R-BASIC.AE1
The Complete R Handbook
Step-by-step R programming course with Lab exercises. Learn the most versatile programming language for statistical computing and data analysis.
- Practice in 57 Hands-On Labs — nothing to install
- 31 Interactive Lessons and 169 topics mapped to the official exam objectives
Beginner Self-paced · 1 year access
57 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
31Interactive Lessons
169Topics
57LiveLab
109Flashcards
109Glossary of terms
01 / Skills you'll get
What you will be able to do
Our Complete R Handbook presents a step-by-step training guide for learning R programming concepts. The comprehensive course curriculum covers everything from beginner to advanced topics. You’ll be exploring a wide range of statistical techniques and machine learning. Our well-structured R programming course includes virtual Labs that facilitate hands-on exercises to help you practice R concepts on real-world problems.
- Expertise with R programming fundamentals including basic syntax, data structures, control flow, functions & packages
- Skilled at data manipulation, and analysis including import, export, cleaning, processing & exploration
- Ability to perform statistical analysis including descriptive statistics, hypothesis testing, correlation, regression
- Expert at machine learning with decision trees, random forests, support vector machines, neural networks, and clustering
- Create data visualizations using ggplot2 and base R graphics
- Ability to use the shiny framework to build interactive web apps
Course Highlights
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31 Structured Lessons Comprehensive coverage of core course objectives
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57 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
31 Interactive Lessons · 169 topics01 Introduction 4 topics +
- About This All-in-One
- What You Can Safely Skip
- Icons Used in This Course
- Where to Go from Here
02 R: What It Does and How It Does It 9 topics · 5 LiveLab +
- The Statistical (and Related) Ideas You Just Have to Know
- Getting R
- Getting RStudio
- A Session with R
- R Functions
- User-Defined Functions
- Comments
- R Structures
- for Loops and if Statements
5 LiveLab in this lesson — see the labs panel →
03 Working with Packages, Importing, and Exporting 6 topics · 1 LiveLab +
- Installing Packages
- Examining Data
- R Formulas
- More Packages
- Exploring the tidyverse
- Importing and Exporting
1 LiveLab in this lesson — see the labs panel →
04 Getting Graphic 4 topics · 4 LiveLab +
- Finding Patterns
- Doing the Basics: Base R Graphics, That Is
- Kicking It Up a Notch to ggplot2
- Putting a Bow On It
4 LiveLab in this lesson — see the labs panel →
05 Finding Your Center 7 topics · 1 LiveLab +
- Means: The Lure of Averages
- Calculating the Mean
- The Average in R: mean()
- Medians: Caught in the Middle
- The Median in R: median()
- Statistics à la Mode
- The Mode in R
1 LiveLab in this lesson — see the labs panel →
06 Deviating from the Average 3 topics · 1 LiveLab +
- Measuring Variation
- Back to the Roots: Standard Deviation
- Standard Deviation in R
1 LiveLab in this lesson — see the labs panel →
07 Meeting Standards and Standings 4 topics · 3 LiveLab +
- Catching Some Zs
- Standard Scores in R
- Where Do You Stand?
- Summarizing
3 LiveLab in this lesson — see the labs panel →
08 Summarizing It All 5 topics · 2 LiveLab +
- How Many?
- The High and the Low
- Living in the Moments
- Tuning in the Frequency
- Summarizing a Data Frame
2 LiveLab in this lesson — see the labs panel →
09 What’s Normal? 3 topics · 2 LiveLab +
- Hitting the Curve
- Working with Normal Distributions
- Meeting a Distinguished Member of the Family
2 LiveLab in this lesson — see the labs panel →
10 The Confidence Game: Estimation 4 topics · 2 LiveLab +
- Understanding Sampling Distributions
- An EXTREMELY Important Idea: The Central Limit Theorem
- Confidence: It Has Its Limits!
