R For Data Science Import Tidy Transform Visualize And Model Data Free Pdf
Download R For Data Science Import Tidy Transform Visualize And Model Data Free Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get R For Data Science Import Tidy Transform Visualize And Model Data Free Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
SSD for R
Author: Charles Auerbach
language: en
Publisher: Oxford University Press
Release Date: 2021-10-22
Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. SSD for R serves as a guide for those desiring to conduct single-subject data analysis and introduces readers to the various functions available in SSD for R, a new, free, and innovative software package written in R--the open-source statistical programming language that was written by the book's authors. This second edition of SSD for R is the most comprehensive guide to the numerous graphing and charting functions for conducting robust visual analysis including line graphs and more complex standard deviation lines. This book also contains numerous tests of statistical significance, such as t-tests, chi-squares, and the conservative dual criteria. Auerbach and Zeitlin guide readers through the analytical process based on the characteristics of their data. In addition to presentations and assignments, this new edition contains more examples and illustrations to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation. SSD for R is the only book of its kind to describe single-subject data analysis while providing free statistical software to do so. For more instructional videos, blogs, and a growing community of researchers interested in single-subject designs, visit the authors' website: http://ssdanalysis.com.