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R Programming from the Ground Up WA2711 - Course Book product photo Front View EL

Delivery Information:

You will receive required software set up for install 48 hours from time of purchase.

Version 1.0 

Product Type: Courseware
Level: Foundation
Duration: 2 Days

Participants should have the general knowledge of statistics and programming

Language: English (en-US)
Delivery Format: eBook

Delivery Information

Delivered as a voucher. You can access the vouchers and assign them from Active Vouchers on myLeapest or you can use Classes function to assign the vouchers to a group of learners.

Product Content

This product contains the following items. Upon purchasing, you will get access to all available version prior to the latest version.

Course Description :

Delivery Information:

You will receive required software set up for install 48 hours from time of purchase.

Version 1.0 

Course Outline :

Chapter 1.

  • What is R?
  • Positioning of R in the Data Science Space
  • The Legal Aspects
  • Microsoft R
  • OpenR Integrated Development Environments
  • Running RRunning
  • RStudio
  • Getting HelpGeneral Notes on R Commands and Statements
  • Assignment OperatorsR 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 RVector 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
  • 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

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
  • 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

Chapter 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

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
  • 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

Chapter 9.

  • Data Visualization in R
  • Data Visualization
  • The ggplot2 Data Visualization Package
  • Creating Bar Plots in R
  • Creating Horizontal Bar Plots in R

Target Audience :

Business Analysts, Technical Managers, and Programmers

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.

Course Agenda :

  • High octane introduction to R programming
  • Learning about R data structures
  • Working with R functions
  • Statistical data analysis with R

R Programming from the Ground Up WA2711 - Course Book

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