Graphical Data Analysis with R / by Antony Unwin.

Format:

Book

Author/Creator:

Unwin, Antony, author.

Series:

Chapman & Hall/CRC the R series (CRC Press)

Chapman & Hall/CRC the R series

Language:

English

Subjects (All):

Information visualization.

Visual analytics.

Data mining--Graphic methods.

Data mining.

R (Computer program language).

Physical Description:

1 online resource (306 p.)

Edition:

1st edition

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, [2018].

System Details:

text file

Summary:

See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so) All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Contents:

Setting the Scene Graphics in action Introduction What is graphical data analysis (GDA)? Using this book, the R code in it, and the books webpage

Brief Review of the Literature and Background Materials Literature review Interactive graphics Other graphics software Websites Datasets Statistical texts

Examining Continuous Variables Introduction What features might continuous variables have? Looking for features Comparing distributions by subgroups What plots are there for individual continuous variables? Plot options Modelling and testing for continuous variables

Displaying Categorical Data Introduction What features might categorical variables have? Nominal datano fixed category order Ordinal datafixed category order Discrete datacounts and integers Formats, factors, estimates, and barcharts Modelling and testing for categorical variables

Looking for Structure: Dependency Relationships and Associations Introduction What features might be visible in scatterplots? Looking at pairs of continuous variables Adding models: lines and smooths Comparing groups within scatterplots Scatterplot matrices for looking at many pairs of variables Scatterplot options Modelling and testing for relationships between variables

Investigating Multivariate Continuous Data Introduction What is a parallel coordinate plot (pcp)? Features you can see with parallel coordinate plots Interpreting clustering results Parallel coordinate plots and time series Parallel coordinate plots for indices Options for parallel coordinate plots Modelling and testing for multivariate continuous data Parallel coordinate plots and comparing model results

Studying Multivariate Categorical Data Introduction Data on the sinking of the Titanic What is a mosaicplot? Different mosaicplots for different questions of interest Which mosaicplot is the right one? Additional options Modelling and testing for multivariate categorical data

Getting an Overview Introduction Many individual displays Multivariate overviews Multivariate overviews for categorical variables Graphics by group Modelling and testing for overviews

Graphics and Data Quality: How Good Are the Data? Introduction Missing values Outliers Modelling and testing for data quality

Comparisons, Comparisons, Comparisons Introduction Making comparisons Making visual comparisons Comparing group effects graphically Comparing rates visually Graphics for comparing many subsets Graphics principles for comparisons Modelling and testing for comparisons

Graphics for Time Series Introduction Graphics for a single time series Multiple series Special features of time series Alternative graphics for time series R classes and packages for time series Modelling and testing time series

Ensemble Graphics and Case Studies Introduction What is an ensemble of graphics? Combining different viewsa case study example Case studies

Some Notes on Graphics with R Graphics systems in R Loading datasets and packages for graphical analysis Graphics conventions in statistics What is a graphic anyway? Options for all graphics Some R graphics advice and coding tips Other graphics Large datasets Perfecting graphics

Summary Data analysis and graphics Key features of GDA Strengths and weaknesses of GDA Recommendations for GDA

References

General Index

Datasets Index.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781315362298

1315362295

9781315360041

1315360047

9781315370088

1315370085

9781498786775

1498786774

OCLC:

906798286