Introduction to R Programming

Price
$1,190.00
Duration
 2 Days
Delivery Methods
 VILT    Private Group
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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.

 

Upcoming Class Dates and Times

Mar 10  - Mar 11, 2022
8:00AM - 4:00PM Central
2 Days
Virtual Instructor Led
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Jun 20  - Jun 21, 2022
8:00AM - 4:00PM Central
2 Days
Virtual Instructor Led
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Who Should Attend

Business Analysts, Technical Managers, and Programmers

Course 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.

Agenda

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
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
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
  • Loading External Scripts in RStudio
  • Listing Objects in Workspace
  • Removing Objects in Workspace
  • Saving Your Workspace in R
  • Saving Your Workspace in RStudio
  • Saving Your Workspace in R GUI
  • Loading Your Workspace
  • Diverting Output to a File
  • Batch (Unattended) Processing
  • Controlling Global Options
  • Summary
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
  • Adding to a List
  • 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
5 - EXTENDING R
  • The Base R Packages
  • Loading 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
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
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
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
  • Object Masking (Shadowing) Considerations
  • Getting More Information on dplyr in RStudio
  • 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
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
  • Saving Your Work
  • Supported Export Options
  • Plots in RStudio
  • Saving a Plot as an Image
  • Summary
10 - USING R EFFICIENTLY
  • Object Memory Allocation Considerations
  • Garbage Collection
  • Finding Out About Loaded Packages
  • 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
  • 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
11 - 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
 

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