Introduction to R programming

Course Code: 1215

$2195

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

Delivery Format
Starting Date
Starting Time
Duration

Live Classroom
Wednesday, 4 November 2020
10:00 AM - 6:00 PM EST
3 Days

$2195 Add to cart

Delivery Format
Starting Date
Starting Time
Duration

Live Classroom
Monday, 14 December 2020
10:00 AM - 6:00 PM EST
3 Days

$2195 Add to cart

Looking for more sessions of this class?

Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 3 days

Live Virtual Classroom
Duration: 3 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 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

Module 3: 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

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. 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

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
Nonlinear 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 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