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rstudio::conf 2018 case study
Understanding PCA using Shiny and Stack Overflow data
February 26, 2018
Principal component analysis (PCA) is a powerful approach for exploring high-dimensional data, but can be challenging for learners to comprehend. In this talk, I will walk through a practical and interactive explanation of what PCA is and how it works. As a case study I’ll explore a domain that many data analysts and data scientists are familiar with: programming languages and technologies, as understood through traffic to Stack Overflow questions. We will explore how interactive visualization using Shiny gives us insight into the complex, real-world relationships in high-dimensional datasets.
Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning practice. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.