Reproducible Research with R and R Studio

von

GANDRUD, CHRISTOPHER

BuchKartoniert, Paperback

Verkaufsrang16630inMathematik

CHF98.90

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Beschreibung

Presents a practical reproducible research workflow that researchers in any quantitative empirical discipline can use to gather data, analyse it, and dynamically present it.

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ISBN/GTIN978-1-4987-1537-9

ProduktartBuch

EinbandKartoniert, Paperback

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum01.07.2015

Auflage2. A.

ReiheChapman & Hall/CRC The R Series

Seiten323 Seiten

SpracheEnglisch

MasseBreite 156 mm, Höhe 235 mm

Gewicht454 g

IllustrationenFarb., s/w. Abb.

Artikel-Nr.21925609

KatalogBuchzentrum

Datenquelle-Nr.18058847

WarengruppeMathematik

Weitere Details

Reihe

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

Über den/die AutorIn

GANDRUD, CHRISTOPHER

Christopher Gandrud is a postdoctoral researcher in the Fiscal Governance Centre at the Hertie School of Governance. His research focuses on the international political economy of public financial and monetary institutions as well as applied social science statistics and software development. He has published many articles in peer-reviewed journals, including the Journal of Common Market Studies, Review of International Political Economy, Political Science Research and Methods, Journal of Statistical Software, and International Political Science Review. He earned a PhD in quantitative political science from the London School of Economics.

Reproducible Research with R and R Studio

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