# Introduction to R Training Course

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Node ID: 14197

21 hours

## Requirements

Good understanding of statistics.

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## Overview

Forecasters, statisticians, managers, analysts who want to use R software http://www.r-project.org/.

It shows how use the software in available GUI's and command line.

## Course Outline

### 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

• The scan() function
• Accessing builtin datasets
• 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

• Simple examples
• Defining new binary operators
• Named arguments and defaults
• The '...' argument
• Assignments within functions
• 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