Coverart for item
The Resource Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti

Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti

Label
Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language
Title
Data analysis with R
Title remainder
load, wrangle, and analyze your data using the world's most powerful statistical programming language
Statement of responsibility
Tony Fischetti
Creator
Author
Subject
Language
eng
Summary
Annotation
Member of
Cataloging source
UMI
http://library.link/vocab/creatorName
Fischetti, Tony
Dewey number
519.50285
Illustrations
illustrations
Index
index present
LC call number
QA276.45.R3
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Community experience distilled
http://library.link/vocab/subjectName
  • R (Computer program language)
  • Data mining
  • Mathematical statistics
  • MATHEMATICS
  • MATHEMATICS
  • Mathematical statistics
  • R (Computer program language)
Summary expansion
Load, wrangle, and analyze your data using the world's most powerful statistical programming languageAbout This Book Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practiceWho This Book Is ForWhether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn Navigate the R environment Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Employ hypothesis tests to draw inferences from your data Learn Bayesian methods for estimating parameters Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibilityIn DetailFrequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approachLearn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach
Label
Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
Instantiates
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: RefresheR; Navigating the basics; Arithmetic and assignment; Logicals and characters; Flow of control; Getting help in R; Vectors; Subsetting; Vectorized functions; Advanced subsetting; Recycling; Functions; Matrices; Loading data into R; Working with packages; Exercises; Summary; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises
  • The binomial distributionThe normal distribution; The three-sigma rule and using z-tables; Exercises; Summary; Chapter 5: Using Data to Reason About the World; Estimating means; The sampling distribution; Interval estimation; How did we get 1.96?; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; Null Hypothesis Significance Testing; One and two-tailed tests; When things go wrong; A warning about significance; A warning about p-values; Testing the mean of one sample; Assumptions of the one sample t-test; Testing two means; Don't be fooled!
  • Assumptions of the independent samples t-testTesting more than two means; Assumptions of ANOVA; Testing independence of proportions; What if my assumptions are unfounded?; Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC -- stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor
  • A word of warningMultiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off; Cross-validation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; k-Nearest Neighbors; Using k-NN in R; Confusion matrices; Limitations of k-NN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary
Control code
935744749
Dimensions
unknown
Extent
1 online resource (1 volume)
Form of item
online
Governing access note
Owing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland. For more information contact enquiries@nls.uk.
Isbn
9781785288142
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
9781785288142
Other physical details
illustrations.
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000706
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)935744749
Label
Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language, Tony Fischetti
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: RefresheR; Navigating the basics; Arithmetic and assignment; Logicals and characters; Flow of control; Getting help in R; Vectors; Subsetting; Vectorized functions; Advanced subsetting; Recycling; Functions; Matrices; Loading data into R; Working with packages; Exercises; Summary; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises
  • The binomial distributionThe normal distribution; The three-sigma rule and using z-tables; Exercises; Summary; Chapter 5: Using Data to Reason About the World; Estimating means; The sampling distribution; Interval estimation; How did we get 1.96?; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; Null Hypothesis Significance Testing; One and two-tailed tests; When things go wrong; A warning about significance; A warning about p-values; Testing the mean of one sample; Assumptions of the one sample t-test; Testing two means; Don't be fooled!
  • Assumptions of the independent samples t-testTesting more than two means; Assumptions of ANOVA; Testing independence of proportions; What if my assumptions are unfounded?; Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC -- stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor
  • A word of warningMultiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off; Cross-validation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; k-Nearest Neighbors; Using k-NN in R; Confusion matrices; Limitations of k-NN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary
Control code
935744749
Dimensions
unknown
Extent
1 online resource (1 volume)
Form of item
online
Governing access note
Owing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland. For more information contact enquiries@nls.uk.
Isbn
9781785288142
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
9781785288142
Other physical details
illustrations.
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000706
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)935744749

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