R PROGRAMMING TRAINING – GTR1

Course Description

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.
 

OBJECTIVES

This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.

 

TOPICS

High octane introduction to R programming

Learning about R data structures

Working with R functions

Statistical data analysis with R

Supervised and unsupervised machine learning with R

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Duration

2 Days

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Target Audience

Business Analysts, Technical Managers, and Programmers

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Prerequisites

Participants should have the general knowledge of statistics and programming.

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Suggested Follow on Courses

There are a number of options, please contact us for further details.

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Course Content

CHAPTER 1. INTRODUCTION

Installing R

Character Terminal and GUI Interfaces to R

Other GUI Integrated Development Environments

CHAPTER 2. WORKING WITH R

Running R

Learning GUI Integrated Development Environment

Interacting with R Interpreter

R Sessions and Workspaces

Saving Your Workspace

Loading Your Workspace

Removing Objects in Workspace

Getting Help

Getting System Information

Standard R Packages

Loading Packages

CRAN (The Comprehensive R Archive Network)

Extending R

CHAPTER 3. R SYNTAX

General Notes on R Commands and Statements

Variables

Assignment Operators

Arithmetic Operators

Logical Operators

CHAPTER 4. R DATA STRUCTURES

R Objects

Vectors

Logical Vectors

Character Vectors

Creating and Working with Vectors

Lists

Creating and Working with Lists

Matrices

Creating and Working with Matrices

Data Frames

Creating and Working with Data Frames

Interactive Creation of Data Frames

Getting Info about a Data Frame

Sorting Data in Data Frames

Matrices vs Data Frames

CHAPTER 5. FUNCTIONS

Using R Common Functions

Numeric Functions

Character / String Functions

Date and Time Functions

Other Useful Functions

Applying Functions to Matrices and Data Frames

Type Conversion

Creating and Using User-Defined Functions

CHAPTER 6. CONTROL STATEMENTS

Conditional Execution

Repetitive Execution

CHAPTER 7. SCRIPTS

Creating Scripts

Loading and Executing Scripts

Batch Execution Mode

CHAPTER 8. INPUT / OUTPUT

Reading Data from Files

Writing Data to Files

Getting the List of Files in a Directory

Diverting System Output to a File

CHAPTER 9. DATA IMPORT AND EXPORT

Import and Export Operations in R

Working with CSV Files

Reading Data from Excel

Exporting Data in SPSS Data Format

CHAPTER 10. R STATISTICAL COMPUTING FEATURES

Basic Statistical Functions

Writing Your Own skew and kurtosis Functions

Generating Normally Distributed Random Numbers

Generating Uniformly Distributed Random Numbers

Using the summary() Function

Math Functions Used in Data Analysis

Correlations

Testing Correlation Coefficient for Significance

Regression Analysis

Types of Regression

Simple Linear Regression Model

Least-Squares Method (LSM)

LSM Assumptions

Fitting Linear Regression Models in R

Confidence Intervals for Model Parameters

Multiple Regression Analysis

Finding the Best-Fitting Regression Model

Comparing Regression Models with anova and AIC

CHAPTER 11. DATA VISUALIZATION

R Graphics

Graphics Export Options

Creating Bar Plots in R

Using barplot() with Matrices

Stacked vs Juxtaposed Layouts

Customizing Plots

Histograms

Building Histograms with hist()

Pie Charts

Generic X-Y Plotting

Dot Plots

CHAPTER 12. DATA SCIENCE ALGORITHMS AND ANALYTICAL METHODS

Supervised and Unsupervised Machine Learning Algorithms

k-Nearest Neighbors

Monte Carlo Simulation

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