R programming

Course#: BSWA2324

About this Course

Course Type Course Code Duration
Big Data BSWA2324 2 Days

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.

Why Attend this Course?

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.

What Makes this Course Stand Apart?

Accelerator Plus1

What you will Learn?

Upon completion of this course, you will be able to:

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

Audience

Business Analysts, Technical Managers, and Programmers

Prerequisites

Participants should have the general knowledge of statistics and programming

Course Outline

1. INTRODUCTION

Installing R
Character Terminal and GUI Interfaces to R
Other GUI Integrated Development Environments

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

3. R SYNTAX:

General Notes on R Commands and Statements
Variables
Assignment Operators
Arithmetic Operators
Logical Operators

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

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

6. CONTROL STATEMENTS

Conditional Execution
Repetitive Execution

7. SCRIPTS

Creating Scripts
Loading and Executing Scripts
Batch Execution Mode

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

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

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

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

12.DATA SCIENCE ALGORITHMS AND ANALYTICAL METHODS

Supervised and Unsupervised Machine Learning Algorithms
k-Nearest Neighbors
Monte Carlo Simulation

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