Learning R Programming for Data Science

This comprehensive learning R programming for data science course is ideal for anyone wishing to boost their career profile or advance their career in this field by gaining a thorough understanding of the subject. Anyone willing to gain extensive knowledge on this R programming can also take this course.

Whether you are a complete beginner or an aspiring professional, this course will provide you with the necessary skills and professional competence, and open your doors to a wide number of professions within your chosen sector.

This learning R programming for data science course has no academic prerequisites and is open to students from all academic disciplines. You will, however, need a laptop, desktop, tablet, or smartphone, as well as a reliable internet connection.

This learning R programming for data science course assesses learners through multiple-choice questions (MCQs). Upon successful completion of the modules, learners must answer MCQs to complete the assessment procedure. Through the MCQs, it is measured how much a learner could grasp from each section. In the assessment pass mark is 60%.

Data Frames

• What’s a Data Frame
• Creating a Data Frame
• Selection of Data Frame Elements
• Conditional Selection
• Sorting a Data Frame

Lists

• Why Would You Need Lists
• Creating a List
• Selecting Elements From a List
• Adding More Data to The List

Relational Operators

• Equality
• Greater and Less Than
• Compare Vectors
• Compare Matrices

Logical Operators

• AND, OR, NOT Operators
• Logical Operators with Vectors and Matrices
• Reverse The Result
• Relational and Logical Operators Together

Conditional Statements

• IF Statement
• IF…ELSE
• The ELSEIF Statement

Loops

• Write a While Loop
• Looping with More Conditions
• Break Stop The While Loop
• What’s a For Loop.
• Loop Over a Vector
• Loop Over a List
• Loop Over a Matrix
• For Loop with Conditionals
• Using Next and Break with For Loop

Functions

• What Is a Function.
• Arguments Matching
• Required and Optional Arguments
• Nested Functions
• Writing Own Functions
• Functions with No Arguments
• Defining Default Arguments in Functions
• Function Scoping
• Control Flow in Functions

R Packages

• Installing R Packages
• Loading R Packages
• Different Ways to Load a Package

The Apply Family – Lapply

• What Is Lapply and When Is Used.
• Use Lapply with User-Defined Functions
• Lapply and Anonymous Functions
• Use Lapply with Additional Arguments

The Apply Family – Sapply & Vapply

• What Is Sapply.
• How to Use Sapply.
• Sapply with Your Own Function
• Sapply with a Function Returning a Vector
• When Can’t Sapply Simplify.
• What Is Vapply and Why Is It Used.

Useful Functions

• Mathematical Functions
• Data Utilities

Regular Expressions

• Grepl & Grep
• Metacharacters
• Sub & Gsub
• More Metacharacters

Dates And Times

• Today and Now
• Create and Format Dates
• Create and Format Times
• Calculations with Dates
• Calculations with Times

Getting and Cleaning Data

• Get and Set Current Directory
• Get Data From The Web
• Loading Flat Files
• Loading Excel Files

Plotting Data in R

• Base Plotting System
• Base Plots Histograms
• Base Plots Scatterplots
• Base Plots Regression Line
• Base Plots Boxplot

Data Manipulation With Dplyr

• Introduction to Dplyr Package
• Using the Pipe Operator (%>%)
• Columns Component Select()
• Columns Component Rename() and Rename_with()
• Columns Component Mutate()
• Columns Component Relocate()
• Rows Component Filter()
• Rows Component Slice()
• Rows Component Arrange()
• Rows Component Rowwise()
• Grouping of Rows Summarise()
• Grouping of Rows Across()
• Covid-19 Analysis Task