Online Course
Introduction to R Programming

Name: Introduction to R Programming Course
Price: 36 USD

Master R programming for data science: manipulate, analyze, and visualize data with the best programming language for statistical analysis

4.8

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19,384 students already enrolled

Skill level:

Basic

Duration:

6 hours

Lessons (6 hours)

Projects (7 hours)

CPE credits:

8.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Get a comprehensive introduction to the world of R programming.
Write well-structured and professional R code in RStudio.
Create, import, and manipulate data frames, vectors, and matrices.
Learn how to use R for statistical analysis and data visualization.
Solve hypothesis testing and linear regression tasks in R.

Topics & tools

ProgrammingData AnalysisData VisualizationData ProcessingR

Your instructor

Simona Dobreva

Head of Product at Team-GPT | Psychology Graduate | Data-Driven Marketing & Product Leader

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Psychology

Media and recognition

Daniel Garrad Prize, University of Edinburgh
Edinburgh Award for student leadership and volunteering

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Iliya Valchanov

CEO of Team-GPT | AI Educator | Co-founder of 365 Data Science & 3veta

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, International Economics, Management, and Finance

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 8.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

R is one of the best programming languages specifically designed for statistics and graphics. Programming in R is a fast and effective way to perform advanced data analyses and manipulations. In this course, you will learn how to use R and utilize the many data analysis techniques, methods, and functions it has to offer to the professional data scientist.

Prerequisites

R (latest version from cran.r-project.org) and RStudio (free version available at posit.co)

Advanced preparation

None

Curriculum

87 lessons 60 exercises 1 project 1 exam

1. Introduction & Getting Started

24 min

In this introductory part of the course, we will go for a walk in the R environment. First, we are going to install R and RStudio together. Then, we’ll dive straight into RStudio and learn about its interface, and how to make use of the main windows and tabs there. We will also talk about setting your working directory and getting additional help.

24 min

In this introductory part of the course, we will go for a walk in the R environment. First, we are going to install R and RStudio together. Then, we’ll dive straight into RStudio and learn about its interface, and how to make use of the main windows and tabs there. We will also talk about setting your working directory and getting additional help.

What does this course cover Free

What does section 1 cover Free

Downloading and installing R and RStudio Free

Quick guide to the RStudio user interface Free

Exercise Free

Coding exercise Free

Changing the appearance of RStudio Free

Installing packages and using the library Free
2. The building blocks of R

33 min

In this section, you will learn about objects and coercion rules in R, functions in R, and how to use R’s console. Not only that, but by the end of the section, you will have built your first function; it will be able to draw cards from a deck, so you can play your favorite board game even if you don’t have the physical cards in front of you.

33 min

In this section, you will learn about objects and coercion rules in R, functions in R, and how to use R’s console. Not only that, but by the end of the section, you will have built your first function; it will be able to draw cards from a deck, so you can play your favorite board game even if you don’t have the physical cards in front of you.

Creating an object in R Free

Coding exercise Free

Data types in R (Integers and doubles) Free

Exercise Free

Coding exercise Free

Data types in R (Characters and logicals) Free

Coding exercise Free

Coercion rules in R Free

Exercise Free

Coding exercise Free

Functions in R Free

Coding exercise Free

Functions and arguments Free

Coding exercise Free

Building a function in R Free

Coding exercise Free

Using the script vs. using the console Free
3. Vectors and vector operations

29 min

Now that we have covered the basics, in this section, we are about to drill deeper into R’s most widely used object type – the vector. You will learn how to create vectors and how to perform vector arithmetic operations. You will also see how to index and access elements from a vector, and how vectors recycle. Then, you will see how to change the dimensions of a vector and create a two-dimensional object from it. That will be our nice little segue into matrices.

29 min

Now that we have covered the basics, in this section, we are about to drill deeper into R’s most widely used object type – the vector. You will learn how to create vectors and how to perform vector arithmetic operations. You will also see how to index and access elements from a vector, and how vectors recycle. Then, you will see how to change the dimensions of a vector and create a two-dimensional object from it. That will be our nice little segue into matrices.

