Advanced R Solutions

Grosser, MalteBumann, HenningWickham, Hadley

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This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible.

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ISBN/GTIN978-1-032-00749-6

Product TypeBook

BindingPaperback

PublisherTaylor and FrancisChapman and Hall/CRC

Publishing date24/08/2021

Edition1. A.

SeriesChapman & Hall/CRC The R Series

Pages302 pages

LanguageEnglish

SizeWidth 156 mm, Height 234 mm

Weight420 g

IllustrationsFarb., s/w. Abb.

Article no.43619296

CatalogsBuchzentrum

Data source no.35806503

Product groupMathematik

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Series

Dealing with Memory Limits and Working with Large Data Sets in R

Statistical Computing in C++ and R

Customer and Business Analytics

Programming Graphical User Interfaces in R

Analyzing Baseball Data with R

Implementing Reproducible Research

Stated Preference Methods Using R

Growth Curve Analysis and Visualization Using R

Computational Actuarial Science with R

Multiple Factor Analysis by Example Using R

Displaying Time Series, Spatial, and Space-Time Data with R

Using R for Numerical Analysis in Science and Engineering

Introduction to Scientific Programming and Simulation Using R

Using R for Introductory Statistics

Parallel Computing for Data Science

Dynamic Documents with R and knitr

Statistics in Toxicology Using R

Graphical Data Analysis with R

Reproducible Research with R and R Studio

Introductory Fisheries Analyses with R

Spatial Microsimulation with R

The Essentials of Data Science: Knowledge Discovery Using R

Dynamic Documents with R and knitr

Reproducible Research with R and R Studio

Customer and Business Analytics

Flexible Regression and Smoothing

Practical R for Mass Communication and Journalism

Displaying Time Series, Spatial, and Space-Time Data with R

Statistical Computing with R, Second Edition

Hands-On Machine Learning with R

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Reproducible Research with R and RStudio

Learning Microeconometrics with R

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Engineering Production-Grade Shiny Apps

Dose-Response Analysis Using R

Statistics in Toxicology Using R

Distributions for Modeling Location, Scale, and Shape

Outstanding User Interfaces with Shiny

Rasch Measurement Theory Analysis in R

Starting Quantitative Analysis with R

Deep Learning and Scientific Computing with R torch

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Graphical Data Analysis with R

Statistical Computing in C++ and R

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Modern Data Visualization with R

Behavior Analysis with Machine Learning Using R

Spatial Analysis in Geology Using R

Analyzing Baseball Data with R

Introductory Fisheries Analyses with R

Multiple Factor Analysis by Example Using R

Author

Grosser, MalteBumann, HenningWickham, Hadley

Malte Grosser is a business mathematician from Hamburg, who has been programming in R regularly since the beginning of his career. He is currently finishing his PhD on machine learning for stroke outcome prediction and develops solutions in business as a data scientist.

Henning Bumann is a psychologist and statistician who enjoys making sense of data and is motivated to build data-driven solutions that are beautiful and meaningful. He prefers free programming tools to support effective and transparent collaboration.

Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science.

Advanced R Solutions

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