- Fit to a t
2 LiveLab in this lesson — see the labs panel →
11 One-Sample Hypothesis Testing 11 topics · 2 LiveLab +
- Hypotheses, Tests, and Errors
- Hypothesis Tests and Sampling Distributions
- Catching Some Z’s Again
- Z Testing in R
- t for One
- t Testing in R
- Working with t-Distributions
- Visualizing t-Distributions
- Testing a Variance
- Working with Chi-Square Distributions
- Visualizing Chi-Square Distributions
2 LiveLab in this lesson — see the labs panel →
12 Two-Sample Hypothesis Testing 10 topics · 3 LiveLab +
- Hypotheses Built for Two
- Sampling Distributions Revisited
- t for Two
- Like Peas in a Pod: Equal Variances
- t-Testing in R
- A Matched Set: Hypothesis Testing for Paired Samples
- Paired Sample t-testing in R
- Testing Two Variances
- Working with F Distributions
- Visualizing F Distributions
3 LiveLab in this lesson — see the labs panel →
13 Testing More than Two Samples 5 topics · 2 LiveLab +
- Testing More than Two
- ANOVA in R
- Another Kind of Hypothesis, Another Kind of Test
- Getting Trendy
- Trend Analysis in R
2 LiveLab in this lesson — see the labs panel →
14 More Complicated Testing 5 topics · 2 LiveLab +
- Cracking the Combinations
- Two-Way ANOVA in R
- Two Kinds of Variables … at Once
- After the Analysis
- Multivariate Analysis of Variance
2 LiveLab in this lesson — see the labs panel →
15 Regression: Linear, Multiple, and the General Linear Model 8 topics · 3 LiveLab +
- The Plot of Scatter
- Graphing Lines
- Regression: What a Line!
- Linear Regression in R
- Juggling Many Relationships at Once: Multiple Regression
- ANOVA: Another Look
- Analysis of Covariance: The Final Component of the GLM
- But Wait — There’s More
3 LiveLab in this lesson — see the labs panel →
16 Correlation: The Rise and Fall of Relationships 9 topics · 2 LiveLab +
- Understanding Correlation
- Correlation and Regression
- Testing Hypotheses about Correlation
- Correlation in R
- Multiple Correlation
- Partial Correlation
- Partial Correlation in R
- Semipartial Correlation
- Semipartial Correlation in R
2 LiveLab in this lesson — see the labs panel →
17 Curvilinear Regression: When Relationships Get Complicated 7 topics · 4 LiveLab +
- What Is a Logarithm?
- What Is e?
- Power Regression
- Exponential Regression
- Logarithmic Regression
- Polynomial Regression: A Higher Power
- Which Model Should You Use?
4 LiveLab in this lesson — see the labs panel →
18 In Due Time 4 topics · 2 LiveLab +
- A Time Series and Its Components
- Forecasting: A Moving Experience
- Forecasting: Another Way
- Working with Real Data
2 LiveLab in this lesson — see the labs panel →
19 Non-Parametric Statistics 5 topics · 4 LiveLab +
- Independent Samples
- Matched Samples
- Correlation: Spearman’s rS
- Correlation: Kendall’s Tau
- A Heads-Up
4 LiveLab in this lesson — see the labs panel →
20 Introducing Probability 11 topics · 1 LiveLab +
- What Is Probability?
