03/27/2023 - 03/28/2023
10:00 AM - 06:00 PM Online Virtual Class
USD \$1,360.00
05/15/2023 - 05/16/2023
10:00 AM - 06:00 PM Online Virtual Class
USD \$1,360.00
06/19/2023 - 06/20/2023
10:00 AM - 06:00 PM Online Virtual Class
USD \$1,360.00

## 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

## AUDIENCE

Business Analysts, Technical Managers, and Programmers

## PREREQUISITES

Participants should have the general knowledge of statistics and programming

2 Days

## Chapter 1. What is R

• What is R?
• Positioning of R in the Data Science Space
• The Legal Aspects
• Microsoft R Open
• R Integrated Development Environments
• Running R
• Running RStudio
• Getting Help
• General Notes on R Commands and Statements
• Assignment Operators
• R Core Data Structures
• Assignment Example
• R Objects and Workspace
• Printing Objects
• Arithmetic Operators
• Logical Operators
• System Date and Time
• Operations
• User-defined Functions
• Control Statements
• Conditional Execution
• Repetitive Execution
• Repetitive execution
• Built-in Functions
• Summary

## Chapter 2. Introduction to Functional Programming with R

• What is Functional Programming (FP)?
• Terminology: Higher-Order Functions
• A Short List of Languages that Support FP
• Functional Programming in R
• Vector and Matrix Arithmetic
• Vector Arithmetic Example
• More Examples of FP in R
• Summary

## Chapter 3. Managing Your Environment

• Getting and Setting the Working Directory
• Getting the List of Files in a Directory
• The R Home Directory
• Executing External R commands
• Listing Objects in Workspace
• Removing Objects in Workspace
• Saving Your Workspace in R
• Saving Your Workspace in RStudio
• Saving Your Workspace in R GUI
• Diverting Output to a File
• Batch (Unattended) Processing
• Controlling Global Options
• Summary

## Chapter 4. R Type System and Structures

• The R Data Types
• System Date and Time
• Formatting Date and Time
• Using the mode() Function
• R Data Structures
• What is the Type of My Data Structure?
• Creating Vectors
• Logical Vectors
• Character Vectors
• Factorization
• Multi-Mode Vectors
• The Length of the Vector
• Getting Vector Elements
• Lists
• A List with Element Names
• Extracting List Elements
• Matrix Data Structure
• Creating Matrices
• Creating Matrices with cbind() and rbind()
• Working with Data Frames
• Matrices vs Data Frames
• A Data Frame Sample
• Creating a Data Frame
• Accessing Data Cells
• Getting Info About a Data Frame
• Selecting Columns in Data Frames
• Selecting Rows in Data Frames
• Getting a Subset of a Data Frame
• Sorting (ordering) Data in Data Frames by Attribute(s)
• Editing Data Frames
• The str() Function
• Type Conversion (Coercion)
• The summary() Function
• Checking an Object's Type
• Summary

## Chapter 5. Extending R

• The Base R Packages
• What is the Difference between Package and Library?
• Extending R
• The CRAN Web Site
• Extending R in R GUI
• Extending R in RStudio
• Installing and Removing Packages from Command-Line
• Summary

## Chapter 6. Read-Write and Import-Export Operations in R

• Reading Data from a File into a Vector
• Example of Reading Data from a File into A Vector
• Writing Data to a File
• Example of Writing Data to a File
• Reading Data into A Data Frame
• Writing CSV Files
• Importing Data into R
• Exporting Data from R
• Summary

## Chapter 7. Statistical Computing Features in R

• Statistical Computing Features
• Descriptive Statistics
• Basic Statistical Functions
• Examples of Using Basic Statistical Functions
• Non-uniformity of a Probability Distribution
• 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
• Examples of Using Math Functions
• Correlations
• Correlation Example
• Testing Correlation Coefficient for Significance
• The cor.test() Function
• The cor.test() Example
• Regression Analysis
• Types of Regression
• Simple Linear Regression Model
• Least-Squares Method (LSM)
• LSM Assumptions
• Fitting Linear Regression Models in R
• Example of Using lm()
• Confidence Intervals for Model Parameters
• Example of Using lm() with a Data Frame
• Regression Models in Excel
• Multiple Regression Analysis
• Summary

## Chapter 8. Data Manipulation and Transformation in R

• Applying Functions to Matrices and Data Frames
• The apply() Function
• Using apply()
• Using apply() with a User-Defined Function
• apply() Variants
• Using tapply()
• Adding a Column to a Data Frame
• Dropping A Column in a Data Frame
• The attach() and detach() Functions
• Sampling
• Using sample() for Generating Labels
• Set Operations
• Example of Using Set Operations
• The dplyr Package
• The search() or searchpaths() Functions
• Handling Large Data Sets in R with the data.table Package
• The fread() and fwrite() functions from the data.table Package
• Using the Data Table Structure
• Summary

## Chapter 9. Data Visualization in R

• Data Visualization
• Data Visualization in R
• The ggplot2 Data Visualization Package
• Creating Bar Plots in R
• Creating Horizontal Bar Plots
• Using barplot() with Matrices
• Using barplot() with Matrices Example
• Customizing Plots
• Histograms in R
• Building Histograms with hist()
• Example of using hist()
• Pie Charts in R
• Examples of using pie()
• Generic X-Y Plotting
• Examples of the plot() function
• Dot Plots in R
• Supported Export Options
• Plots in RStudio
• Saving a Plot as an Image
• Summary

## Chapter 10. Using R Efficiently

• Object Memory Allocation Considerations
• Garbage Collection
• Using the conflicts() Function
• Getting Information About the Object Source Package with the pryr Package
• Using the where() Function from the pryr Package
• Timing Your Code with system.time()
• Timing Your Code with System.time()
• Sleeping a Program
• Handling Large Data Sets in R with the data.table Package
• Passing System-Level Parameters to R
• Summary

## Lab Exercises

Lab 1. Getting Started with R
Lab 2. Learning the R Type System and Structures
Lab 3. Read and Write Operations in R
Lab 4. Data Import and Export in R
Lab 5. k-Nearest Neighbors Algorithm
Lab 6. Creating Your Own Statistical Functions
Lab 7. Simple Linear Regression
Lab 8. Monte-Carlo Simulation (Method)
Lab 9. Data Processing with R
Lab 10. Using R Graphics Package
Lab 11. Using R Efficiently