Still need z scores for CBM Winter and Spring

Material introdutório para aulas de monitoria no Mestrado Profissional em Economia do Setor Público do CAEN/UFC

In this study, we present a detailed methodology for cleaning and organizing the data from the Spanish football league. The primary objective is to transform raw match data into a structured and analyzable format, ensuring consistency and accuracy. We employ several data processing techniques using R, including reading raw CSV files, converting team names to their abbreviations, and calculating match results. Our approach involves creating a new dataframe that includes both home and away team perspectives, with additional columns for results and win points. The processed data enables more straightforward analysis for various applications, such as performance analysis, trend identification, and predictive modeling. The effectiveness of our method is demonstrated through a step-by-step transformation of the dataset, ensuring that it is ready for advanced statistical analysis and machine learning applications. This paper contributes to the field by providing a reproducible and scalable data cleaning framework, essential for researchers and analysts working with sports data.