R in action: (Record no. 5015)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 07331nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230811144101.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230322b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789351198079 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.502855133 |
| Item number | KAB |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kabacoff, Robert L. |
| 245 ## - TITLE STATEMENT | |
| Title | R in action: |
| Remainder of title | data analysis and graphics with R |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Dreamtech Publisher |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxxviii, 579 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 949.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/>Part 1 Getting started<br/><br/> <br/><br/>1 Introduction to R<br/><br/> 1.1 Why use R?<br/><br/> 1.2 Obtaining and installing R<br/><br/> 1.3 Working with R<br/><br/> 1.4 Packages<br/><br/> 1.5 Batch processing<br/><br/> 1.6 Using output as input: reusing results<br/><br/> 1.7 Working with large datasets<br/><br/> 1.8 Working through an example<br/><br/> 1.9 Summary<br/><br/> <br/><br/>2 Creating a dataset<br/><br/> 2.1 Understanding datasets<br/><br/> 2.2 Data structures<br/><br/> 2.3 Data input<br/><br/> 2.4 Annotating datasets<br/><br/> 2.5 Useful functions for working with data objects<br/><br/> 2.6 Summary<br/><br/> <br/><br/>3 Getting started with graphs<br/><br/> 3.1 Working with graphs<br/><br/> 3.2 A simple example<br/><br/> 3.3 Graphical parameters<br/><br/> 3.4 Adding text, customized axes and legends<br/><br/> 3.5 Combining graphs<br/><br/> 3.6 Summary<br/><br/> <br/><br/>4 Basic data management<br/><br/> 4.1 A working example<br/><br/> 4.2 Creating new variables<br/><br/> 4.3 Recoding variables<br/><br/> 4.4 Renaming variables<br/><br/> 4.5 Missing values<br/><br/> 4.6 Date values<br/><br/> 4.7 Type conversions<br/><br/> 4.8 Sorting data<br/><br/> 4.9 Merging datasets<br/><br/> 4.10 Subsetting datasets<br/><br/> 4.11 Using SQL statements to manipulate data frames<br/><br/> 4.12 Summary<br/><br/> <br/><br/>5 Advanced data management<br/><br/> 5.1 A data-management challenge<br/><br/> 5.2 Numerical and character functions<br/><br/> 5.3 A solution for the data-management challenge<br/><br/> 5.4 Control flow<br/><br/> 5.5 User-written functions<br/><br/> 5.6 Aggregation and reshaping<br/><br/> 5.7 Summary<br/><br/> <br/><br/>Part 2 Basic methods<br/><br/> <br/><br/>6 Basic graphs<br/><br/> 6.1 Bar plots<br/><br/> 6.2 Pie charts<br/><br/> 6.3 Histograms<br/><br/> 6.4 Kernel density plots<br/><br/> 6.5 Box plots<br/><br/> 6.6 Dot plots<br/><br/> 6.7 Summary<br/><br/> <br/><br/>7 Basic statistics<br/><br/> 7.1 Descriptive statistics<br/><br/> 7.2 Frequency and contingency tables<br/><br/> 7.3 Correlations<br/><br/> 7.4 T-tests<br/><br/> 7.5 Nonparametric tests of group differences<br/><br/> 7.6 Visualizing group differences<br/><br/> 7.7 Summary<br/><br/> <br/><br/>Part 3 Intermediate methods<br/><br/> <br/><br/>8 Regression<br/><br/> 8.1 The many faces of regression<br/><br/> 8.2 OLS regression<br/><br/> 8.3 Regression diagnostics<br/><br/> 8.4 Unusual observations<br/><br/> 8.5 Corrective measures<br/><br/> 8.6 Selecting the “best” regression model<br/><br/> 8.7 Taking the analysis further<br/><br/> 8.8 Summary<br/><br/> <br/><br/>9 Analysis of variance<br/><br/> 9.1 A crash course on terminology<br/><br/> 9.2 Fitting ANOVA models<br/><br/> 9.3 One-way ANOVA<br/><br/> 9.4 One-way ANCOVA<br/><br/> 9.5 Two-way factorial ANOVA<br/><br/> 9.6 Repeated measures ANOVA<br/><br/> 9.7 Multivariate analysis of variance (MANOVA)<br/><br/> 9.8 ANOVA as regression<br/><br/> 9.9 Summary<br/><br/> <br/><br/>10 Power analysis<br/><br/> 10.1 A quick review of hypothesis testing<br/><br/> 10.2 Implementing power analysis with the pwr package<br/><br/> 10.3 Creating power analysis plots<br/><br/> 10.4 Other packages<br/><br/> 10.5 Summary<br/><br/> <br/><br/>11 Intermediate graphs<br/><br/> 11.1 Scatter plots<br/><br/> 11.2 Line charts<br/><br/> 11.3 Corrgrams<br/><br/> 