A Step-by- Step Tutorial in R has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R.

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Niel le Roux is an Emeritus Professor of Statistics at Stellenbosch University and Sugnet Lubbe joined University of Cape Town as Associate Professor in Statistical Sciences in 2009 after spending thirteen years as statistician in industry. Both authors have more than twenty years’ experience with the...

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A Step-by- Step Tutorial in R has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R. Emphasis is on converting ideas about analysing data into useful R programs. It is stressed throughout that programming starts first by getting a clear understanding of the problem. Once the problem is well formulated the next phase is to write step-by-step code for execution by the R evaluator. Although A Step-by-Step Tutorial in R is primarily intended as a course directed by an instructor, it can also be used with a little more effort as a self-teaching option. The first 11 chapters form the core and deal with management of R objects, workspaces, functions, graphics, data structures, subscripting, search paths, evaluation environments, vectorised programming, mapping functions, loops, error tracing and statistical modelling. The optional final chapters take a closer look at analysis of variance and covariance and optimization techniques.

- Preface
- Acknowledgement
- Introducing the R system
- Introduction
- Downloading the R system
- A quick sample R session
- R: An interpretive computer language
- A closer look at the R console
- More R basics
- Regular expressions in R: The basics
- From single instructions to sets of instructions: Introducing R functions
- R within the Windows environment
- Working with RStudio
- Using R Commander
- Activating an R project
- A note on computations by a computer
- Built-in data sets in R
- The use of .First() and .Last()
- Options
- R output (text and graphics) to Microsoft Word
- Creating PDF and HTML documents from R output: R package knitr
- Command line editing

- Introduction
- Managing objects
- Creating separate workspaces for each project
- Instructions and objects in R
- How R finds data
- The organization of data (data structures)
- Time series
- The functions as.xxx() and is.xxx()
- Simple manipulations; numbers and vectors
- Objects, their modes and attributes
- Representation of objects
- Exercise

- Creating separate workspaces for each project
- R operators and functions
- Arithmetic operators
- Logical operators
- The operators <-, <<- and ~
- Operator precedence
- Introduction to functions in R
- Some mathematical functions
- Differentiation and integration
- Exercise

- Arithmetic operators
- Introducing traditional R graphics
- General
- High-level plotting instructions
- Interactive communication with graphs
- 3D graphics: Package rgl
- Exercise

- General
- Subscripting
- Subscripting with vectors
- Subscripting with matrices
- Extracting elements of lists
- Extracting elements from dataframes
- Combining vectors, matrices, lists and dataframes
- Rearranging the elements in a matrix
- Exercise

- Subscripting with vectors
- Revision tasks
- Guidelines for problem solving by writing R code
- Exercise

- Guidelines for problem solving by writing R code
- Writing functions in R
- General
- Writing a new function
- Checking for object name clashes
- Returning multiple values
- Local variables and evaluation environments
- Cleaning up
- Variable number of arguments: argument …
- Retrieving names of arguments: Functions deparse() and substitute()
- Operators
- Replacement functions
- Default values and lazy evaluation
- The dynamic loading of external routines

- General
- Vectorized programming and mapping functions
- Mapping functions to a matrix
- Mapping functions to vectors, dataframes and lists
- The functions: mapply(), rapply() an Vectorize()
- The mapping function tapply() for grouped data
- The control of execution flow statement if-else and the control functions ifelse() and switch()
- Loops in R
- The execution time of R tasks
- The calling of functions with argument lists
- Evaluating R strings as commands
- Object-oriented programming in R
- Recursion
- Environments in R
- “Computing on the language”
- Writing user friendly functions in R: The function menu()
- Exercise
- The function on.exit()
- Error tracing
- Error handling: The function try()

- Mapping functions to a matrix
- Reading data files into R, formatting and printing
- Reading Microsoft Excel files into R
- Reading other data files into R
- Sending output to a file
- Writing R objects for transport
- The use of the file .Rhistory and the function history()
- Command re-editing
- Customized printing
- Formatting numbers
- Printing tables
- Communicating with the operating system
- Exercise

- Reading Microsoft Excel files into R
- R graphics: Round II
- Graphics parameters
- Layout of graphics
- Low-level plotting commands
- Using the plotting commands
- Exact distances in graphics
- Multiple graphics windows in R
- More complex layouts
- Dynamic 3D graphics in R
- Animation
- Exercise

- Graphics parameters
- Statistical modelling with R
- Introduction
- Data for statistical models
- Expressing a statistical model in R
- Common arguments to R modelling functions
- Using the statistical modelling objects
- Usage of the function with()
- Linear regression and anova
- The function glm()
- The function gam()
- The function loess()
- The function rpart()
- Nonlinear regression and the function nls()
- Normal quantile plot
- A coplot with two conditioning variables
- Regression diagnostics
- Experimental design
- Consider the following data

- Introduction
- Analysis of Variance and Covariance with R
- One-way ANOVA models
- Two-way ANOVA models: Main effects and interaction effects
- One-way ANCOVA models
- Maize example
- Heart rate example

- One-way ANOVA models
- Introduction to Optimization
- The bisection method for solving f (x) = 0
- The Newton-Raphson method
- The R functions optim()and constrOptim()
- Packages quadprog, lpSolve and Rsolnp for constrained optimization

- The bisection method for solving f (x) = 0
- References and Further Reading
- Endnotes
- Index

Excellent for studying computer programming.

March 24, 2019 at 4:13 PM