Homework #9

Project Objective This analysis evaluates the performance of various precipitation and climate models (CHIRPS, PERSIANN, TAMSATV3, ARCV2, ERA5) against ground-based observations using Taylor diagrams in R. Taylor diagrams provide a concise visualization of model performance by comparing correlation, standard deviation, and centered root-mean-square error (RMSE). The analysis uses synthetic data representing annual and seasonal (JF, MAM, JJAS, OND) precipitation measurements. By leveraging the plotrix and openair packages, we assess the models’ ability to replicate observed precipitation patterns, offering insights into their accuracy and reliability for climate studies.

Visualization of Echoview processing results.

Del curso SOC294, PUCP 2025-1. Incluye gráficos: i) pie, ii) boxplot, iii) histograma, iv) personalización con ggplot2. Con base adaptada del Latinbarometro 2022.

This is the final(week 4)project for https://www.coursera.org/learn/reproducible-research/home/welcome course

Tutorial rápido para rodar as análises RLQ e Fourth-Corner, que relacionam matrizes ambientais, dados de abundância e atributos funcionais de espécies.

This R script report harnesses R’s advanced plotting libraries to explore built-in datasets (iris, mtcars, Titanic, volcano) through diverse visualization techniques, including scatter plots, boxplots, histograms, 3D scatter plots, and interactive visualizations. The code uncovers critical patterns, such as species clustering in iris or performance correlations in mtcars, making it a powerful tool for data analysis. By leveraging ggplot2, plotly, and Chart.js, it produces publication-ready HTML output, ideal for data scientists, analysts, and educators aiming to communicate complex insights with clarity and interactivity.