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R Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages Paperback – November 11, 2019
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From the Back Cover
- Get started with RMarkdown and notebooks
- Import data with readr
- Work with categories using forcats, time and dates with lubridate, and strings with stringr
- Format data using tidyr and then transform that data using magrittr and dplyr
- Write functions with R for data science, data mining, and analytics-based applications
- Visualize data with ggplot 2 and data fit for models using modelr and broom
- Report results with markdown, knitr, shiny, and more
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