I recently (Gow, 2026) proposed an extension to the data science “whole game” of R for Data Science (Wickham et al., 2023). In Gow (2026), I used Australian stock price data to illustrate the data curation process and, in this note, I use US bank “Call Report” data as a second illustration. In effect, I provide complete instructions for building a high-performance data library covering all Call Reports data provided by the FFIEC Bulk Data website that can be construction in less than ten minutes on fast hardware (or a couple of hours on an older machine). I also give a few brief demonstrations of how to use the curated data, with examples for both R and Python. I conclude by discussing challenges encountered during processing and offering some observations about AI and data curation.

Laporan ini bertujuan untuk menganalisis struktur data HCV menggunakan pendekatan aljabar matriks. Tahapan dimulai dengan pre-processing untuk menyeleksi variabel numerik dan menangani missing values. Fokus analisis meliputi perhitungan Matriks Korelasi untuk mengidentifikasi pola hubungan antar-variabel, serta Matriks Varians-Kovarians untuk mengukur penyebaran data. Terakhir, dilakukan perhitungan nilai dan vektor eigen untuk memahami karakteristik dimensi data tersebut.

Min/Max/Avg. Temperatures

Worcester, MA Temperature Plot - January, February, March, & April (Q1) - Min/Max/Avg. Temperatures

New Haven Temperature Plot - January, February, March, & April (Q1) - Min/Max/Avg. Temperatures

This analysis examined the linear association between height and weight among U.S. adults using Pearson correlation. Results indicated a moderate positive relationship (r = 0.451), suggesting that taller individuals tend to weigh more. The association was statistically significant (t = 42.618, p < 0.001), and the 95% confidence interval (0.432, 0.469) excluded zero, providing strong evidence against the null hypothesis of no correlation. The coefficient of determination (r² = 0.203) indicates that approximately 20.3% of the variability in one measure is explained by the other, reflecting a meaningful but not complete linear relationship.