Complete R Programming course: Beginner to Advanced Level
Master R 4.2.2: Learn Data Aggregation, Tidyverse, reshape, tidyr, dplyr, data.table packages and so on!
Updated on Apr, 2024
Language - English
Lectures -122
Resources -4
Duration -7.5 hours
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Course Description
*Learn R Programming (4.2) by Coding Along*
**Learn R 4.2 at your own pace!**
Are you starting you R programming journey? Are you a complete beginner in programming?
This course is suitable for you!
Why learn R using is 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 R
2. Data Structures in R
Vectors
Matrices
Arrays
Dataframes
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!
Goals
What will you learn in this course:
In this course 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 in r
- Calling R Functions & How to write your own functions
- R for Complicated mathematics formulae
- Master R 4.2.2
- What is the Pipe Operator & How to use it
Prerequisites
What are the prerequisites for this course?
- No prior programming experience required
- Eager to learn
- The course only require internet connection for installing the latest version of R & R Studio
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to the Course
1 Lectures
-
Course Introduction
08:39
08:39
R & R Studio Set up
2 Lectures
Mastering the Basics of R
4 Lectures
Data Structures in R
1 Lectures
Vectors in R
8 Lectures
Matrices in R
8 Lectures
Arrays in R
3 Lectures
Lists in R
6 Lectures
Data Frames in R
5 Lectures
Working with Categorical Data in R
6 Lectures
Functions in R
5 Lectures
If-else statements in R
7 Lectures
Coding Loops in R
6 Lectures
Data Analysis: The apply family of functions
14 Lectures
Importing Data into R with tidyverse package
2 Lectures
Data Analysis, Transformation & Transformation
4 Lectures
Merging with merge function
3 Lectures
Restructuring Datasets with reshape package
3 Lectures
Restructuring Datasets with tidyr package
4 Lectures
Data Aggregation using R
6 Lectures
Dplyr package
12 Lectures
Data.table package
12 Lectures
Instructor Details
Nkosingimele Ngcobo
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