R Programming Complete Certification Training

R Programming Complete Certification Training, available at $54.99, has an average rating of 4.23, with 39 lectures, 6 quizzes, based on 242 reviews, and has 14752 subscribers.

You will learn about Deep practical knowledge of R programming language Become a Data Scientist, Data Engineer, Data Analyst or Consultant Fundamentals and setup of R Language Get familiar with RStudio Variables and Data Types Input-Output Features in R Operators in R Data Structure in R Vectors, Lists and their application R Programs for Lists and Vectors in RStudio Matrix and application of Matrices in R with R Programs Arrays with R Programs for Arrays in RStudio Data Frames and R Programs for Data Frame in RStudio Factors, application of Factors, R Programs for Factors in RStudio Decision-making in R, types of decision-making statements with R Programs Loops in R, flowcharts and programs for loops in R Functions in R Strings in R Packages in R Data and File Management in R Plotting in R (graphs, charts, plots, histograms) Write complex R programs for practical industry scenarios This course is ideal for individuals who are R Developers & Data Developers or Data Scientists – R, Python or Newbies and beginners aspiring for a career in programming & statistical analysis or Data Engineers and Statistical Analysts or R & Python Programmers or Technical & Analytics Consultants or Anyone wishing to learn data science and machine learning or Lead R Developers or R Modelling Analysts or Data Software Developers or Financial and Marketing Analysts or Software Engineers or Web Application Developers or Business Analysts and Consultants or Data Science and Machine Learning enthusiasts It is particularly useful for R Developers & Data Developers or Data Scientists – R, Python or Newbies and beginners aspiring for a career in programming & statistical analysis or Data Engineers and Statistical Analysts or R & Python Programmers or Technical & Analytics Consultants or Anyone wishing to learn data science and machine learning or Lead R Developers or R Modelling Analysts or Data Software Developers or Financial and Marketing Analysts or Software Engineers or Web Application Developers or Business Analysts and Consultants or Data Science and Machine Learning enthusiasts.

Enroll now: R Programming Complete Certification Training

Summary

Title: R Programming Complete Certification Training

Price: $54.99

Average Rating: 4.23

Number of Lectures: 39

Number of Quizzes: 6

Number of Published Lectures: 39

Number of Published Quizzes: 6

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Deep practical knowledge of R programming language

Become a Data Scientist, Data Engineer, Data Analyst or Consultant

Fundamentals and setup of R Language

Get familiar with RStudio

Variables and Data Types

Input-Output Features in R

Operators in R

Data Structure in R

Vectors, Lists and their application

R Programs for Lists and Vectors in RStudio

Matrix and application of Matrices in R with R Programs

Arrays with R Programs for Arrays in RStudio

Data Frames and R Programs for Data Frame in RStudio

Factors, application of Factors, R Programs for Factors in RStudio

Decision-making in R, types of decision-making statements with R Programs

Loops in R, flowcharts and programs for loops in R

Functions in R

Strings in R

Packages in R

Data and File Management in R

Plotting in R (graphs, charts, plots, histograms)

Write complex R programs for practical industry scenarios

Who Should Attend

R Developers & Data Developers

Data Scientists – R, Python

Newbies and beginners aspiring for a career in programming & statistical analysis

Data Engineers and Statistical Analysts

R & Python Programmers

Technical & Analytics Consultants

Anyone wishing to learn data science and machine learning

Lead R Developers

R Modelling Analysts

Data Software Developers

Financial and Marketing Analysts

Software Engineers

Web Application Developers

Business Analysts and Consultants

Data Science and Machine Learning enthusiasts

Target Audiences

R Developers & Data Developers

Data Scientists – R, Python

Newbies and beginners aspiring for a career in programming & statistical analysis

Data Engineers and Statistical Analysts

R & Python Programmers

Technical & Analytics Consultants

Anyone wishing to learn data science and machine learning

Lead R Developers

R Modelling Analysts

Data Software Developers

Financial and Marketing Analysts

Software Engineers

Web Application Developers

Business Analysts and Consultants

Data Science and Machine Learning enthusiasts

A warm welcome to the R Programmingcourse by Uplatz.

R is a programming language that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.

While there is something called the S language which is often the vehicle of choice for research in statistical methodology, on the other hand R provides an Open Source route to participation in that activity. R is nothing but an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

R can be considered as an integrated version of a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.

This R Programming course by Uplatzis designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. Even if you are a beginner, this R course is the perfect place to start. If you are trying to understand the R programming language as a beginner, this R Programming course will provide you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise. This R tutorial will provide you an opportunity to take a deep-dive into R programming and build your R skills from scratch. To get the most out of the R programming training, you would need to practice as you proceed with the tutorials.

After successful completion of the R Programming training course you will be able to:

Master the use of the R and RStudio interactive environment

Expand R by installing R packages

Explore and understand how to use the R documentation

Read Structured Data into R from various sources

Understand the different data types in R

Understand the different data structures in R

R programming constructs – variables, functions, string manipulation, loops, etc.

Conduct decision-making using R

Able to do Data and file management in R

Packages in R

Plotting and Visualization in R

and more

R Programming – Course Syllabus

1. Fundamentals of R Language

Introduction to R

History of R

Why R programming Language

Comparison between R and Python

Application of R

2. Setup of R Language

Local Environment setup

Installing R on Windows

Installing R on Linux

RStudio

What is RStudio?

