R Programming for Data Science and Data Analytics
Last Updated:
19 May 2026
Number of Students:
30 Students
Accreditation:
CPD Accredited
Prerequisites:
No formal qualification required (Anyone can participate)
Course Rating:
4.8 out of 5
Key features of the R Programming for Data Science and Data Analytics
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our R Programming for Data Science and Data Analytics is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
Course Curriculum
23 Sections | 130 Lessons
Introduction to Data Science
1m
Data Science: Career of the Future
4m
What is Data Science?
2m
Data Science as a Process
2m
Data Science Toolbox
3m
Data Science Process Explained
5m
What's Next?
1m
Engine and coding environment
3m
Installing R and RStudio
4m
RStudio: A quick tour
4m
Arithmetic with R
3m
Variable assignment
4m
Basic data types in R
3m
Creating a vector
5m
Naming a vector
4m
Arithmetic calculations on vectors
7m
Vector selection
6m
Selection by comparison
4m
What's a Matrix?
2m
Analyzing Matrices
3m
Naming a Matrix
5m
Adding columns and rows to a matrix
6m
Selection of matrix elements
3m
Arithmetic with matrices
7m
Additional Materials
What's a Factor?
2m
Categorical Variables and Factor Levels
4m
Summarizing a Factor
1m
Ordered Factors
5m
What's a Data Frame?
3m
Creating Data Frames
20m
Selection of Data Frame elements
3m
Conditional selection
3m
Sorting a Data Frame
3m
Additional Materials
Why would you need lists?
1m
Creating a List
6m
Selecting elements from a list
3m
Adding more data to the list
2m
Additional Materials
Equality
3m
Greater and Less Than
3m
Compare Vectors
3m
Compare Matrices
2m
Additional Materials
AND, OR, NOT Operators
4m
Logical operators with vectors and matrices
4m
Reverse the result: (!)
1m
Relational and Logical Operators together
6m
Additional Materials
The IF statement
4m
IF…ELSE
3m
The ELSEIF statement
5m
Full Exercise
3m
Additional Materials
Write a While loop
4m
Looping with more conditions
4m
Break: stop the While Loop
4m
What’s a For loop?
2m
Loop over a vector
2m
Loop over a list
3m
Loop over a matrix
4m
For loop with conditionals
1m
Using Next and Break with For loop
3m
Additional Materials
What is a Function?
2m
Arguments matching
3m
Required and Optional Arguments
3m
Nested functions
2m
Writing own functions
3m
Functions with no arguments
2m
Defining default arguments in functions
4m
Function scoping
2m
Control flow in functions
3m
Additional Materials
Installing R Packages
1m
Loading R Packages
4m
Different ways to load a package
2m
Additional Materials
What is lapply and when is used?
4m
Use lapply with user-defined functions
3m
lapply and anonymous functions
1m
Use lapply with additional arguments
4m
Additional Materials
What is sapply?
2m
How to use sapply
2m
sapply with your own function
2m
sapply with a function returning a vector
2m
When can't sapply simplify?
2m
What is vapply and why is it used?
4m
Additional Materials
Mathematical functions
5m
Data Utilities
8m
Additional Materials
grepl & grep
4m
More metacharacters
4m
sub & gsub
2m
More metacharacters
4m
Additional Materials
Today and Now
2m
Create and format dates
6m
Create and format times
3m
Calculations with Dates
3m
Calculations with Times
7m
Additional Materials
Get and set current directory
4m
Get data from the web
4m
Loading flat files
3m
Loading Excel files
5m
Additional Materials
Base plotting system
3m
Base plots: Histograms
3m
Base plots: Scatterplots
5m
Base plots: Regression Line
3m
Base plots: Boxplot
3m
Introduction to dplyr package
4m
Using the pipe operator (%>%)
2m
Columns component: select()
5m
Columns component: rename() and rename_with()
2m
Columns component: mutate()
2m
Columns component: relocate()
2m
Rows component: filter()
1m
Rows component: slice()
4m
Rows component: arrange()
1m
Rows component: rowwise()
2m
Grouping of rows: summarise()
3m
Grouping of rows: across()
2m
COVID-19 Analysis Task
8m
Additional Materials
Order Your Certificates or Transcripts
Course Reviews
No reviews yet.
