Mailund R Data Science Quick Reference

A Pocket Guide to APIs, Libraries, and Packages
1. ed
ISBN: 978-1-4842-4894-2
Verlag: Apress
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 246 Seiten

In this handy, practical book you will cover each concept concisely, with many illustrative 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 LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize 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.  

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.  

Autoren/Hrsg.

Mailund, Thomas

Inhaltsverzeichnis

1;Table of Contents;4
2;About the Author;8
3;About the Technical Reviewer;9
4;Chapter 1: Introduction;10
5;Chapter 2: Importing Data: readr;13
5.1;Functions for Reading Data;14
5.2;File Headers;16
5.3;Column Types;19
5.3.1;String-based Column Type Specification;20
5.4;Function-based Column Type Specification;26
5.5;Parsing Time and Dates;30
5.6;Space-separated Columns;36
5.7;Functions for Writing Data;39
6;Chapter 3: Representing Tables: tibble;40
6.1;Creating Tibbles;40
6.2;Indexing Tibbles;45
7;Chapter 4: Reformatting Tables: tidyr;51
7.1;Tidy Data;51
7.2;Gather and Spread;52
7.3;Complex Column Encodings;57
7.4;Expanding, Crossing, and Completing;63
7.5;Missing Values;67
7.6;Nesting Data;72
8;Chapter 5: Pipelines: magrittr;76
8.1;The Problem with Pipelines;76
8.2;Pipeline Notation;79
8.3;Pipelines and Function Arguments;80
8.4;Function Composition;83
8.5;Other Pipe Operations;84
9;Chapter 6: Functional Programming: purrr;87
9.1;General Features of purrr Functions;88
9.2;Filtering;88
9.3;Mapping;90
9.4;Reduce and Accumulate;101
9.5;Partial Evaluation and Function Composition;105
9.6;Lambda Expressions;108
10;Chapter 7: Manipulating Data Frames: dplyr;112
10.1;Selecting Columns;112
10.2;Filter;120
10.3;Sorting;128
10.4;Modifying Data Frames;130
10.5;Grouping and Summarizing;136
10.6;Joining Tables;149
10.7;Income in Fictional Countries;158
11;Chapter 8: Working with Strings: stringr;164
11.1;Counting String Patterns;164
11.2;Splitting Strings;167
11.3;Capitalizing Strings;169
11.4;Wrapping, Padding, and Trimming;169
11.5;Detecting Substrings;174
11.6;Extracting Substrings;177
11.7;Transforming Strings;177
12;Chapter 9: Working with Factors: forcats;184
12.1;Creating Factors;184
12.2;Concatenation;186
12.3;Projection;189
12.4;Adding Levels;193
12.5;Reorder Levels;194
13;Chapter 10: Working with Dates: lubridate;197
13.1;Time Points;197
13.2;Time Zones;199
13.3;Time Intervals;201
14;Chapter 11: Working with Models: broom and modelr;206
14.1;broom;206
14.2;modelr;209
15;Chapter 12: Plotting: ggplot2;220
15.1;The Basic Plotting Components in ggplot2;220
15.2;Adding Components to Plot Objects;222
15.2.1;Adding Data;224
15.2.2;Adding Aesthetics;224
15.2.3;Adding Geometries;225
15.2.4;Facets;233
15.2.5;Adding Coordinates;237
16;Chapter 13: Conclusions;240
17;Index;241