Installation of RStudio

First Program – Hello World

3. Variables and Data Types

Variables in R

Declaration of variable

Variable assignment

Finding variable

Data types in R

Data type conversion

R programs for Variables and Data types in RStudio

4. Input-Output Features in R

scan() function

readline() function

paste() function

paste0() function

cat() function

R Programs for implementing these functions in RStudio

5. Operators in R

Arithmetic Operators

Relational Operators

Logical Operators

Assignment Operators

Miscellaneous Operators

R Programs to perform various operations using operators in RStudio

6. Data Structure in R (part-I)

What is data structure?

Types of data structure

Vector

– What is a vector in R?

– Creating a vector

– Accessing element of vector

– Some more operations on vectors

– R Programs for vectors in RStudio

Application of Vector in R

List

– What is a list in R?

– Creating a list

– Accessing element of list

– Modifying element of list

– Some more operations on list

R Programs for list in RStudio

7. Data Structure in R (part-II)

Matrix or Matrices

– What is matrix in R?

– Creating a matrix

– Accessing element of matrix

– Modifying element of matrix

– Matrix Operations

R Programs for matrices in RStudio

Application of Matrices in R

Arrays

– What are arrays in R?

– Creating an array

– Naming rows and columns

– Accessing element of an array

– Some more operations on arrays

R Programs for arrays in RStudio

8. Data Structure in R (part-III)

Data frame

– What is a data frame in R?

– Creating a data frame

– Accessing element of data frame

– Modifying element of data frame

– Add the new element or component in data frame

– Deleting element of data frame

– Some more operations on data frame

R Programs for data frame in RStudio

Factors

– Factors in R

– Creating a factor

– Accessing element of factor

– Modifying element of factor

R Programs for Factors in RStudio

Application of Factors in R

9. Decision Making in R

Introduction to Decision making

Types of decision-making statements

Introduction, syntax, flowchart and programs for

– if statement

– if…else statement

– if…else if…else statement

– switch statement

10. Loop control in R

Introduction to loops in R

Types of loops in R

– for loop

– while loop

– repeat loop

– nested loop

break and next statement in R

Introduction, syntax, flowchart and programs for

– for loop

– while loop

– repeat loop

– nested loop

11. Functions in R

Introduction to function in R

Built-in Function

User-defined Function

Creating a Function

Function Components

Calling a Function

Recursive Function

Various programs for functions in RStudio

12. Strings in R

Introduction to string in R

– Rules to write R Strings

– Concatenate two or more strings in R

– Find length of String in R

– Extract Substring from a String in R

– Changing the case i.e. Upper to lower case and lower to upper case

Various programs for String in RStudio

13. Packages in R

Introduction to Packages in R

Get the list of all the packages installed in RStudio

Installation of the packages

How to use the packages in R

Useful R Packages for Data Science

R program for package in RStudio

14. Data and File Management in R

Getting and Setting the Working Directory

Input as CSV File

Analysing the CSV File

Writing into a CSV File

R programs to implement CSV file

15. Plotting in R (Part-I)

Line graph

Scatterplots

Pie Charts

3D Pie Chart

16. Plotting in R (Part-II)

Bar / line chart

Histogram

Box plot

Course Curriculum

Chapter 1: Introduction to R Programming

Lecture 1: Introduction to R Programming

Chapter 2: Setup of R Language

Lecture 1: Setup of R Language

Chapter 3: Variables and Data Types

Lecture 1: Variables and Data Types – part 1

Lecture 2: Variables and Data Types – part 2

Chapter 4: Input-Output Features

Lecture 1: Input-Output Features – part 1

Lecture 2: Input-Output Features – part 2

Chapter 5: Operators in R

Lecture 1: Operators in R – part 1

Lecture 2: Operators in R – part 2

Chapter 6: Vectors – Data Structure

Lecture 1: Vectors – Data Structure – part 1

Lecture 2: Vectors – Data Structure – part 2

Chapter 7: List – Data Structure

Lecture 1: List – Data Structure – part 1

Lecture 2: List – Data Structure – part 2

Chapter 8: Matrix – Data Structure

Lecture 1: Matrix – Data Structure – part 1

Lecture 2: Matrix – Data Structure – part 2

Chapter 9: Arrays – Data Structure

Lecture 1: Arrays – Data Structure – part 1

Lecture 2: Arrays – Data Structure – part 2

Chapter 10: Data Frame – Data Structure

Lecture 1: Data Frame – Data Structure – part 1

Lecture 2: Data Frame – Data Structure – part 2

Lecture 3: Data Frame – Data Structure – part 3

Chapter 11: Factors – Data Structure

Lecture 1: Factors – Data Structure – part 1

Lecture 2: Factors – Data Structure – part 2

Chapter 12: Decision Making in R

Lecture 1: Decision Making in R – part 1

Lecture 2: Decision Making in R – part 2

Chapter 13: Loops in R

Lecture 1: Loops in R – part 1

Lecture 2: Loops in R – part 2

Lecture 3: Loops in R – part 3

Chapter 14: Functions in R

Lecture 1: Functions in R – part 1

Lecture 2: Functions in R – part 2

Chapter 15: Strings in R

Lecture 1: Strings in R – part 1

Lecture 2: Strings in R – part 2

Chapter 16: Packages in R

Lecture 1: Packages in R

Chapter 17: Data and File Management in R

Lecture 1: Data and File Management in R – part 1

Lecture 2: Data and File Management in R – part 2

Chapter 18: Charts, Plots, Histograms in R

Lecture 1: Line chart in R

Lecture 2: Scatterplot in R

Lecture 3: Pie chart in R

Lecture 4: Bar chart in R

Lecture 5: Histogram in R

Lecture 6: Boxplots in R

Chapter 19: End of Course Quiz

Chapter 20: Coding Exercises

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 13 votes

2 stars: 14 votes

3 stars: 41 votes

4 stars: 94 votes

5 stars: 80 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!