R for Data Science
Download free course R for Data Science, pdf file on 520 pages by Garrett Grolemund, Hadley Wickham.
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.
Table of contents
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Introduction
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Introduction
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Data visualisation
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Workflow: basics
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Data transformation
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Workflow: scripts
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Exploratory Data Analysis
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Workflow: projects
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Introduction
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Tibbles
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Data import
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Tidy data
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Relational data
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Strings
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Factors
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Dates and times
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Introduction
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Pipes
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Functions
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Vectors
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Iteration
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Introduction
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Model basics
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Model building
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Many models
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Introduction
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R Markdown
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Graphics for communication
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R Markdown formats
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R Markdown workflow
| Pages : | 520 |
| Size : | |
| File type : | |
| Downloads: | 90 |
| Created: | 2022-02-03 |
| License: | CC BY-NC-ND |
| Author(s): | Garrett Grolemund, Hadley Wickham |
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