R 語言中的 janitor 套件，就是專門為了解決這些日常資料清理問題而設計的工具。

ExamIDs2026a

Five interactive charts showing Australian flight reliability, delays and cancellations from January 2023 onwards.

For MSBA Lab #1 - Creating a about me page as a introduction into R.

Análise Visual e Enviesamento de Gráficos na Mídia

This workflow performs principal component analysis (PCA) on a large operando X-ray powder diffraction (OXRPD) dataset stored in JSON format. The dataset contains over 92,000 diffraction patterns collected from multiple contributing institutions and is designed to facilitate the exploration of structural relationships, phase evolution, and dominant diffraction features within a large-scale diffraction database. The analysis begins by extracting the dataset and importing all diffraction patterns. Each pattern is interpolated onto a common 2θ grid spanning 5° to 80° with a step size of 0.05° to ensure consistency across samples. Intensities are normalized to their maximum values, allowing comparisons that emphasize structural characteristics rather than absolute signal magnitude. Samples containing missing values after interpolation are removed prior to multivariate analysis. Principal Component Analysis (PCA) is then applied to the processed diffraction matrix to reduce dimensionality while retaining the major sources of structural variation. The workflow generates cumulative variance-exained plots, PCA score plots, and trajectory visualizations that reveal similarities and differences among diffraction patterns and enable investigation of structural evolution across the dataset. To identify possible phase transitions or major structural changes, changepoint analysis is performed on the first principal component (PC1) using the Pruned Exact Linear Time (PELT) algorithm. Detected changepoints partition the dataset into distinct phase regions that are subsequently visualized in PCA space. This provides an automated approach for identifying transitions in diffraction behavior across the sequence of samples. The workflow further examines PCA loadings to determine which diffraction angles contribute most strongly to the observed variance. Peaks with the largest absolute PC1 loadings are extracted and reported as candidate diffraction features associated with structural transformations. Results are exported as CSV files containing PCA scores and the most influential diffraction peak positions for downstream statistical analysis and interpretation. Outputs generated by the workflow include: Cumulative variance explained by principal components PCA score plots colored by source institution Structural evolution trajectories in PCA space Phase-transition detection using changepoint analysis PC1 loading profiles identifying influential diffraction features CSV files containing PCA scores and important diffraction peak positions Identification of diffraction peaks contributing most strongly to structural variation This workflow provides a scalable framework for exploring large OXRPD datasets and facilitates the identification of structural trends, phase evolution pathways, and diffraction features associated with material transformations. Dataset citation: OXRPD Dataset, Zenodo Record 15298026, accessed June 2026. Zenodo Record 15298026