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

Additional course details:

Nexus Humans Introduction to R Programming training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Introduction to R Programming course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

FAQ for the Introduction to R Programming Course

Available Delivery Options for the Introduction to R Programming training.

• Live Instructor Led Classroom Online (Live Online)
• Traditional Instructor Led Classroom (TILT/ILT)
• Delivery at your offices in London or anywhere in the UK
• Private dedicated course as works for your staff.

How many CPD hours does the Introduction to R Programming training provide?

The 2 day. Introduction to R Programming training course give you up to 12 CPD hours/structured learning hours. If you need a letter or certificate in a particular format for your association, organisation or professional body please just ask.

What is the correct audience for the Introduction to R Programming training?

Business Analysts, Technical Managers, and Programmers

Do you provide training for the Introduction to R Programming.

Yes we provide corporate training, dedicated training and closed classes for the Introduction to R Programming. This can take place anywhere in UK including, England, Scotland, Cymru (Wales) or Northern Ireland or live online allowing you to have your teams from across UK or further afield to attend a single training event saving travel and delivery expenses.

What is the duration of the Introduction to R Programming program.

The Introduction to R Programming training takes place over 2 day(s), with each day lasting approximately 8 hours including small and lunch breaks to ensure that the delegates get the most out of the day.

Why are Nexus Human the best provider for the Introduction to R Programming?

Nexus Human are recognised as one of the best training companies as they and their trainers have won and hold many awards and titles including having previously won the Small Firms Best Trainer award, national training partner of the year for UK on multiple occasions, having trainers in the global top 30 instructor awards in 2012, 2019 and 2021. Nexus Human has also been nominated for the Tech Excellence awards multiple times. Learning Performance institute (LPI) external training provider sponsor 2024.

Is there a discount code for the Introduction to R Programming training.

Yes, the discount code PENPAL5 is currently available for the Introduction to R Programming training. Other discount codes may also be available but only one discount code or special offer can be used for each booking. This discount code is available for companies and individuals.