1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、課程完成后,授課老師留給學員手機和Email,保障培訓效果,免費提供半年的技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

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