Beginning R [electronic resource] : An Introduction to Statistical Programming /
Pace, Larry.author.
Wiley, Joshua.author.
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research. What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advanced techniques.
Description based upon print version of record.
Contents at a Glance; Contents; About the Author; In Memoriam; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Getting Star ted;  1.1 What is R, Anyway?;  1.2 A First R Session;  1.3 Your Second R Session;  1.3.1 Working with Indexes;  1.3.2 Representing Missing Data in R;  1.3.3 Vectors and Vectorization in R;  1.3.4 A Brief Introduction to Matrices;  1.3.5 More on Lists;  1.3.6 A Quick Introduction to Data Frames; Chapter 2: Dealing with Dates, Strings, and Data Frames;  2.1 Working with Dates and Times;  2.2 Working with Strings
Chapter 5: Functional Programming 5.1 Scoping Rules;  5.2 Reserved Names and Syntactically Correct Names;  5.3 Functions and Arguments;  5.4 Some Example Functions;  5.4.1 Guess the Number;  5.4.2 A Function with Arguments;  5.5 Classes and Methods;  5.5.1 S3 Class and Method Example;  5.5.2 S3 Methods for Existing Classes; Chapter 6: Probability Distributions;  6.1 Discrete Probability Distributions;  6.2 The Binomial Distribution;  6.2.1 The Poisson Distribution;  6.2.2 Some Other Discrete Distributions;  6.3 Continuous Probability Distributions;  6.3.1 The Normal Distribution
6.3.2 The t Distribution 6.3.3  The F distribution;  6.3.4  The Chi-Square Distribution;  References; Chapter 7: Working with Tables;  7.1 Working with One-Way Tables;  7.2 Working with Two-Way Tables; Chapter 8: Descriptive Statistics and Exploratory Data Analysis;  8.1 Central Tendency ;  8.1.1 The Mean;  8.1.2 The Median;  8.1.3 The Mode;  8.2 Variability ;  8.2.1 The Range;  8.2.2 The Variance and Standard Deviation ;  8.3 Boxplots and Stem-and-Leaf Displays ;  8.4 Using the fBasics Package for Summary Statistics;  References; Chapter 9: Working with Graphics
9.1 Creating Effective Graphics 9.2 Graphing Nominal and Ordinal Data;  9.3 Graphing Scale Data;  9.3.1 Boxplots Revisited ;  9.3.2 Histograms and Dotplots;  9.3.3 Frequency Polygons and Smoothed Density Plots;  9.3.4 Graphing Bivariate Data;  References; Chapter 10: Traditional Statistical Methods;  10.1 Estimation and Confidence Intervals;  10.1.1 Confidence Intervals for Means;  10.1.2 Confidence Intervals for Proportions;  10.1.3 Confidence Intervals for the Variance;  10.2 Hypothesis Tests with One Sample;  10.3 Hypothesis Tests with Two Samples;  References
Chapter 11: Modern Statistical Methods
Includes bibliographical references and index.
Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research. What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advanced techniques.
Programming languages (Electronic computers).
Computer software.
R (Computer program language).
Programming Languages, Compilers, Interpreters.
Mathematical Software.