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Table of 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

Author/Creator:

Mailund, Thomas , author

Languages:

English

Language Notes:

Item content: English

Main Work:

Beginning data science in R: data analysis, visualization, and modelling for the data scientist [by Mailund, T.]

Subjects:

Quantitative research

General Notes:

Includes index.
Description based on: Vendor-supplied metadata.

Physical Description:

1 online resource.

Digital Characteristics:

text file

Call Numbers:

Q180.55.Q36 M35 2017

ISBNs:

9781484226711
1484226712
9781484226704 [Invalid]

OCLC Numbers:

975486855

Other Control Numbers:

EBC4821246 (source: MiAaPQ)
[Unknown Type]: ybp13670000