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.
Description
Content
Reviews
- 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.