Data Science by Nicolas Pedraza

  StatVizR: Simplifying Statistical Analysis and Visualization in R   Nicolas Pedraza LIS4370  Final Project  4/26/2024  Greetings! Today I introduce StatVizR, an R package designed to streamline statistical analysis and visualization tasks. Whether you're a seasoned data analyst or just dipping your toes in data science, StatVizR is here to make your life easier.  What is StatVizR?   StatVizR is more than just another R package - it's your go-to toolkit for everything related to statistical analysis and visualization. With a comprehensive set of functions, StatVizR empowers users to efficiently manipulate data, explore patterns, conduct hypothesis tests, perform regression analysis, and create insightful visualizations.  Why StatVizR?  You might be wondering, "Why should I choose StatVizR over other packages?" Well, here are a few reasons:  1. Ease of Use:  StatVizR is designed with user-friendliness in mind. Whether you're a begin...

  Project Documentation: Exploring Top Spotify Songs    Introduction:  In an era dominated by digital streaming platforms, the music industry has undergone a profound transformation in how music is created, distributed, and consumed. Among these platforms, Spotify stands out as a global leader, providing users with access to a vast library of songs and personalized music recommendations. Understanding the factors that contribute to the success of top Spotify songs is crucial for artists, record labels, and industry analysts seeking to navigate the evolving landscape of music consumption.  In this project, we embark on a journey to explore the intricacies of top Spotify songs, aiming to unravel the underlying patterns and trends that shape their popularity. Through a combination of exploratory data analysis (EDA) techniques and visual analytics methodologies, we explore the vast reservoir of Spotify data to uncover insights that inform strategic decision-making a...

 In this blog post, we'll delve into the world of animated scatter plots using R, leveraging the ggplot2 and animation packages to create dynamic visualizations. Animated plots can be powerful tools for conveying changes and patterns over time, making complex data more accessible and engaging. Setting the Stage:  To begin, we must ensure that the required R packages (animation and ggplot2) are installed and loaded. These packages offer robust functionality for creating animations and generating sophisticated plots, respectively. Crafting the Animation:  Our goal is to construct a series of scatter plots, each representing a distinct frame in our animation. We'll use randomly generated data points within specified limits (-3 to 3 on both the x- and y-axes) to create variability across frames. Bringing It to Life:  Upon executing the code, a GIF named scatter_animation.gif will be generated in your working directory. This file contains a sequence of frames, each showca...

In the realm of data visualization, exploring social networks can yield fascinating insights into relationships and connectivity. Recently, I delved into this area using R, leveraging packages like GGally, network, sna, and ggplot2. Here’s a recount of my journey, highlighting both successes and challenges encountered along the way. Package Installation and Setup:  The initial step was straightforward—installing and loading the necessary packages. Using install.packages() and library() commands, I quickly integrated GGally, network, sna, and ggplot2 into my R environment. Generating Random Network Data: I utilized the rgraph() function from the network package to create a random graph consisting of 10 nodes. Setting mode = "graph" and tprob = 0.5 ensured a symmetric and undirected graph. Visualizing the Network: With the network data prepared, I used ggnet2() from GGally to generate a visualization of the social network. This function creates an aesthetically pleasing graph r...

  CODE:  > # Load required libraries > library(ggplot2) > library(ggthemes) >  > # Sample data > data <- data.frame( +     x = rnorm(100), +     y = rnorm(100) + ) >  > # Create scatter plot > scatter_plot <- ggplot(data, aes(x = x, y = y)) + +     geom_point() + +     theme_tufte() >  > # Display plot > scatter_plot