Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. No previous knowledge of R is necessary.
Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. No previous knowledge of R is necessary.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Rafael A. Irizarry is professor and chair of Data Science at the Dana-Farber Cancer Institute, professor of biostatistics at Harvard, and a fellow of the American Statistical Association and the International Society of Computational Biology. Prof. Irizarry is an applied statistician and during the last 25 years has worked in diverse areas, including genomics, sound engineering, and public health surveillance. He disseminates solutions to data analysis challenges as open source software, tools that are widely downloaded and used. Prof. Irizarry has also developed and taught several data science courses at Harvard as well as popular online courses.
Inhaltsangabe
Preface Acknowledgements Introduction Part 1: R 1. Getting started 2. R basics 3. Programming basics 4. The tidyverse 5. data.table 6. Importing data Part 2: Data Visualization 7. Visualizing data distributions 8. ggplot2 9. Data visualization principles 10. Data visualization in practice Part 3: Data Wrangling 11. Reshaping data 12. Joining tables 13. Parsing dates and times 14. Locales 15. Extracting data from the web 16. String processing 17. Text analysis Part 4: Productivity Tools 18. Organizing with Unix 19. Git and GitHub 20. Reproducible projects
Preface Acknowledgements Introduction Part 1: R 1. Getting started 2. R basics 3. Programming basics 4. The tidyverse 5. data.table 6. Importing data Part 2: Data Visualization 7. Visualizing data distributions 8. ggplot2 9. Data visualization principles 10. Data visualization in practice Part 3: Data Wrangling 11. Reshaping data 12. Joining tables 13. Parsing dates and times 14. Locales 15. Extracting data from the web 16. String processing 17. Text analysis Part 4: Productivity Tools 18. Organizing with Unix 19. Git and GitHub 20. Reproducible projects
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