What does section 3 cover

Introduction to vectors

Coding exercise

Vector recycling

Naming a vector

Exercise

Exercise

Coding exercise

Exercise

Getting help with R

Slicing and indexing a vector

Coding exercise

Changing the dimensions of an object in R

Coding exercise
4. Matrices

48 min

In this section, you will learn how to create and rename matrices, and how to index and slice matrices. All of this will lay a super solid foundation for the big star of data analysis: the data frame. Not only that, but we will also talk about factors, which are related to the statistics part of the course. Finally, we will cover lists: R’s way of storing hierarchical data.

48 min

In this section, you will learn how to create and rename matrices, and how to index and slice matrices. All of this will lay a super solid foundation for the big star of data analysis: the data frame. Not only that, but we will also talk about factors, which are related to the statistics part of the course. Finally, we will cover lists: R’s way of storing hierarchical data.

Creating a matrix

Coding exercise

Faster code - creating a matrix in a single line of code

Coding exercise

Do matrices recycle

Indexing an element from a matrix

Coding exercise

Slicing a matrix

Coding exercise

Matrix arithmetic

Coding exercise

Matrix operations

Coding exercise

Categorical data

Creating a factor in R

Coding exercise

Lists in R

Coding exercise
5. Fundamentals Of Programming With R

43 min

In this section of the course, we will go through some of the fundamental tools you need to learn when programming with R (and many other programming languages). We will cover relational operators, logical operators, vectors, IF, ELSE, and different types of loops (for, while, and repeat) in R. Some of these topics will have already been introduced to you in our Python training, but here you will have the chance to reinforce what you have learned and see things with R in mind.

43 min

In this section of the course, we will go through some of the fundamental tools you need to learn when programming with R (and many other programming languages). We will cover relational operators, logical operators, vectors, IF, ELSE, and different types of loops (for, while, and repeat) in R. Some of these topics will have already been introduced to you in our Python training, but here you will have the chance to reinforce what you have learned and see things with R in mind.

Relational operators in R

Coding exercise

Logical operators in R

Coding exercise

Logical operators and vectors

Coding exercise

If else else-if statements

Coding exercise

If else else-if keep-in-minds's

For loops in R

Coding exercise

While loops in R

Coding exercise

Repeat loops in R

Coding exercise

Building a function in R 2.0

Coding exercise

Building a function in R 2.0 Scoping

Coding exercise
6. Data frames in R

36 min

In this section, we will focus our attention on how to create and import data frames into R. How to quickly get a sense of your data frame by using the str() function, summary(), col-and row-names, and so on. We’ll learn about accessing individual elements of your data frame for further use. And about extending a data frame with either new observations or variables (or row and columns). Furthermore, we will talk about dealing with missing data because in real life that happens more often than we’d like. And we’ll discuss exporting data frames once we’re happy with their general state and are ready to share them with the world.

36 min

In this section, we will focus our attention on how to create and import data frames into R. How to quickly get a sense of your data frame by using the str() function, summary(), col-and row-names, and so on. We’ll learn about accessing individual elements of your data frame for further use. And about extending a data frame with either new observations or variables (or row and columns). Furthermore, we will talk about dealing with missing data because in real life that happens more often than we’d like. And we’ll discuss exporting data frames once we’re happy with their general state and are ready to share them with the world.

What does section 6 cover

Creating a data frame

Coding exercise

The Tidyverse package

Data import into R

Coding exercise

Importing a CSV into R

Data export in R

Getting a sense of your data frame

Coding exercise

Indexing and slicing a data frame in R

Coding exercise

Extending a data frame in R

Coding exercise

Dealing with missing data

Coding exercise
7. Manipulating data with R

25 min

Here, we will be talking about data transformation with the dplyr package. More specifically, how to filter(), arrange(), mutate(), and transmute() your data, as well as how to sample() fractions and a fixed number of elements from it. You will also learn what tidy data is, why it is extremely important for the efficiency of your work to tidy your data sets in the most meaningful way, and how to achieve this by using the tidyr package. You will be tidying several messy real-life data sets, and you will learn how to combine multiple operations in an intuitive way by using the pipe operator.