Key features of the R Programming for Data Science and Data Analytics
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our R Programming for Data Science and Data Analytics is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
23 Sections | 130 Lessons
Introduction to Data Science
1m
Data Science: Career of the Future
4m
What is Data Science?
2m
Data Science as a Process
2m
Data Science Toolbox
3m
Data Science Process Explained
5m
What's Next?
1m
Engine and coding environment
3m
Installing R and RStudio
4m
RStudio: A quick tour
4m
Arithmetic with R
3m
Variable assignment
4m
Basic data types in R
3m
Creating a vector
5m
Naming a vector
4m
Arithmetic calculations on vectors
7m
Vector selection
6m
Selection by comparison
4m
What's a Matrix?
2m
Analyzing Matrices
3m
Naming a Matrix
5m
Adding columns and rows to a matrix
6m
Selection of matrix elements
3m
Arithmetic with matrices
7m
Additional Materials
What's a Factor?
2m
Categorical Variables and Factor Levels
4m
Summarizing a Factor
1m
Ordered Factors
5m
What's a Data Frame?
3m
Creating Data Frames
20m
Selection of Data Frame elements
3m
Conditional selection
3m
Sorting a Data Frame
3m
Additional Materials
Why would you need lists?
1m
Creating a List
6m
Selecting elements from a list
3m
Adding more data to the list
2m
Additional Materials
Equality
3m
Greater and Less Than
3m
Compare Vectors
3m
Compare Matrices
2m
Additional Materials
AND, OR, NOT Operators
4m
Logical operators with vectors and matrices
4m
Reverse the result: (!)
1m
Relational and Logical Operators together
6m
Additional Materials
The IF statement
4m
IF…ELSE
3m
The ELSEIF statement
5m
Full Exercise
3m
Additional Materials
Write a While loop
4m
Looping with more conditions
4m
Break: stop the While Loop
4m
What’s a For loop?
2m
Loop over a vector
2m
Loop over a list
3m
Loop over a matrix
4m
For loop with conditionals
1m
Using Next and Break with For loop
3m
Additional Materials
What is a Function?
2m
Arguments matching
3m
Required and Optional Arguments
3m
Nested functions
2m
Writing own functions
3m
Functions with no arguments
2m
Defining default arguments in functions
4m
Function scoping
2m
Control flow in functions
3m
Additional Materials
Installing R Packages
1m
Loading R Packages
4m
Different ways to load a package
2m
Additional Materials
What is lapply and when is used?
4m
Use lapply with user-defined functions
3m
lapply and anonymous functions
1m
Use lapply with additional arguments
4m
Additional Materials
What is sapply?
2m
How to use sapply
2m
sapply with your own function
2m
sapply with a function returning a vector
2m
When can't sapply simplify?
2m
What is vapply and why is it used?
4m
Additional Materials
Mathematical functions
5m
Data Utilities
8m
Additional Materials
grepl & grep
4m
More metacharacters
4m
sub & gsub
2m
More metacharacters
4m
Additional Materials
Today and Now
2m
Create and format dates
6m
Create and format times
3m
Calculations with Dates
3m
Calculations with Times
7m
Additional Materials
Get and set current directory
4m
Get data from the web
4m
Loading flat files
3m
Loading Excel files
5m
Additional Materials
Base plotting system
3m
Base plots: Histograms
3m
Base plots: Scatterplots
5m
Base plots: Regression Line
3m
Base plots: Boxplot
3m
Introduction to dplyr package
4m
Using the pipe operator (%>%)
2m
Columns component: select()
5m
Columns component: rename() and rename_with()
2m
Columns component: mutate()
2m
Columns component: relocate()
2m
Rows component: filter()
1m
Rows component: slice()
4m
Rows component: arrange()
1m
Rows component: rowwise()
2m
Grouping of rows: summarise()
3m
Grouping of rows: across()
2m
COVID-19 Analysis Task
8m
Additional Materials
Order Your Certificates or Transcripts
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