Do longer matches affect serving performance? Let's examine the WTA Grand Slam data analysis (2018–2022) to discover trends in aces, double faults, and the challenges posed by different court surfaces.

Exploring how total champion bans influence early-game gold advantage in League of Legends matches. I analyze the relationship between the total number of bans and gold difference at 15 minutes (golddiffat15), a critical point in the game. Through visualizations, statistical analysis, and some straightforward insights, this project bridges esports strategy with data science concepts, making it relatable even for those unfamiliar with the game.

An interactive exploration of health indicators across Buffalo neighborhoods in 2017. This project visualizes data on preventive health measures like annual checkups, cholesterol screenings, and health insurance using bar plots, geographic scatter plots, and an enhanced interactive map with neighborhood-specific insights. These visualizations highlight where health engagement varies, offering a data-driven foundation for targeting public health resources.

Like stars scattered across the cosmos, the secrets of stellar data can seem light-years away—until now. From giants like Atria (herro～it me～) to cool dwarfs, near and far, we'll bring these insights into focus with the right tools. We'll use the stars dataset from DSLabs, starting with exploratory analysis to uncover patterns across spectral classes. The main interactive chart allows viewers to dive into individual star characteristics—temperature, magnitude, and spectral class, while a trend line reveals the overarching relationship between temperature and brightness. This project demonstrates how data visualization can make complex scientific datasets accessible, helping uncover insights within the vastness of the cosmos. So, come on and journey to the stars~

I want to know whether players on the ice longer tend to attempt more shots on goal, focusing on the individual positions as different hockey positions may influence shooting behavior. The project analyzes the relationship between ice time and SOG for all skaters during the 2023-24 NHL regular season by using linear regression, data visualization, and EDA to provide player insights.

Let's take a look at two charts created from the Nations dataset, where we'll see how GDP has changed over time for four selected countries (Philippines, Egypt, Turkey, and Finland) and how regions compare in terms of GDP across different years. Utilizing dplyr and ggplot2 for storytelling and visualization, see how these nations' economies have evolved and how regions stack up against each other with added interactivity!

I dove into some interesting patterns I found while looking at attendance during The Excalibur Oct. 4 meeting. By tracking when people arrive and how long they stay, I discovered some cool insights, like when we have the most people present and how that can help us plan meetings better. With these trends, we can make sure key moments, like announcements or main activities, happen when everyone’s around and fully engaged. This analysis is a first step, and I’ll update it as we collect more data in future meetings to make our weekly meetings more effective. It’s all about making our meetings smoother and more effective for the staff. 13 people attended this hybrid meeting in person or via Zoom.

This visualization uses the nycflights23 dataset to explore the relationship between scheduled departure times and arrival delays for flights departing from New York City's major airports (JFK, LGA, and EWR). The scatterplot highlights how departure delays, represented by a color gradient, interact with scheduled departure times to affect arrival delays. Key trends include the wider range of delays for later flights and the outliers that suggest unpredictable factors influencing delays throughout the day.

I explored hate crime data from New York between 2010 and 2016 using R, ggplot2, and the Hate Crimes dataset. By visualizing trends across different categories (race, religion, LGBTQ+), we dive into patterns by year and county. I also normalized the data using population info to give a clearer picture of what’s happening. I also wrote a short essay discussing the strengths and weaknesses of the dataset, along with some ideas for future research.

The project explores the Airquality dataset using ggplot2 in R as a tutorial for data visualization. Various plots, including histograms, boxplots, and a density plot, are created to visualize seasonal trends in temperature and wind speed from May to September 1973. The fifth plot, a density plot of wind speed, highlights month-to-month variations. The dataset, recorded daily, is sourced from the New York State Department of Conservation and the National Weather Service for the period of May to September 1973.