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

Course Images

Introduction to R Programming

By Nexus Human

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Highlights