Anatomía de un colapso financiero evitable en la Caja Fiscal (2015–2025)

Overview of methods for assembling multi-panel figures in R. BRC study group session tutorial.

Titanic Data Analysis: Correlation Matrix, Variance-Covariance Matrix, and Eigenvalue/Eigenvector Author: Dimas Rafi Izzulhaq - 24031554084

This project is part of the INT24 assignment, which aims to develop foundational skills in using R and Rpubs for data analysis and statistical exploration. The dataset used in this task is the Titanic Dataset obtained from Kaggle https://www.kaggle.com/datasets/yasserh/titanic-dataset?select=Titanic-Dataset.csv The analysis begins with importing the dataset into R and conducting an initial exploration to understand its structure and variables. From the available features, four numerical variables were selected for further analysis: Age, SibSp, Parch, and Fare. Rows containing missing values in these variables were removed to ensure the validity of statistical computations. Several statistical analyses were performed using R. First, a correlation matrix was generated to examine the strength and direction of relationships among the selected variables. Next, a variance–covariance matrix was computed to measure how the variables vary together and to provide insight into their joint variability. Eigen values and eigen vectors were then calculated based on the covariance matrix to identify the principal directions of variance in the data, which serves as a foundation for understanding dimensionality reduction concepts such as Principal Component Analysis (PCA). Each output is interpreted to explain the relationships between variables, the scale of variability, and the contribution of each component to the overall variance of the dataset. Through this task, R is used not only as a computational tool but also as a medium for reproducible data analysis, while Rpubs is utilized as a platform to publish and share analytical results in a clear and structured format.

Projet d'analyse des K plus proches voisin

Comparativo da produção entre cooperativa local e indústria. O gráfico apresenta a participação percentual relativa da cooperativa e da indústria em diferentes indicadores produtivos e econômicos, incluindo capacidade de produção, custos, receita, lucro e payback estimado. Observa-se maior participação industrial nos indicadores de escala produtiva e receita, enquanto a cooperativa apresenta desempenho competitivo em custos e tempo de retorno, evidenciando seu potencial como alternativa produtiva sustentável.

Publication de la séance 26 janvier 2026 en .rmd.