Introduction to R programming

Course Code: 1215

$2295

Introduction to R programming

Learn to analyze data with common statistical models

Course Code : 1215

$2295

Overview
Schedule classes
What you'll learn
Outline
Prerequisites
Who should attend

Overview

The Introduction to R programming course helps participants familiarize themselves with concepts like manipulating objects in R, such as, reading data, accessing R packages, writing R functions and creating informative graphs. The course also covers how to analyze data with the help of common statistical models and how to apply the R software on a command line as well as in a Graphical User Interface (GUI). The course combines hands-one exercises and engaging lectures to ensure that the participants get a thorough understanding of the concepts discussed.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 4 days

Live Virtual Classroom
Duration: 4 days

What You'll learn

R and available GUIs
R and statistics
Data permanency and removing objects
Objects, modes, attributes
Arrays
Matrices
Lists and dataframes
R as a set of statistical tables
Packages and namespaces

Outline

Module 1: 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

Module 2: Simple manipulations, numbers and vectors

Vectors and assignments
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

Module 3: Objects, their modes and attributes

Intrinsic attributes: modes and lengths
Changing the length of an object
Getting and setting attributes
The class of an object

Module 4: Ordered and unordered factor

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

Module 5: Arrays and matrices

Arrays
Array indexing
Sub-sections 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

Module 6: 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

Module 7: Reading data from files

The read.table() functions
The scan() function
Accessing built-in data sets
- Loading data from other R packages
Editing data

Module 8: Probability distributions

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

Module 9: Grouping, loops and conditional execution

Grouped expressions
Control statements
- Conditional execution: IF statements
- Repetitive execution: FOR loops, REPEAT and WHILE

Module 10: 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

Module 11: 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
Non-linear least squares and maximum likelihood models
- Least squares
- Maximum likelihood
Some non-standard models

Module 12: 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 annotations
- Hershey vector fonts
Interacting with graphics
Using graphics parameters
- Permanent changes: The par() function
- Temporary changes: Arguments to graphic functions
Graphic parameters list
- Graphic elements
- Axes and tick marks
- Figure margins
- Multiple figure environment
Device drivers
- PostScript diagrams for typeset documents
- Multiple graphics devices
Dynamic graphics