This report presents an R-based workflow for processing GSAS XYE diffraction data and applying multivariate analysis to detect phase transitions. The pipeline includes data import, normalization using incident intensity and channel width corrections, and automated batch processing of multiple datasets. Principal Component Analysis (PCA) is used to capture structural variation across diffraction patterns, and change-point detection is applied to identify phase transitions. This approach enables scalable and data-driven analysis of large diffraction datasets.

This report explores Netflix movies and TV shows using modern visualization techniques. The project focuses on trends in genres, countries, ratings, release patterns, and content growth.

Construcción de Modelos Predictivos y Validació

This project analyzes retail sales performance using the Sample Superstore dataset. The goal of the project is to identify patterns in sales, profit, discounts, shipping modes, and customer segments using a variety of visualizations. The project includes eight visualizations using multiple chart types including: Bar Charts Scatter Plots Line Charts Box Plots Heatmaps Interactive Plotly Visualizations

R

Juan Jose Crespo

Este trabajo evalua la relacion entre natalidad, corrupcion y crecimiento del PBI, caso Peru-LATAM.

Recently worked on a healthcare analytics project in R involving Exploratory Data Analysis, Correlation Analysis, and Multiple Linear Regression to examine cardiovascular and metabolic health indicators. The analysis process included data cleaning, handling missing values, assumption testing, Box-Cox transformation, regression diagnostics, visualization, and interpretation of statistical outputs. All interpretations and supporting discussions were grounded on the actual statistical results obtained from the dataset and supported with related literature to provide evidence-based analytical insights.