Complete R Programming course- Beginner to Advanced Level

Complete R Programming course: Beginner to Advanced Level, available at $69.99, has an average rating of 4.25, with 122 lectures, based on 8 reviews, and has 39 subscribers.

You will learn about Advanced data analytics with dplyr package Advanced analytics with datatable package How to perform sorting, subscripting, Merging of R Data structures Data Aggregartion using dplyr and data table packages Data Aggregation with aggregate function Data Analysis with Apply family of functions: apply(), sapply(), tapply(), lapply() Importing data into R using tidyverse package Restructuring real datasets with reshape package Restructuring real world datasets with melt and cast functions Restructuring real datasets with tidy package package Restructuring real world datasets with gather and spare functions How to work with categorical data what are factors in R? Regular expression with grep & gsub function if statements: nested in statements How to use the switch function in r Complete explanations of the for loops and while loops using R How to use loops within your own functions How to create, manage and subscript R Data Structures Complete explanation and Application of Vectors, Matrices and Arrays with real datasets Complete explanation and Application of Dataframes and Lists Calling R Functions & How to write your own functions R for Complicated mathematics formulae Master R 4.2 What is the Pipe Operator & How to use it This course is ideal for individuals who are Beginner R programmers curious about working with data or R programmers who are looking for a unique way of learning R or R programmers who are not in a rush to master everything at once It is particularly useful for Beginner R programmers curious about working with data or R programmers who are looking for a unique way of learning R or R programmers who are not in a rush to master everything at once.

Enroll now: Complete R Programming course: Beginner to Advanced Level

Summary

Title: Complete R Programming course: Beginner to Advanced Level

Price: $69.99

Average Rating: 4.25

Number of Lectures: 122

Number of Published Lectures: 122

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Original Price: R299.99

Quality Status: approved

Status: Live

What You Will Learn

Advanced data analytics with dplyr package

Advanced analytics with datatable package

How to perform sorting, subscripting, Merging of R Data structures

Data Aggregartion using dplyr and data table packages

Data Aggregation with aggregate function

Data Analysis with Apply family of functions: apply(), sapply(), tapply(), lapply()

Importing data into R using tidyverse package

Restructuring real datasets with reshape package

Restructuring real world datasets with melt and cast functions

Restructuring real datasets with tidy package package

Restructuring real world datasets with gather and spare functions

How to work with categorical data

what are factors in R?

Regular expression with grep & gsub function

if statements: nested in statements

How to use the switch function in r

Complete explanations of the for loops and while loops using R

How to use loops within your own functions

How to create, manage and subscript R Data Structures

Complete explanation and Application of Vectors, Matrices and Arrays with real datasets

Complete explanation and Application of Dataframes and Lists

Calling R Functions & How to write your own functions

R for Complicated mathematics formulae

Master R 4.2

What is the Pipe Operator & How to use it

Who Should Attend

Beginner R programmers curious about working with data

R programmers who are looking for a unique way of learning R

R programmers who are not in a rush to master everything at once

Target Audiences

Beginner R programmers curious about working with data

R programmers who are looking for a unique way of learning R

R programmers who are not in a rush to master everything at once

*Learn R Programming by Coding Along*

Are you starting you R programming journey? Are you a complete beginner in programming?

This course is suitable for for!

Why learn R using this course?

This course covers all the theory needed for the understanding of writing a well neat R code. The latest version of R and R Studio is used to cover all   the required concepts for everyone who wants to have a career in the fields like:

Data Analyst

Quantitative Analyst

Data Scientists

Financial Analysts and many other high paying careers

By the end of this course you will have mastered:

1. The Basics of R

R Data Types

R for Basic Maths

Complicated Arithmetic formulas using R programming

2. Data Structures in R

Vectors

Matrices

Arrays

Data frames

Lists

3. Working with Categorical Data

What is categorical Data?

Factors in R programming – what are factors in r

Creating factors

Regular expression – grep and gsub functions in r

4. Functions in R

Calling R functions

Writing R functions

5. if statements

Stand-alone statement

else if & else statements

using if statements in functions

nested if statements

switch function

6. Loops

what is a loop?

for loops

while loops

nested loops

using loops within a function

7. The apply family of functions

apply function

lapply function

sapply function

tapply function

8. Importing Data into R with tidyverse

read a csv file in r

read an excel file in r with tidyverse

9. Data Manipulation & Transformation in R

Sorting, Appending and Merging

Duplicated Values

Restructuring with reshape package

Melting and Casting

Restructuring with tidyr package

Gather and spare

Data Aggregation

10. dplyr package

Sorting

Subscripting

Merging

Aggregation

What is the pipe operator in r?

