Advanced R Course

Learn the concepts of R from scratch & become a master in R Programming

  • 24 hours of Instructor led Training
  • Comprehensive hands-on Coverage on Basic and Advanced R
  • Master Data Visualization with R
  • Learn more with Real-Life Use Cases
Group Discount

Description

R is one of the leading statistical programming languages used by statisticians and data scientists.
Part of the reason for its popularity is that it is sophisticated, versatile and flexible and has uses in a variety of fields, be it engineering, business, medicine or science. R allows data analysis in a variety of methods and also has capabilities to produce a range of graphics including charts, plots, and graphs that can be used for presentations. This course offers an expert’s eye overview of how these advanced tasks fit together in R as a whole along with practical examples.

You will learn about the primary functions of R such as its installation and how to use it for data analyses and manipulation. Through hands-on exercises and in-depth coaching, you will learn about R data structures, basic R commands, how to use graphics, writing R functions and R flow control structures.

What you will learn

Prerequisites

Participants are expected to have basic programming knowledge

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Who should Attend?

  • Those interested in the field of data science and want to learn R programming
  • Those looking for a more robust, structured learning program
  • Software or Data Engineers interested in learning R Programming

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied in the real world.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what is R, and why R is such a popular tool among Data Scientists.

Topics Covered:

  • What is R?               
  • Why is it in demand?

Hands-on: No hands-on

Learning Objectives:

In this module you will learn to install R and its components, install and load R libraries and learn about the frequently used libraries.

Topics Covered:

  • Installation of R - step by step
  • Installing Libraries                 
  • Getting to know important Libraries

Hands-on:

Know how to install R, R Studio and other libraries.

Learning Objectives:
Learn about various data structures in R.

Topics Covered:

  • List
  • Vectors
  • Arrays
  • Matrices
  • Factors
  • String
  • Data Frames

Hands-on:

Write R Code to understand and implement R Data Structures.

Learning Objectives:

Learn all about loops and control statements in R.

Topics Covered:

  • For Loop
  • While Loop
  • Break Statement
  • Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement

Hands-on:

Write R Code to implement loop and control structures in R.

Learning Objectives:

Learn how to write custom functions, nested functions and functions with arguments.

Topics Covered:

  • Writing your own functions (UDF)
  • Calling R Functions
  • Nested Function Calls in R
  • Functions with Arguments
  • Calling R Functions by passing Arguments

Hands-on:

Write R Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required.

Learning Objectives:

Lean all about loop functions available in R which are efficient and can be written with a single command.

Topics Covered:

  • apply           
  • lapply           
  • sapply
  • mapply     
  • tapply

Hands-on:

Write R Code to implement various types of apply functions and understand their usage.

Learning Objectives:

Learn all about string manipulations and regular expressions. The functions can be extremely useful for text or unstructured data manipulations.

Topics Covered:

  • stringr()         
  • grep() & grepl()         
  • regexpr() & gregexpr()           
  • regexec()                 
  • sub() & gsub()  

Hands-on:

Write R Code for string manipulation and handle regular expression.

Learning Objectives:

Learn how to import data from various sources in R. Also learn how to write files from R and connect to various databases from R.

Topics Covered:

  • Reading data files in R
  • Reading data files from other Statistical Software
  • Writing files in R
  • Connecting to Databases from R
  • Data Manipulation & Analysis

Hands-on:

Write R Code to read and write data from/to R. Read data not only from CSV files but also using direct connection to various databases.

Learning Objectives:

Learn how to apply various data processing functions in R. These operations can be useful to describe data and perform certain operations on it. This will help you to take necessary steps for further analysis.

Topics Covered:

  • Pipe operator for data processing
  • Using the dplyr verbs
  • Using the customized function within the dplyr verbs
  • Using the select verb for data processing
  • Using the filter verb for data processing
  • Using the arrange verb for data processing
  • Using mutate for data processing
  • Using summarise to summarize dataset

Hands-on:

Write R code to apply various functions in R in order to process data.

Learning Objectives:

Learn how to handle data from different sources and different data formats

Topics Covered:

  • Extracting unstructured text data from a plain web page
  • Extracting text data from an HTML page
  • Extracting text data from an HTML page using the XML library
  • Extracting text data from PubMed
  • Importing unstructured text data from a plain text file
  • Importing plain text data from a PDF file
  • Pre-processing text data for topic modeling and sentiment analysis
  • Creating a word cloud to explore unstructured text data
  • Using regular expression in text processing

Hands-on:

Write R code to implement text processing in order to handle data from various sources.

Learning Objectives:

Learn how to work with complex data structures and associated large data

Topics Covered:

  • Creating an XDF file from CSV input
  • Processing data as a chunk
  • Comparing computation time with data frame and XDF
  • Linear regression with larger data (rxFastLiner)

Hands-on:

Write R code to implement various functions in R and apply linear regression using large data.

Learning Objectives:

Learn basic data visualization techniques to build charts using R.

Topics Covered:

  • Basic Data Visualization with standard libraries

Hands-on:

Write R code to perform basic visualization of the data.

Learning Objective:

Case Study to explore R Programming

Topics Covered:

  • Case Study : R Programming

Hands-on:

Case Study to explore R

Projects

Create informative visualization using R

Write R code to perform basic visualization of the data.

Note:These were the projects undertaken by students from previous batches.  

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Faq

The Course

R is more than just a statistical language, it is a programming environment with powerful capabilities providing tools and functions that are much in demand in this age of information overload. Top companies whether they are in business, medicine, engineering or science require data scientists who can analyse tons of data and R is the preferred choice of programming language for these data analysts. Not only does it offer powerful data manipulation capabilities but also offers graphics functions that can be used for sharing information in presentations. This course will introduce you to the world of R and take you from the basics of how to install it and its packages to how to use it for statistical analyses. You will also learn through hands-on exercises and examples how to use R graphics and other functionalities.

  • How to use the R programming language and its environment
  • How to use R functions to manipulate data
  • How to analyze and manipulate data with R

By the end of this course, you would have gained knowledge on the use of R language to build applications on data statistics. This will help you land jobs as data analysts.

Tools and Technologies used for this course are

  • R
  • R Studio

There are no restrictions but participants would benefit if they have elementary programming knowledge.

Yes, KnowledgeHut does offer virtual training. Call us for more information on the same.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut. 

Your instructors are R experts who have years of industry experience. 

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: Refund Policy.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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