Advanced R Course

Curriculum

1 Intro to R Programming
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?

2 Installing and Loading Libraries
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.

3 Data Structures in R
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.

4 Control & Loop Statements in R
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.

5 Functions 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.

6 Loop Functions in R
Learning Objectives:
Learn 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.

7 String Manipulation & Regular Expression in R
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.

8 Working with Data in R
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.

9 Querying, Filtering, and Summarizing
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.

10 R for Text Processing
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.

11 Handling large data in R
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.

12 Basic Data Visualization
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.

13 Case Study
Learning Objective:
Case Study to explore R Programming

Topics Covered:

• Case Study : R Programming

Hands-on:

Case Study to explore R