R Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages
by Thomas Mailund  (Author)

About the Author
Thomas Mailund is an associate professor at Aarhus University, Denmark. He has a background in math and computer science.  For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.  He has published Beginning Data Science in R, Functional Programming in R, and Metaprogramming in R with Apress as well as other books.  

About this book
In this handy, practical book you will cover each concept concisely, with many examples. You'll be introduced to several R data science packages, with examples of how to use each of them.
In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.
After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language.  You'll also be able to carry out data analysis.  

What You Will Learn

• Import data with readr
• Work with categories using forcats, time and dates with lubridate, and strings with stringr
• Format data using tidyr and then transform that data using magrittr and dplyr
• Write functions with R for data science, data mining, and analytics-based applications
• Visualize data with ggplot2 and fit data to models using modelr
  

Who This Book Is For
Programmers new to R's data science, data mining, and analytics packages.  Some prior coding experience with R in general is recommended.  

Brief contents
Chapter 1: Introduction 1
Chapter 2: Importing Data: readr 5
    Functions for Reading Data .6
    File Headers 8
    Column Types 11
        String-based Column Type Specification .12
    Function-based Column Type Specification 18
    Parsing Time and Dates 22
    Space-separated Columns 28
    Functions for Writing Data 31
Chapter 3: Representing Tables: tibble 33
    Creating Tibbles 33
    Indexing Tibbles 38
Chapter 4: Reformatting Tables: tidyr 45
    Tidy Data .45
    Gather and Spread 46
    Complex Column Encodings 51
    Expanding, Crossing, and Completing .57
    Missing Values 61
    Nesting Data .66
Chapter 5: Pipelines: magrittr 71
    The Problem with Pipelines 71
    Pipeline Notation .74
    Pipelines and Function Arguments .75
    Function Composition .78
    Other Pipe Operations .79
Chapter 6: Functional Programming: purrr .83
    General Features of purrr Functions .84
    Filtering .84
    Mapping 86
    Reduce and Accumulate .97
    Partial Evaluation and Function Composition 101
    Lambda Expressions .104
Chapter 7: Manipulating Data Frames: dplyr 109
    Selecting Columns 109
    Filter 117
    Sorting 125
    Modifying Data Frames .127
    Grouping and Summarizing 133
    Joining Tables .146
    Income in Fictional Countries .155
Chapter 8: Working with Strings: stringr .161
    Counting String Patterns .161
    Splitting Strings 164
    Capitalizing Strings .166
    Wrapping, Padding, and Trimming 166
    Detecting Substrings .171
    Extracting Substrings 174
    Transforming Strings 174
Chapter 9: Working with Factors: forcats 181
    Creating Factors 181
    Concatenation .183
    Projection 186
    Adding Levels 190
    Reorder Levels 191
Chapter 10: Working with Dates: lubridate .195
    Time Points .195
    Time Zones 197
    Time Intervals .199
Chapter 11: Working with Models: broom and modelr .205
    broom .205
    modelr .208
Chapter 12: Plotting: ggplot2  219
    The Basic Plotting Components in ggplot2 .219
    Adding Components to Plot Objects 221
        Adding Data .223
        Adding Aesthetics 223
        Adding Geometries 224
        Facets 232
        Adding Coordinates .236
Chapter 13: Conclusions .239
Index .241

Pages: 246 pages
Publisher: Apress; 1st ed. edition (September 30, 2019)
Language: English
ISBN-10: 1484248937
ISBN-13: 978-1484248935

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