Introduction to R Programming

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

6 hours of content 17302 students

$99.00

Lifetime access

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14-Day Money-Back Guarantee

What you get:

  • 6 hours of content
  • 17 Interactive exercises
  • 104 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Introduction to R Programming

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 6 hours of content
  • 17 Interactive exercises
  • 104 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 6 hours of content
  • 17 Interactive exercises
  • 104 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Get a comprehensive introduction to the world of R programming
  • Master writing well-structured and professional R code in RStudio
  • Acquire data manipulation proficiency in R: Create, import, and manipulate data frames vectors, and matrices
  • Learn how to use R for statistical analysis and data visualization
  • Engage in practical hands-on statistical tasks: Solve hypotheses testing and linear regression challenges in R
  • Boost your resume with key R programming skills demanded by data science employers

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Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

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What does this course cover

1.1 What does this course cover

5 min

What does section 1 cover

1.2 What does section 1 cover

1 min

Downloading and installing R and RStudio

1.3 Downloading and installing R and RStudio

3 min

Quick guide to the RStudio user interface

1.4 Quick guide to the RStudio user interface

8 min

Changing the appearance of RStudio

1.7 Changing the appearance of RStudio

2 min

Installing packages and using the library

1.8 Installing packages and using the library

5 min

Curriculum

  • 1. Introduction & Getting Started
    6 Lessons 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
    5 min
    What does section 1 cover
    1 min
    Downloading and installing R and RStudio
    3 min
    Quick guide to the RStudio user interface
    8 min
    Changing the appearance of RStudio
    2 min
    Installing packages and using the library
    5 min
  • 2. The building blocks of R
    8 Lessons 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
    5 min
    Data types in R (Integers and doubles)
    5 min
    Data types in R (Characters and logicals)
    3 min
    Coercion rules in R
    3 min
    Functions in R
    3 min
    Functions and arguments
    3 min
    Building a function in R
    8 min
    Using the script vs. using the console
    3 min
  • 3. Vectors and vector operations
    7 Lessons 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
    1 min
    Introduction to vectors
    4 min
    Vector recycling
    2 min
    Naming a vector
    3 min
    Getting help with R
    7 min
    Slicing and indexing a vector
    7 min
    Changing the dimensions of an object in R
    5 min
  • 4. Matrices
    10 Lessons 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
    7 min
    Faster code - creating a matrix in a single line of code
    3 min
    Do matrices recycle
    2 min
    Indexing an element from a matrix
    5 min
    Slicing a matrix
    4 min
    Matrix arithmetic
    7 min
    Matrix operations
    4 min
    Categorical data
    4 min
    Creating a factor in R
    6 min
    Lists in R
    6 min
  • 5. Fundamentals Of Programming With R
    10 Lessons 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
    5 min
    Logical operators in R
    3 min
    Logical operators and vectors
    2 min
    If else else-if statements
    6 min
    If else else-if keep-in-minds's
    4 min
    For loops in R
    6 min
    While loops in R
    4 min
    Repeat loops in R
    3 min
    Building a function in R 2.0
    5 min
    Building a function in R 2.0 Scoping
    5 min
  • 6. Data frames in R
    10 Lessons 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
    1 min
    Creating a data frame
    6 min
    The Tidyverse package
    3 min
    Data import into R
    3 min
    Importing a CSV into R
    3 min
    Data export in R
    3 min
    Getting a sense of your data frame
    4 min
    Indexing and slicing a data frame in R
    4 min
    Extending a data frame in R
    4 min
    Dealing with missing data
    5 min
  • 7. Manipulating data with R
    7 Lessons 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
    1 min
    Data transformation with R - the Dplyr package - Part I
    6 min
    Data transformation with R - the Dplyr package - Part II
    3 min
    Sampling data with the Dplyr package
    2 min
    Using the pipe operator
    3 min
    Tidying your data - gather() and separate()
    7 min
    Tidying your data - unite() and spread()
    3 min
  • 8. Visualizing data with R
    8 Lessons 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
    1 min
    Intro to data visualisation
    4 min
    Intro to ggplot2
    7 min
    Variables revisited
    6 min
    Building a histogram with ggplot2
    7 min
    Building a bar chart with ggplot2
    6 min
    Building a box and whiskers plot with ggplot2
    6 min
    Building a scatterplot with ggplot2
    5 min
  • 9. Exploratory data analysis with R
    5 Lessons 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
    4 min
    Mean, median, mode
    5 min
    Skewness
    3 min
    Variance, standard deviation, and coefficient of variability
    6 min
    Covariance and correlation
    7 min
  • 10. Hypothesis Testing
    9 Lessons 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
    7 min
    Standard Error and Confidence Intervals
    9 min
    Hypothesis Testing
    8 min
    Type I and Type II errors
    3 min
    Test for the mean. Population variance known
    7 min
    The P-value
    5 min
    Test for the mean. Population variance unknown
    5 min
    Dependent samples
    7 min
    Comparing two means. Independent samples
    5 min
  • 11. Linear Regression Analysis in R
    7 Lessons 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
    5 min
    Correlation vs Regression
    2 min
    Geometrical Representation
    2 min
    Doing the Regression in R
    4 min
    How to interpret the regression table
    4 min
    Decomposition of variability
    3 min
    R-Squared
    5 min

Topics

Programmingdata analysisdata visualizationData processingR

Tools & Technologies

r

Course Requirements

  • No prior experience with R is required. We will start this R programming course from the basics and gradually build your understanding. Everything you need is included
  • You will need to install RStudio

Who Should Take This Course?

Level of difficulty: Beginner

  • Aspiring data analysts and data scientists
  • Graduate students who need R for their studies
  • Everyone who wants to learn how to code and problem-solve in R

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Simona Dobreva

Simona Dobreva

Head of Product at

1 Courses

798 Reviews

17302 Students

Simona is a cognitive science researcher by formal training, and the author of the R for Statistics and Data Science course in our program. Her rigorous academic approach and an uncompromising drive for excellence are the main contributors to her students’ success. She was a Lead Research Technician during her studies, running experimental manipulations and ensuring the representativeness in the data. Simona is very passionate about game development and game theory, and she enjoys exploring new video games in her free time.

What Our Learners Say

13.12.2024
13.12.2024

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