11.4 Mosaic plots<br/><br/> 11.5 Summary<br/><br/> <br/><br/>12 Resampling statistics and bootstrapping<br/><br/> 12.1 Permutation tests<br/><br/> 12.2 Permutation tests with the coin package<br/><br/> 12.3 Permutation tests with the lmPerm package<br/><br/> 12.4 Additional comments on permutation tests<br/><br/> 12.5 Bootstrapping<br/><br/> 12.6 Bootstrapping with the boot package<br/><br/> 12.7 Summary<br/><br/> <br/><br/>Part 4 Advanced methods<br/><br/> <br/><br/>13 Generalized linear models<br/><br/> 13.1 Generalized linear models and the glm() function<br/><br/> 13.2 Logistic regression<br/><br/> 13.4 Poisson regression<br/><br/> 13.5 Summary<br/><br/> <br/><br/>14 Principal components and factor analysis<br/><br/> 14.1 Principal components and factor analysis in R<br/><br/> 14.2 Principal components<br/><br/> 14.3 Exploratory factor analysis<br/><br/> 14.4 Other latent variable models<br/><br/> 14.5 Summary<br/><br/> <br/><br/>15 Time series<br/><br/> 15.1 Creating a time-series object in R<br/><br/> 15.2 Smoothing and seasonal decomposition<br/><br/> 15.3 Exponential forecasting models<br/><br/> 15.4 ARIMA forecasting models<br/><br/> 15.5 Going further<br/><br/> 15.6 Summary<br/><br/> <br/><br/>16 Cluster analysis<br/><br/> 16.1 Common steps in cluster analysis<br/><br/> 16.2 Calculating distances<br/><br/> 16.3 Hierarchical cluster analysis<br/><br/> 16.4 Partitioning cluster analysis<br/><br/> 16.5 Avoiding nonexistent clusters<br/><br/> 16.6 Summary<br/><br/> <br/><br/>17 Classification<br/><br/> 17.1 Preparing the data<br/><br/> 17.2 Logistic regression<br/><br/> 17.3 Decision trees<br/><br/> 17.4 Random forests<br/><br/> 17.5 Support vector machines<br/><br/> 17.6 Choosing a best predictive solution<br/><br/> 17.7 Using the rattle package for data mining<br/><br/> 17.8 Summary<br/><br/> <br/><br/>18 Advanced methods for missing data<br/><br/> 18.1 Steps in dealing with missing data<br/><br/> 18.2 Identifying missing values<br/><br/> 18.3 Exploring missing-values patterns<br/><br/> 18.4 Understanding the sources and impact of missing data<br/><br/> 18.5 Rational approaches for dealing with incomplete data<br/><br/> 18.6 Complete-case analysis (listwise deletion)<br/><br/> 18.7 Multiple imputation<br/><br/> 18.8 Other approaches to missing data<br/><br/> 18.9 Summary<br/><br/> <br/><br/> <br/><br/>Part 5 Expanding your skills<br/><br/> <br/><br/>19 Advanced graphics with ggplot2<br/><br/> 19.1 The four graphics systems in R<br/><br/> 19.2 An introduction to the ggplot2 package<br/><br/> 19.3 Specifying the plot type with geoms<br/><br/> 19.4 Grouping<br/><br/> 19.5 Faceting<br/><br/> 19.6 Adding smoothed lines<br/><br/> 19.7 Modifying the appearance of ggplot2 graphs<br/><br/> 19.8 Saving graphs<br/><br/> 19.9 Summary<br/><br/> <br/><br/>20 Advanced programming<br/><br/> 20.1 A review of the language<br/><br/> 20.2 Working with environments<br/><br/> 20.3 Object-oriented programming<br/><br/> 20.4 Writing efficient code<br/><br/> 20.5 Debugging<br/><br/> 20.6 Going further<br/><br/> 20.7 Summary<br/><br/> <br/><br/>21 Creating a package<br/><br/> 21.1 Nonparametric analysis and the npar package<br/><br/> 21.2 Developing the package<br/><br/> 21.3 Creating the package documentation<br/><br/> 21.4 Building the package<br/><br/> 21.5 Going further<br/><br/> 21.6 Summary<br/><br/> <br/><br/>22 Creating dynamic reports<br/><br/> 22.1 A template approach to reports<br/><br/> 22.2 Creating dynamic reports with R and Markdown<br/><br/> 22.3 Creating dynamic reports with R and LaTeX<br/><br/> 22.4 Creating dynamic reports with R and Open Document<br/><br/> 22.5 Creating dynamic reports with R and Microsoft Word<br/><br/> 22.6 Summary<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining and dynamic report writing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | R (Computer program language) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3162 | 16-02-2023 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/22/2023 | Technical Bureau India Pvt. Ltd. | 664.30 | 519.502855133 KAB | 004874 | 03/22/2023 | 1 | 949.00 | 03/22/2023 | Book |