課程大綱： R培訓(xùn)
Day 1
Introduction and preliminaries
Making R more friendly, R and available GUIs
Rstudio
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
Day 2
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
Data manipulation
Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Character manipulation, stringr package
Reading data
Txt files
CSV files
XLS, XLSX files
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats
Accessing data from databases using SQL language
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
Day 3
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 analysis in R
Linear regression models
Generic functions for extracting model information
Updating fitted models
Generalized linear models
Families
The glm() function
Classification
Logistic Regression
Linear Discriminant Analysis
Unsupervised learning
Principal Components Analysis
Clustering Methods (k-means, hierarchical clustering, k-medoids)
Survival analysis
Survival objects in r
Kaplan-Meier estimate
Confidence bands
Cox PH models, constant covariates
Cox PH models, time-dependent covariates
Graphical procedures
High-level plotting commands
The plot() function
Displaying multivariate data
Display graphics
Arguments to high-level plotting functions
Basic visualisation graphs
Multivariate relations with lattice and ggplot package
Using graphics parameters
Graphics parameters list
Automated and interactive reporting
Combining output from R with text
Creating html, pdf documents
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