R programming for Beginners

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Wrikki Lahiri

3:18:53

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1. Downloading and Installing R and R studio

1. Downloading R and R studio.mp4

03:26

2. R and R studio installation.mp4

02:27

2. Introduction to R studio

1. Introduction to R studio.mp4

02:42

3. Basic Syntax in R

1. Printing in R.mp4

03:35

2. Assigning Values in R.mp4

05:26

4. Data Types

1. Data Types.mp4

08:42

5. Operators in R

1. Arithmetic Operator.mp4

04:46

2. Relational Operator.mp4

04:57

3. Logical Operators.mp4

04:42

4. Assignment Operators.mp4

01:49

5. Miscellaneous Operators.mp4

02:06

6. Conditional Statements in R

1. If Statements.mp4

05:13

2. If statement with AND or OR.mp4

04:38

3. If Else statement.mp4

03:34

4. If else if and else statements.mp4

04:56

5. Switch statements.mp4

03:48

7. Loops

1. For Loops.mp4

06:14

2. Repeat Loops.mp4

05:40

3. While Loops.mp4

05:05

8. Functions in R

1. In built functions in R.mp4

02:39

2. Simple Functions.mp4

02:51

3. Functions with arguments.mp4

03:09

4. Simple functions that return values.mp4

04:57

5. Functions with arguments that return values.mp4

02:41

9. Data Structures

1. Vectors.mp4

08:37

2. Lists.mp4

05:05

3. Matrices.mp4

09:43

4. Arrays.mp4

15:10

10. Data Visualisation

1. Line Plot.mp4

04:22

2. Bar Plot.mp4

03:36

3. Scatter Plot.mp4

02:01

4. Histogram Plot.mp4

03:37

5. Box Plot.mp4

01:44

11. Descriptive Statistics in R

1. Descriptive Statistics.mp4

08:39

12. Data Analysis

1. Correlation.mp4

05:47

2. Linear Regression.mp4

11:16

3. Logistic Regression.mp4

11:29

13. Data Interface

1. Look up and set directory.mp4

03:47

2. Import CSV.mp4

03:57

14. Practise Exercise

1. Data Analysis exercise.html

Description

Fundamentals of R programming and data visualisation and statistical analysis in R for beginners

What You'll Learn?

Students will learn the fundamentals of R programming
Students will learn how to write functions, loops and conditional statements
Students will learn data visualisation in R
Students will learn Statistics in R
Students will learn how to write code in R
Students will gain knowledge about R syntax and language

Who is this for?

Beginners who want to learn R programming language

Beginners who want to learn R syntax

Beginners who want to learn how to write code in R

Beginners who want to learn how to visualise data in R

Beginners who want to learn how to use R for statistics

Beginners who want to learn the building blocks of programming such as loops, functions and conditional statements

What You Need to Know?

Knowledge of basic high school statistics

Basic knowledge of plots and graphs

Computer literacy

A windows or Mac computer

More details

Description
The "R Programming for Beginners" course covers a wide range of fundamental topics related to learning and using the R programming language for data analysis and visualization. Here's a breakdown of the main topics covered in the course:

1. Installing R software: Students learn how to install the R programming environment on their computers, which is the first step in getting started with R.

2. Basic syntax: The course covers the basic syntax of the R language, which includes understanding how to write and execute simple R commands.

3. Variable definition: Students learn how to create and manipulate variables to store data and values in R.

4. Operators in R: This topic covers various types of operators in R, including arithmetic, comparison, and logical operators.

5. Conditionals: Students learn how to use conditional statements (if-else) to make decisions in their R programs based on certain conditions.

6. Loops: The course covers different types of loops (such as for loops and while loops) that allow students to repeat tasks and operations.

7. Functions: Students learn how to define and use functions in R, which allow them to encapsulate code into reusable blocks.

8. Data structures: The course introduces various data structures in R, such as vectors, matrices, data frames, and lists.

9. Visualization of data: Students learn how to create visualizations using R to represent data graphically.

10. Descriptive statistics: The course covers essential descriptive statistics, including measures like mean, median, standard deviation, and more.

11. Statistical analysis: Students are introduced to statistical techniques like correlation, linear regression, and logistic regression for analyzing relationships and making predictions from data.

12. Data interfaces: The course likely covers how to importÂ data from CSV and change working directory.

The overall goal of the course is to provide beginners with a solid foundation in R programming and data analysis techniques. By the end of the course, students will be able to write and understand R code, write programs using loops, conditional statements and functions, create data visualizations, and conduct statistical analyses.

At the end of the course there is a final project that will help people practise what they have learnt. This project requires students to analyse data, allowing students to practically apply their knowledge, bolstering their skills and confidence. This emphasis on real-world application ensures students can adeptly handle data analysis tasks beyond the course.

Who this course is for:
Beginners who want to learn R programming language
Beginners who want to learn R syntax
Beginners who want to learn how to write code in R
Beginners who want to learn how to visualise data in R
Beginners who want to learn how to use R for statistics
Beginners who want to learn the building blocks of programming such as loops, functions and conditional statements

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R Programming

Wrikki Lahiri

Instructor's Courses

Wrikki Lahiri is a doctoral student at the City, University of London, and has worked as a data scientist in industry before embarking on his PhD. He has a Master of Science degree in Industrial and Systems Engineering and a Bachelor's degree in Mechanical Engineering. Wrikki's research involves big data mining, entrepreneurial finance, and Behavioural Psychology and Economics. For his research projects Wrikki scrapes, and analyses big data on crowdfunding and social media using machine learning, statistical, and data mining methods. Other research projects Wrikki is working on investigates entrepreneurial habits and motivation and productivity in startup employees. As a doctoral student Wrikki has taught undergraduate statistics and Wrikki also has teaching experience through providing private tuition to undergraduates and high school students in statistics and maths. Wrikki teaches statistics, data mining methods, entrepreneurial psychology and entrepreneurial finance.

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