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Introduction to R Programming

Introduction to R Programming

  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

Highlights

  • Delivered Online

  • Two days

  • All levels

Description

Duration

2 Days

12 CPD hours

This course is intended for

Business Analysts, Technical Managers, and Programmers

Overview

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.

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.

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

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

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

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

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

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

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

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

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

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

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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