Beginning data science in R : data analysis, visualization, and modelling for the data scientist / Thomas Mailund.

Format:

Book

Author/Creator:

Mailund, Thomas, author.

Contributor:

ebrary, Inc.

Language:

English

Subjects (All):

Quantitative research.

Physical Description:

1 online resource

polychrome

Place of Publication:

New York : Apress, [2017].

System Details:

text file

Contents:

At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction to R Programming; Basic Interaction with R; Using R as a Calculator; Simple Expressions; Assignments; Actually, All of the Above Are Vectors of Values&; Indexing Vectors; Vectorized Expressions; Comments; Functions; Getting Documentation for Functions; Writing Your Own Functions; Vectorized Expressions and Functions; A Quick Look at Control Structures; Factors; Data Frames; Dealing with Missing Values; Using R Packages

Data Pipelines (or Pointless Programming)Writing Pipelines of Function Calls; Writing Functions that Work with Pipelines; The magical "." argument; Defining Functions Using .; Anonymous Functions; Other Pipeline Operations; Coding and Naming Conventions; Exercises; Mean of Positive Values; Root Mean Square Error; Chapter 2: Reproducible Analysis; Literate Programming and Integration of Workflow and Documentation; Creating an R Markdown/knitr Document in RStudio; The YAML Language; The Markdown Language; Formatting Text; Cross-Referencing; Bibliographies

Controlling the Output (Templates/Stylesheets)Running R Code in Markdown Documents; Using Chunks when Analyzing Data (Without Compiling Documents); Caching Results; Displaying Data; Exercises; Create an R Markdown Document; Produce Different Output; Add Caching; Chapter 3: Data Manipulation; Data Already in R; Quickly Reviewing Data; Reading Data; Examples of Reading and Formatting Datasets; Breast Cancer Dataset; Boston Housing Dataset; The readr Package; Manipulating Data with dplyr; Some Useful dplyr Functions; select(): Pick Selected Columns and Get Rid of the Rest

Mutate():Add Computed Values to Your Data FrameTransmute(): Add Computed Values to Your Data Frame and Get Rid of All Other Columns; arrange(): Reorder Your Data Frame by Sorting Columns; filter(): Pick Selected Rows and Get Rid of the Rest; group_by(): Split Your Data Into Subtables Based on Column Values; summarise/summarize(): Calculate Summary Statistics; Breast Cancer Data Manipulation; Tidying Data with tidyr; Exercises; Importing Data; Using dplyr; Using tidyr; Chapter 4: Visualizing Data; Basic Graphics; The Grammar of Graphics and the ggplot2 Package; Using qplot(); Using Geometries

FacetsScaling; Themes and Other Graphics Transformations; Figures with Multiple Plots; Exercises; Chapter 5: Working with Large Datasets; Subsample Your Data Before You Analyze the Full Dataset; Running Out of Memory During Analysis; Too Large to Plot; Too Slow to Analyze; Too Large to Load; Exercises; Subsampling; Hex and 2D Density Plots; Chapter 6: Supervised Learning; Machine Learning; Supervised Learning; Regression versus Classification; Inference versus Prediction; Specifying Models; Linear Regression; Logistic Regression (Classification, Really); Model Matrices and Formula

Notes:

Includes index.

Electronic reproduction. Palo Alto, Calif. Available via World Wide Web.

Vendor-supplied metadata.

ISBN:

9781484226711

1484226712

Publisher Number:

99971659598

Access Restriction:

Restricted for use by site license.