Introduction to R培训

Introduction and preliminaries

• Making R more friendly, R and available GUIs
• The R environment
• Related software and documentation
• R and statistics
• Using R interactively
• An introductory session
• Getting help with functions and features
• R commands, case sensitivity, etc.
• Recall and correction of previous commands
• Executing commands from or diverting output to a file
• Data permanency and removing objects

Simple manipulations; numbers and vectors

• Vectors and assignment
• Vector arithmetic
• Generating regular sequences
• Logical vectors
• Missing values
• Character vectors
• Index vectors; selecting and modifying subsets of a data set
• Other types of objects

Objects, their modes and attributes

• Intrinsic attributes: mode and length
• Changing the length of an object
• Getting and setting attributes
• The class of an object

Ordered and unordered factors

• A specific example
• The function tapply() and ragged arrays
• Ordered factors

Arrays and matrices

• Arrays
• Array indexing. Subsections of an array
• Index matrices
• The array() function
  • Mixed vector and array arithmetic. The recycling rule
• The outer product of two arrays
• Generalized transpose of an array
• Matrix facilities
  • Matrix multiplication
  • Linear equations and inversion
  • Eigenvalues and eigenvectors
  • Singular value decomposition and determinants
  • Least squares fitting and the QR decomposition
• Forming partitioned matrices, cbind() and rbind()
• The concatenation function, (), with arrays
• Frequency tables from factors

Lists and data frames

• Lists
• Constructing and modifying lists
  • Concatenating lists
• Data frames
  • Making data frames
  • attach() and detach()
  • Working with data frames
  • Attaching arbitrary lists
  • Managing the search path

Reading data from files

• The read.table()function
• The scan() function
• Accessing builtin datasets
  • Loading data from other R packages
• Editing data

Probability distributions

• R as a set of statistical tables
• Examining the distribution of a set of data
• One- and two-sample tests

Grouping, loops and conditional execution

• Grouped expressions
• Control statements
  • Conditional execution: if statements
  • Repetitive execution: for loops, repeat and while

Writing your own functions

• Simple examples
• Defining new binary operators
• Named arguments and defaults
• The '...' argument
• Assignments within functions
• More advanced examples
  • Efficiency factors in block designs
  • Dropping all names in a printed array
  • Recursive numerical integration
• Scope
• Customizing the environment
• Classes, generic functions and object orientation

Statistical models in R

• Defining statistical models; formulae
  • Contrasts
• Linear models
• Generic functions for extracting model information
• Analysis of variance and model comparison
  • ANOVA tables
• Updating fitted models
• Generalized linear models
  • Families
  • The glm() function
• Nonlinear least squares and maximum likelihood models
  • Least squares
  • Maximum likelihood
• Some non-standard models

Graphical procedures

• High-level plotting commands
  • The plot() function
  • Displaying multivariate data
  • Display graphics
  • Arguments to high-level plotting functions
• Low-level plotting commands
  • Mathematical annotation
  • Hershey vector fonts
• Interacting with graphics
• Using graphics parameters
  • Permanent changes: The par() function
  • Temporary changes: Arguments to graphics functions
• Graphics parameters list
  • Graphical elements
  • Axes and tick marks
  • Figure margins
  • Multiple figure environment
• Device drivers
  • PostScript diagrams for typeset documents
  • Multiple graphics devices
• Dynamic graphics

Packages

• Standard packages
• Contributed packages and CRAN
• Namespaces