25 min

Here, we will be talking about data transformation with the dplyr package. More specifically, how to filter(), arrange(), mutate(), and transmute() your data, as well as how to sample() fractions and a fixed number of elements from it. You will also learn what tidy data is, why it is extremely important for the efficiency of your work to tidy your data sets in the most meaningful way, and how to achieve this by using the tidyr package. You will be tidying several messy real-life data sets, and you will learn how to combine multiple operations in an intuitive way by using the pipe operator.

What does section 7 cover

Data transformation with R - the Dplyr package - Part I

Data transformation with R - the Dplyr package - Part II

Sampling data with the Dplyr package

Using the pipe operator

Tidying your data - gather() and separate()

Tidying your data - unite() and spread()
8. Visualizing data with R

42 min

Plotting and graphing data is the most elegant way to understand your data and present your findings to others. In this section, we are going to learn about the grammar of graphics and the seven layers that comprise a visualization. Then, we will jump straight into creating graphs and plots, with the ggplot2 package. Starting with the histogram, we will continue on to the bar chart, then onto the box and whiskers plot, and finally, the scatterplot. You will notice that with each new type of plot, you will also be learning about a new layer or two while getting familiarized with ggplot2 and its inner workings in an incremental way.

42 min

Plotting and graphing data is the most elegant way to understand your data and present your findings to others. In this section, we are going to learn about the grammar of graphics and the seven layers that comprise a visualization. Then, we will jump straight into creating graphs and plots, with the ggplot2 package. Starting with the histogram, we will continue on to the bar chart, then onto the box and whiskers plot, and finally, the scatterplot. You will notice that with each new type of plot, you will also be learning about a new layer or two while getting familiarized with ggplot2 and its inner workings in an incremental way.

What does section 8 cover

Intro to data visualisation

Intro to ggplot2

Variables revisited

Building a histogram with ggplot2

Building a bar chart with ggplot2

Building a box and whiskers plot with ggplot2

Building a scatterplot with ggplot2
9. Exploratory data analysis with R

25 min

In this part of the course, we start applying R for statistical analysis. We are ready to discuss several exploratory data analysis topics: population vs. sample; mean, median, and mode; skewness; variance, standard deviation, and the coefficient of variability; and covariance and correlation.

25 min

In this part of the course, we start applying R for statistical analysis. We are ready to discuss several exploratory data analysis topics: population vs. sample; mean, median, and mode; skewness; variance, standard deviation, and the coefficient of variability; and covariance and correlation.

Population vs Sample

Mean, median, mode

Coding exercise

Skewness

Variance, standard deviation, and coefficient of variability

Coding exercise

Covariance and correlation

Coding exercise
10. Hypothesis Testing

56 min

At this point, you are already familiar with hypothesis testing. We covered it in one of our earlier modules – Statistics. What we will do here is a natural continuation – you will learn how to carry out hypothesis testing in R.

56 min

At this point, you are already familiar with hypothesis testing. We covered it in one of our earlier modules – Statistics. What we will do here is a natural continuation – you will learn how to carry out hypothesis testing in R.

Distributions

Standard Error and Confidence Intervals

Hypothesis Testing

Type I and Type II errors

Test for the mean. Population variance known

Coding exercise

The P-value

Test for the mean. Population variance unknown

Coding exercise

Dependent samples

Coding exercise

Comparing two means. Independent samples

Coding exercise
11. Linear Regression Analysis in R

25 min

Regression analysis is another topic we covered earlier in our program. As with hypothesis testing, this is a great opportunity to apply the theory you have learned previously in R.

25 min

Regression analysis is another topic we covered earlier in our program. As with hypothesis testing, this is a great opportunity to apply the theory you have learned previously in R.

The Linear Regression Model

Correlation vs Regression

Geometrical Representation

Doing the Regression in R

Coding exercise

How to interpret the regression table

Decomposition of variability

R-Squared
12. Course project and exam

435 min

435 min

Housing Market Data Analysis in R Project

Course exam

Free lessons

1.1 What does this course cover

5 min

1.2 What does section 1 cover

1 min

1.3 Downloading and installing R and RStudio

3 min

1.4 Quick guide to the RStudio user interface

8 min

1.7 Changing the appearance of RStudio

2 min

1.8 Installing packages and using the library

5 min

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