Beginning R [electronic resource] : An Introduction to Statistical Programming /
Pace, Larry.author.
Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, 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. R is also becoming adopted into commercial tools such as Oracle Database. 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 statistical exploration and research. Covers the freely-available R language for statistics Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done.
Title Page; Copyright Page; Contents at a Glance; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Who This Book Is For; How This Book Is Structured; Conventions; Downloading the code; Contacting the Author; CHAPTER 1 Getting R and Getting Started; Getting and Using R; A First R Session; Moving Around in R; Working with Data in R; Vectors; Creating a Vector; Performing Vector Arithmetic; Adding Elements to a Vector; Matrices; Creating a Matrix; Referring to Matrix Rows and Columns; Matrix Manipulation; Data Frames
Creating a Data Frame from VectorsCreating a Data Frame Using the R Data Editor; Reading a Table into a Data Frame; Dealing With Missing Data in R; Conclusion; CHAPTER 2 Programming in R; What is Programming?; Getting Ready to Program; The Requirements for Learning to Program; Flow Control; Looping; Conditional Statements and Branching; Essentials of R Programming; R Operators; Arithmetic Operators; Comparison Operators; Logical Operators; Input and Output in R; Understanding the R Environment; Implementation of Program Flow in R; For Loops; While and Repeat Loops; The While Loop
The Repeat LoopAvoiding Explicit Loops: The Apply Function Family; A First R Program; Another Example-Finding Pythagorean Triples; Using R to Solve Quadratic Equations; Why R is Object-Oriented; The S3 and S4 Classes; Generic Functions; Conclusion; CHAPTER 3 Writing Reusable Functions; Examining an R Function from the Base R Code; Creating a Function; Calculating a Confidence Interval for a Mean; Avoiding Loops with Vectorized Operations; Vectorizing If-Else Statements Using ifelse(); Making More Powerful Functions; Any, All, and Which; Making Functions More Useful
Confidence Intervals RevisitedConclusion; CHAPTER 4 Summary Statistics; Measuring Central Tendency; The Mean; The Median and Other Quantiles; The Mode; Measuring Location via Standard Scores; Measuring Variability; Variance and Standard Deviation; Range; Median and Mean Absolute Deviation; The Interquartile Range; The Coefficient of Variation; Covariance and Correlation; Measuring Symmetry (or Lack Thereof); Conclusion; CHAPTER 5 Creating Tables and Graphs; Frequency Distributions and Tables; Pie Charts and Bar Charts; Pie Charts; Bar Charts; Boxplots; Histograms; Line Graphs; Scatterplots
Saving and Using GraphicsConclusion; CHAPTER 6 Discrete Probability Distributions; Discrete Probability Distributions; Bernoulli Processes; The Binomial Distribution: The Number of Successes as a Random Variable; The Poisson Distribution; Relating Discrete Probability to Normal Probability; Conclusion; CHAPTER 7 Computing Normal Probabilities; Characteristics of the Normal Distribution; Finding Normal Densities Using the dnorm Function; Converting a Normal Distribution to the Standard Normal Distribution; Finding Probabilities Using the pnorm Function; Finding a Left-tailed Probability
Finding the Area Between Two z Scores
Includes index.
Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, 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. R is also becoming adopted into commercial tools such as Oracle Database. 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 statistical exploration and research. Covers the freely-available R language for statistics Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done.
Programming languages (Electronic computers).
Computer software.
R (Computer program language).
Programming Languages, Compilers, Interpreters.
Mathematical Software.