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Paperback. Condition: New. 1st ed. Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling.Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done.You will:Acquire and install R and RStudioImport and export data from multiple file formatsAnalyze data and generate graphics (including confidence intervals)Interactively conduct hypothesis testingCode multiple and moderated regression solutions Who This Book Is For Programmers and data analysts who are new to R. Some prior experience in programming is recommended.
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Add to basketCondition: New. Written by R and statistical programming expertsA unique tutorial on beginning to program with the new R 4 programming languageCovers installing and using R.
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Paperback. Condition: New. 1st ed. Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling.Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done.You will:Acquire and install R and RStudioImport and export data from multiple file formatsAnalyze data and generate graphics (including confidence intervals)Interactively conduct hypothesis testingCode multiple and moderated regression solutions Who This Book Is For Programmers and data analysts who are new to R. Some prior experience in programming is recommended.
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Published by Apress, Incorporated, 2019
ISBN 10: 1484228715 ISBN 13: 9781484228715
Language: English
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Published by Apress, Incorporated, 2019
ISBN 10: 1484228715 ISBN 13: 9781484228715
Language: English
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