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Outstanding User Interfaces with Shiny

ISBN/GTIN

Outstanding User Interfaces with Shiny

von

BuchGebunden

Verkaufsrang33590inInformatik EDV

CHF219.00

E-BookabCHF103.20

In den Warenkorb

Beschreibung

Outstanding User Interfaces with Shiny provides the reader with necessary knowledge to develop beautiful and highly interactive user interfaces.

Weitere Beschreibungen

Details

ISBN/GTIN978-0-367-64526-7

ProduktartBuch

EinbandGebunden

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum31.08.2022

Auflage1. A.

ReiheChapman & Hall/CRC The R Series

Seiten486 Seiten

SpracheEnglisch

MasseBreite 178 mm, Höhe 254 mm

Gewicht1188 g

IllustrationenFarb., s/w. Abb.

Artikel-Nr.44636849

KatalogBuchzentrum

Datenquelle-Nr.37958653

WarengruppeInformatik EDV

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

David Granjon holds a PhD in applied mathematics from Université Pierre et Marie Curie and Université de Lausanne. He is the founder and maintainer of the open source RinteRface organisation (https://rinterface.com) where he develops Shiny extensions ({bs4Dash}, {shinyMobile}, {shinydashboardplus}, ...) and deliver novel advanced Shiny workshops in worldwide R conferences like useR or R in Pharma. David works as a full-fulltime Senior Data Science Expert at Novartis where he provides his expertise to help associates to design outstanding user interfaces for clinical trials.

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