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R Programming from the Ground Up

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...

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By Request
$1,360 USD
Duration 2 days
Course Code WA2711
Available Formats Classroom

Overview

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. 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.

Skills Gained

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

Who Can Benefit

Business Analysts, Technical Managers, and Programmers

Prerequisites

Participants should have the general knowledge of statistics and programming

Course Details

Outline

Chapter 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

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 R
  • Vector 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
  • 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

Chapter 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

Chapter 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

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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Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Good Course. We covered a lot of material in a short amount of time. This course had useful labs that built upon each other.

Class was very informative, although one lab didnt but will try again later

Thank Tech Data for sponsoring this course you really take care of your partners.

ExitCertified gave me some good trainings and I got to learn through doing labs.

Overall ExitCertified is a great training provider and the remote learning is as effective as in person.