R by example
(eBook)
Author:
Series:
Published:
New York : Springer, ©2012.
Format:
eBook
ISBN:
9781461413653, 1461413656, 1461413648, 9781461413646
Content Description:
1 online resource (xiii, 359 pages) : illustrations
Status:
Available Online
Description
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
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APA Citation (style guide)
Albert, J., & Rizzo, M. L. (2012). R by example. New York, Springer.
Chicago / Turabian - Author Date Citation (style guide)Albert, Jim, 1953- and Maria L. Rizzo. 2012. R By Example. New York, Springer.
Chicago / Turabian - Humanities Citation (style guide)Albert, Jim, 1953- and Maria L. Rizzo, R By Example. New York, Springer, 2012.
MLA Citation (style guide)Albert, Jim and Maria L Rizzo. R By Example. New York, Springer, 2012.
Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Language:
English
Notes
Bibliography
Includes bibliographical references (pages 349-351) and index.
Description
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
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Grouped Work ID:
6fe0c596-1529-668b-137e-b170c557cf1b
Record Information
| Last Sierra Extract Time | Apr 05, 2024 08:52:04 AM |
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| Last File Modification Time | Apr 05, 2024 08:58:05 AM |
| Last Grouped Work Modification Time | Apr 05, 2024 08:52:37 AM |
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| 245 | 1 | 0 | |a R by example /|c Jim Albert, Maria Rizzo. |
| 264 | 1 | |a New York :|b Springer,|c ©2012. | |
| 300 | |a 1 online resource (xiii, 359 pages) :|b illustrations | ||
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| 490 | 1 | |a Use R! | |
| 504 | |a Includes bibliographical references (pages 349-351) and index. | ||
| 505 | 0 | |a R by Example; Preface; Contents; Notation and Abbreviations; Chapter 1 Introduction; 1.1 Getting Started; 1.1.1 Preliminaries; 1.1.2 Basic operations; 1.1.3 R Scripts; 1.1.4 The R Help System; 1.2 Functions; 1.3 Vectors and Matrices; 1.4 Data Frames; 1.4.1 Introduction to data frames; Display all data; Display top of data; Sample size and dimension; Names of variables; Structure of the data; Missing values; 1.4.2 Working with a data frame; Compute summary statistics; Extract data from a data frame; Extract a variable using ; Histograms; Attaching a data frame; Scatterplots and correlations. | |
| 505 | 8 | |a 1.5 Importing Data1.5.1 Entering data manually; 1.5.2 Importing data from a text file; 1.12 The simplest way to import spreadsheet data is to save it in .csv; 1.5.3 Data available on the internet; 1.6 Packages; Searching for data or methods in packages; 1.7 The R Workspace; 1.8 Options and Resources; The options function; Graphical parameters: par; 1.13 There are so many possible parameters to graphics functions, that; Graph history; Other resources; Task Views; External resources; The R Graph Gallery and R Graphical Manual; 1.9 Reports and Reproducible Research; Exercises. | |
| 505 | 8 | |a Chapter 2 Quantitative Data2.1 Introduction; 2.2 Bivariate Data: Two Quantitative Variables; 2.2.1 Exploring the data; Body and brain size of mammals; 2.1 In the display above it is not obvious whether mammals is a matrix; Some basic statistics and plots; 2.2 We have seen that mammals consists of two numeric variables, body; 2.2.2 Correlation and regression line; 2.3 A fitted line was added to the scatterplot in Figure 2.3 by the code; 2.2.3 Analysis of bivariate data by group; IQ of twins; 2.2.4 Conditional plots; 2.3 Multivariate Data: Several Quantitative Variables. | |
| 505 | 8 | |a 2.3.1 Exploring the data2.3.2 Missing values; 2.3.3 Summarize by group; 2.3.4 Summarize pairs of variables; 2.3.5 Identifying missing values; 2.4 Time Series Data; 2.5 Integer Data: Draft Lottery; 2.6 Sample Means and the Central Limit Theorem; 2.7 Special Topics; 2.7.1 Adding a new variable; 2.7.2 Which observation is the maximum?; 2.7.3 Sorting a data frame; 2.7.4 Distances between points; 2.7.5 Quick look at cluster analysis; Exercises; Chapter 3 Categorical data; 3.1 Introduction; 3.1.1 Tabulating and plotting categorical data; 3.1.2 Character vectors and factors. | |
| 505 | 8 | |a 3.2 Chi-square Goodness-of-Fit Test3.3 Relating Two Categorical Variables; 3.3.1 Introduction; 3.3.2 Frequency tables and graphs; 3.3.3 Contingency tables; 3.4 Association Patterns in Contingency Tables; 3.4.1 Constructing a contingency table; 3.4.2 Graphing patterns of association; 3.5 Testing Independence by a Chi-square Test; Exercises; Chapter 4 Presentation Graphics; 4.1 Introduction; 4.2 Labeling the Axes and Adding a Title; 4.3 Changing the Plot Type and Plotting Symbol; 4.4 Overlaying Lines and Line Types; 4.5 Using Different Colors for Points and Lines. | |
| 520 | |a R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R. | ||
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