract insights about health inspection trends across different neighborhoods and correlate them with other socio-economic markers.
• Public Engagement: Increase public awareness and engagement regarding the health standards of local dining options.

Methodology:

Data Sources:

• NYC Open Data for health inspection results.
• Geolocation details for restaurants.

Tools and Technologies:

• R: Primary tool for data cleaning, analysis, and visualization.

R Packages:

• ggplot2 for creating static visualizations.
• leaflet for creating interactive maps directly in R.
• dplyr and tidyr for data manipulation.
• shiny to develop an interactive web application.

Challenges:

• Data Accuracy and Completeness: Ensuring the data from public sources is accurate, up-to-date, and comprehensive.
• Performance: Managing large datasets and ensuring the interactive map and web application perform efficiently without significant delays.

Impact:

This project is designed to influence public dining choices positively, assist health inspectors in monitoring and enforcement tasks, and aid restaurant owners in understanding how they are perceived by the public in terms of health safety. By providing a clear visual tool, it can help elevate the overall health standards of the restaurant industry in NYC.