Text Mining with R#
Summary of Source Files#
These materials incorporate content, methods, and examples from Text Mining with R: A Tidy Approach by Julia Silge and David Robinson. The book introduces practical text mining techniques using the tidytext package alongside other tidyverse tools in R. Its emphasis is on applying tidy data principles to natural language, enabling structured, consistent workflows for unstructured text. The examples cover sentiment analysis, tf-idf, n-grams, topic modeling, and case studies using literary, social media, and real-world datasets. The book focuses on applied code examples, intended for users familiar with basic R packages such as dplyr, ggplot2, and the pipe operator %>%.
Acknowledgment#
These files draw heavily on the work of Julia Silge and David Robinson, particularly their Text Mining with R book and the accompanying tidytext package. We are grateful for their contributions to open data science and for providing resources that make text mining more accessible through tidy principles. Their work forms the foundation for much of the analysis and code structure used here.