The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.
The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Rafael A. Irizarry is professor of data sciences at the Dana-Farber Cancer Institute, professor of biostatistics at Harvard, and a fellow of the American Statistical Association. Dr. Irizarry is an applied statistician and during the last 20 years has worked in diverse areas, including genomics, sound engineering, and public health. 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
I R. 1 Installing R and RStudio. 2. Getting Started with R and RStudio. 3. R Basics. 4. Programming basics. 5. The tidyverse. 6. Importing data. II Data Visualization. 7. Introduction to data visualization. 8. ggplot2. 9. Visualizing data distributions. 10. Data visualization in practice. 11. Data visualization principles. 12. Robust summaries. III Statistics with R. 13. Introduction to Statistics with R. 14. Probability. 15. Random variables. 16. Statistical Inference. 17. Statistical models. 18. Regression. 19. Linear Models. 20. Association is not causation. IV Data Wrangling. 21. Introduction to Data Wrangling. 22. Reshaping data. 23. Joining tables. 24. Web Scraping. 25. String Processing. 26. Parsing Dates and Times. 27. Text mining. V Machine Learning. 28. Introduction to Machine Learning. 29. Smoothing. 30. Cross validation. 31. The caret package. 32. Examples of algorithms. 33. Machine learning in practice. 34. Large datasets. 35. Clustering. VI Productivity tools. 36. Introduction to productivity tools. 37. Accessing the terminal and installing Git. 38. Organizing with Unix. 39. Git and GitHub. 40. Reproducible projects with RStudio and R markdown.
I R. 1 Installing R and RStudio. 2. Getting Started with R and RStudio. 3. R Basics. 4. Programming basics. 5. The tidyverse. 6. Importing data. II Data Visualization. 7. Introduction to data visualization. 8. ggplot2. 9. Visualizing data distributions. 10. Data visualization in practice. 11. Data visualization principles. 12. Robust summaries. III Statistics with R. 13. Introduction to Statistics with R. 14. Probability. 15. Random variables. 16. Statistical Inference. 17. Statistical models. 18. Regression. 19. Linear Models. 20. Association is not causation. IV Data Wrangling. 21. Introduction to Data Wrangling. 22. Reshaping data. 23. Joining tables. 24. Web Scraping. 25. String Processing. 26. Parsing Dates and Times. 27. Text mining. V Machine Learning. 28. Introduction to Machine Learning. 29. Smoothing. 30. Cross validation. 31. The caret package. 32. Examples of algorithms. 33. Machine learning in practice. 34. Large datasets. 35. Clustering. VI Productivity tools. 36. Introduction to productivity tools. 37. Accessing the terminal and installing Git. 38. Organizing with Unix. 39. Git and GitHub. 40. Reproducible projects with RStudio and R markdown.
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