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

ISBN/GTIN

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

von

Perpinan Lamigueiro, Oscar

BuchGebunden

Verkaufsrang16630inMathematik

CHF99.90

E-BookabCHF125.75

In den Warenkorb

Beschreibung

This book explores methods to display time series, spatial and spacetimedata using R, and aims to be a synthesis of both groups providing code and detailed information to produce high quality graphics with practical examples. Organized into three parts, the book covers the various visualization methods or data characteristics. The chapters are structured as independent units so readers can jump directly to a certain chapter according to their needs. Dependencies and redundancies between the set of chapters have been conveniently signaled with cross-references.

Weitere Beschreibungen

Details

ISBN/GTIN978-1-4665-6520-3

ProduktartBuch

EinbandGebunden

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum01.07.2014

ReiheChapman & Hall/CRC The R Series

Seiten208 Seiten

SpracheEnglisch

MasseBreite 156 mm, Höhe 235 mm

Gewicht499 g

Illustrationenfarb. Abb.

Artikel-Nr.16626679

KatalogBuchzentrum

Datenquelle-Nr.15748137

WarengruppeMathematik

Weitere Details

Reihe

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

Statistical Computing with R

R Graphics

Statistical Computing in C++ and R

Event History Analysis with 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

Analyzing Sensory Data with R

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

Using R for Numerical Analysis in Science and Engineering

Advanced R

Introduction to Scientific Programming and Simulation Using R

Using R for Introductory Statistics

Data Science in R

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

Using the R Commander

bookdown

Spatial Microsimulation with R

The Essentials of Data Science: Knowledge Discovery Using R

R Primer

Dynamic Documents with R and knitr

Reproducible Research with R and R Studio

Extending R

bookdown

Using the R Commander

Customer and Business Analytics

R Primer

blogdown

Flexible Regression and Smoothing

Data Science in R

R Markdown

Reproducible Finance with R

Practical R for Mass Communication and Journalism

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

Advanced R, Second Edition

Advanced R, Second Edition

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

Reproducible Research with R and RStudio

Learn R

Learn R

Learning Microeconometrics with R

Geocomputation with R

Digital Media

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Interactive Documents with R

Engineering Production-Grade Shiny Apps

Engineering Production-Grade Shiny Apps

Javascript for R

Javascript for R

Advanced R Solutions

Advanced R Solutions

Dose-Response Analysis Using R

Statistics in Toxicology Using R

Distributions for Modeling Location, Scale, and Shape

Event History Analysis with R

Outstanding User Interfaces with Shiny

Outstanding User Interfaces with Shiny

Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R

Spatial Sampling with R

A Criminologist's Guide to R

R for Political Data Science

ANOVA and Mixed Models

ANOVA and Mixed Models

Open Science with R

Spatial Data Science

Starting Quantitative Analysis with R

Analyzing US Census Data

Analyzing US Census Data

Deep Learning and Scientific Computing with R torch

Tidy Finance with R

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

Modern Data Visualization with R

Learn R

Learn R

Behavior Analysis with Machine Learning Using R

Event History Analysis with R

Spatial Analysis in Geology Using R

Analyzing Baseball Data with R

Analyzing Baseball Data with R

Introductory Fisheries Analyses with R

Multiple Factor Analysis by Example Using R

Über den/die AutorIn

Perpinan Lamigueiro, Oscar

Oscar Perpiñán Lamigueiro is a lecturer of photovoltaic and solar energy at the Escuela de Organización Industrial (EOI) and an assistant professor of electrical engineering at the Universidad Politécnica de Madrid (UPM). He develops R packages that provide graphical methods to display multivariate time series, spatial data, and space-time data (rasterVis and solaR).

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