Graphical data analysis with R
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.
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Boca Raton, Florida, United States CRC Press Taylor & Francis Group
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Visualización de la información Métodos gráficos Minería de datos R (Lenguaje de programación para computadora) Visualización de la información Métodos gráficos Minería de datos R (Lenguaje de programación para computadora) |
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Visualización de la información Métodos gráficos Minería de datos R (Lenguaje de programación para computadora) Visualización de la información Métodos gráficos Minería de datos R (Lenguaje de programación para computadora) Unwin, Antony autor/a Graphical data analysis with R |
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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. |
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Visualización de la información Métodos gráficos Minería de datos R (Lenguaje de programación para computadora) |
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Unwin, Antony autor/a |
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Unwin, Antony autor/a |
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Unwin, Antony autor/a |
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Graphical data analysis with R |
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Graphical data analysis with R |
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Graphical data analysis with R |
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Graphical data analysis with R |
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Graphical data analysis with R |
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graphical data analysis with r |
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Boca Raton, Florida, United States CRC Press Taylor & Francis Group |
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c201 |
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KOHA-OAI-ECOSUR:138322020-11-25T06:54:00ZGraphical data analysis with R Unwin, Antony autor/a textBoca Raton, Florida, United States CRC Press Taylor & Francis Groupc2015engGraphical 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.Bibliografía: páginas 279-289 e índice: páginas 291-296Preface xi 1 Setting the Scene.. 1.1 Graphics in action.. 1.2 Introduction.. 1.3 What is Graphical Data Analysis (GDA?.. 1.4 Using this book, the R code in it, and the book's webpage.. 2 Brief Review of the Literature and Background Materials.. 2.1 Literature review.. 2.2 Interactive graphics.. 2.3 Other graphics software.. 2.4 Websites.. 2.5 Datasets.. 2.6 Statistical texts.. 3 Examining Continuous Variables.. 3.1 Introduction.. 3.2 What features might continuous variables have?.. 3.3 Looking for features.. 3.4 Comparing distributions by subgroups.. 3.5 What plots are there for individual continuous variables?.. 3.6 Plot options.. 3.7 Modelling and testing for continuous variables.. 4 Displaying Categorical Data.. 4.1 Introduction.. 4.2 What features might categorical variables have?.. 4.3 Nominal data-no fixed category order.. 4.4 Ordinal data-fixed category order.. 4.5 Discrete data-counts and integers.. 4.6 Formats, factors, estimates, and barcharts.. 4.7 Modelling and testing for categorical variables.. 5 Looking for Structure: Dependency Relationships and Associations.. 5.1 Introduction.. 5.2 What features might be visible in scatterplots?.. 5.3 Looking at pairs of continuous variables.. 5.4 Adding models: lines and smooths.. 5.5 Comparing groups within scatterplots.. 5.6 Scatterplot matrices for looking at many pairs of variables.. 5.7 Scatterplot options.. 5.8 Modelling and testing for relationships between variables.. 6 Investigating Multivariate Continuous Data.. 6.1 Introduction.. 6.2 What is a parallel coordinate plot (pcp?.. 6.3 Features you can see with parallel coordinate plots.. 6.4 Interpreting clustering results.. 6.5 Parallel coordinate plots and time series.. 6.6 Parallel coordinate plots for indices.. 6.7 Options for parallel coordinate plots.. 6.8 Modelling and testing for multivariate continuous data.. 6.9 Parallel coordinate plots and comparing model results7 Studying Multivariate Categorical Data.. 7.1 Introduction.. 7.2 Data on the sinking of the Titanic.. 7.3 What is amosaicplot?.. 7.4 Different mosaicplots for different questions of interest.. 7.5 Which mosaicplot is the right one?.. 7.6 Additional options.. 7.7 Modelling and testing for multivariate categorical data.. 8 Getting an Overview.. 8.1 Introduction.. 8.2 Many individual displays.. 8.3 Multivariate overviews.. 8.4 Multivariate overviews for categorical variables.. 8.5 Graphics by group.. 8.6 Modelling and testing for overviews.. 9 Graphics and Data Quality: How Good Are the Data?.. 9.1 Introduction.. 9.2 Missing values.. 9.3 Outliers.. 9.4 Modelling and testing for data quality.. 10 Comparisons, Comparisons, Comparisons.. 10.1 Introduction.. 10.2 Making comparisons.. 10.3 Making visual comparisons.. 10.4 Comparing group effects graphically.. 10.5 Comparing rates visually.. 10.6 Graphics for comparing many subsets.. 10.7 Graphics principles for comparisons.. 10.8 Modelling and testing for comparisons.. 11 Graphics for Time Series.. 11.1 Introduction.. 11.2 Graphics for a single time series.. 11.3 Multiple series.. 11.4 Special features of time series.. 11.5 Alternative graphics for time series.. 11.6 R classes and packages for time series.. 11.7 Modelling and testing time series.. 12 Ensemble Graphics and Case Studies.. 12.1 Introduction.. 12.2 What is an ensemble of graphics?.. 12.3 Combining different views-a case study example.. 12.4 Case studies.. 13 Some Notes on Graphics with R.. 13.1 Graphics systems in R.. 13.2 Loading datasets and packages for graphical analysis.. 13.3 Graphics conventions in statistics.. 13.4 What is a graphic anyway?.. 13.5 Options for all graphics.. 13.6 Some R graphics advice and coding tips.. 13.7 Other graphics.. 13.8 Large datasets.. 13.9 Perfecting graphics14 Summary.. 14.1 Data analysis and graphics.. 14.2 Key features of GDA.. 14.3 Strengths and weaknesses of GDA.. 14.4 Recommendations for GDA.. References.. General index.. Datasets indexGraphical 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.Visualización de la informaciónMétodos gráficosMinería de datosR (Lenguaje de programación para computadora)URN:ISBN:1498715230URN:ISBN:9781498715232 |