11. data.table package

Setting Key & Subscripting

Merging & Aggregation

I’m certain you will enjoy this course!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the Course

Chapter 2: R & R Studio Set Up

Lecture 1: R 4.2 & R Studio Set Up

Lecture 2: R Studio Overview

Chapter 3: Mastering the Basics of R

Lecture 1: R Data Types

Lecture 2: R for Basic Mathematics

Lecture 3: Complicated Arithmetic Calculations with R

Lecture 4: How to install packages in R?

Chapter 4: Data Structures in R

Lecture 1: What will you learn?

Chapter 5: Vectors in R

Lecture 1: What is a vector? as used in R

Lecture 2: Creating vectors with c function

Lecture 3: Creating a sequence of Integers

Lecture 4: seq Function

Lecture 5: Creating a sequence of repeated values ( rep function)

Lecture 6: creating a named vector with names()

Lecture 7: Vector Attributes

Lecture 8: combining vectors with cbind function

Chapter 6: Matrices in R

Lecture 1: Introduction to Matrices: What is Matrix as used in R?

Lecture 2: Creating matrices with rbind and cbind functions

Lecture 3: Creating matrices with a matrix function

Lecture 4: Matrix Attributes: mode, dimension

Lecture 5: Matrices with names

Lecture 6: Subscripting Matrices

Lecture 7: Subscripting Matrices: Logical Values

Lecture 8: Subscripting Matrices with the condition

Chapter 7: Arrays in R

Lecture 1: What is an Array in R?

Lecture 2: Creating Arrays

Lecture 3: Array Attributes & subsets

Chapter 8: Lists in R

Lecture 1: What is List in R?

Lecture 2: Creating Lists in R

Lecture 3: List Attributes: length, names, mode

Lecture 4: Subscripting Lists: subsets

Lecture 5: Referencing Lists Elements: $ sign and double square bracket referencing

Lecture 6: Adding Elements in a List: Appending a list

Chapter 9: Dataframes in R

Lecture 1: What is Dataframe?

Lecture 2: Creating a dataframe

Lecture 3: Querying Data Frames Attributes

Lecture 4: Selecting Columns from the Data Frame

Lecture 5: Manipulating Data Frames as Matrices

Chapter 10: Working with Categorical Data?

Lecture 1: What is Categorical Data?

Lecture 2: What are R Factors& Factor Levels?

Lecture 3: Creating Factors in R

Lecture 4: Factor Levels

Lecture 5: Manipulating Factor Levels

Lecture 6: Regular Expressions: grep and gsub functions in R

Chapter 11: Functions in R

Lecture 1: Calling Functions in R

Lecture 2: Creating Functions in R: The function command

Lecture 3: Creating you first function in R

Lecture 4: R Functions that return the object

Lecture 5: Exercise

Chapter 12: if-else statements in R

Lecture 1: introduction to if statements using R

Lecture 2: Coding Exercise: Nested if statements

Lecture 3: Nested if statements inside a function

Lecture 4: Coding Exercise

Lecture 5: if statements inside a function: exercise

Lecture 6: The switch function in R

Lecture 7: Integer version of the switch function

Chapter 13: Coding Loops in R

Lecture 1: What is a Loops? For loop & while loops in R

Lecture 2: Introduction to R for loops

Lecture 3: looping a vector

Lecture 4: Looping through a Data Frame

Lecture 5: Nested for loops in R

Lecture 6: While loops

Chapter 14: Data Analysis: The apply family of functions

Lecture 1: Introduction to the Apply family of functions in R

Lecture 2: apply Function

Lecture 3: Using apply with own function

Lecture 4: Using apply Function on a dataset ( or Data Frame)

Lecture 5: lapply Function

Lecture 6: The split Function

Lecture 7: split and lapply on real world datasets (or Data Frame)

Lecture 8: sapply Function

Lecture 9: sapply Function on a real dataset

Lecture 10: tapply Function

Lecture 11: Apply functions on real world datasets: Exercise

Lecture 12: Apply functions on real world datasets: Coding Along Solution 1

Lecture 13: Apply functions on real world datasets: Coding Along Solution 2

Lecture 14: tapply Function on a real world dataset : Solution

Chapter 15: Importing Data into R with tidyverse package

Lecture 1: Importing a csv file in R

Lecture 2: Reading an excel file with tidyverse

Chapter 16: Data Analysis, Transformation & Manipulation

Lecture 1: Introduction to Data Manipulation

Lecture 2: Sorting datasets with sort() function

Lecture 3: Appending

Lecture 4: Duplicated Values

Chapter 17: Merging with merge Function

Lecture 1: What is Merging?

Instructors

hi- mathstats
Quantitative Analysis , Data Science

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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