- Compound Events
- Conditional Probability
- Large Sample Spaces
- R Functions for Counting Rules
- Random Variables: Discrete and Continuous
- Probability Distributions and Density Functions
- The Binomial Distribution
- The Binomial and Negative Binomial in R
- Hypothesis Testing with the Binomial Distribution
- More on Hypothesis Testing: R versus Tradition
1 LiveLab in this lesson — see the labs panel →
21 Probability Meets Regression: Logistic Regression 3 topics · 1 LiveLab +
- Getting the Data
- Doing the Analysis
- Visualizing the Results
1 LiveLab in this lesson — see the labs panel →
22 Tools and Data for Machine Learning Projects 3 topics · 1 LiveLab +
- The UCI (University of California-Irvine) ML Repository
- Introducing the Rattle package
- Using Rattle with iris
1 LiveLab in this lesson — see the labs panel →
23 Decisions, Decisions, Decisions 5 topics · 1 LiveLab +
- Decision Tree Components
- Decision Trees in R
- Decision Trees in Rattle
- Project: A More Complex Decision Tree
- Suggested Project: Titanic
1 LiveLab in this lesson — see the labs panel →
24 Into the Forest, Randomly 4 topics · 1 LiveLab +
- Growing a Random Forest
- Random Forests in R
- Project: Identifying Glass
- Suggested Project: Identifying Mushrooms
1 LiveLab in this lesson — see the labs panel →
25 Support Your Local Vector 4 topics · 1 LiveLab +
- Some Data to Work With
- Separability: It’s Usually Nonlinear
- Support Vector Machines in R
- Project: House Parties
1 LiveLab in this lesson — see the labs panel →
26 K-Means Clustering 3 topics · 1 LiveLab +
- How It Works
- K-Means Clustering in R
- Project: Glass Clusters
1 LiveLab in this lesson — see the labs panel →
27 Neural Networks 5 topics · 1 LiveLab +
- Networks in the Nervous System
- Artificial Neural Networks
- Neural Networks in R
- Project: Banknotes
- Suggested Projects: Rattling Around
1 LiveLab in this lesson — see the labs panel →
28 Exploring Marketing 3 topics · 1 LiveLab +
- Analyzing Retail Data
- Enter Machine Learning
- Suggested Project: Another Data Set
1 LiveLab in this lesson — see the labs panel →
29 From the City That Never Sleeps 6 topics · 1 LiveLab +
- Examining the Data Set
- Warming Up
- Quick Suggested Project: Airline Names
- Suggested Project: Departure Delays
- Quick Suggested Project: Analyze Weekday Differences
- Suggested Project: Delay and Weather
1 LiveLab in this lesson — see the labs panel →
30 Working with a Browser 5 topics · 1 LiveLab +
- Getting Your Shine On
- Creating Your First shiny Project
- Working with ggplot
- Another shiny Project
- Suggested Project
1 LiveLab in this lesson — see the labs panel →
31 Dashboards — How Dashing! 4 topics · 1 LiveLab +
- The shinydashboard Package
- Exploring Dashboard Layouts
- Working with the Sidebar
- Interacting with Graphics
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
57 LiveLabs- Performing Basic Operations
- Creating and Using Custom Functions
- Creating and Working with Data Frames
- Working with Matrices
- Using for Loops and if-else Statements
- Analyzing Data
- Creating a Scatter Plot and a Box Plot
- Creating a Bar Plot and a Pie Graph
- Creating a Histogram and a Density Plot
- Creating a Grouped Bar Plot with ggplot2
- Calculating the Mean, Median, and Mode
- Finding Variance and Standard Deviation
- Calculating Percentiles
- Finding Nth Smallest and Nth Largest Elements
- Handling Tied Ranks
- Calculating Skewness and Kurtosis in Data
- Analyzing Frequency in Data
- Exploring Quantiles of a Normal Distribution
- Visualizing the Normal Distribution Curve
- Simulating the Central Limit Theorem
- Calculating Confidence Intervals Using the T-Distribution
- Performing the Z-Test
- Analyzing a T-Distribution
- Performing a Z-Test for Two Samples
- Performing a T-Test for Two Samples
- Visualizing F Distributions
- Performing Repeated Measures ANOVA
- Performing Trend Analysis
- Performing Two-Way ANOVA
- Performing Mixed ANOVA
- Creating a Linear Regression Model
- Creating a Multiple Regression Model
- Performing ANCOVA
- Performing Correlation Analysis
- Performing Partial Correlation Analysis
- Creating a Power Regression Model
- Creating an Exponential Regression Model
- Creating a Logarithmic Regression Model
- Creating a Polynomial Regression Model
- Analyzing Time Series Data
- Creating Forecasts Using Moving Averages
- Performing the Kruskal-Wallis Rank-Sum Test
- Performing the Wilcoxon Rank-Sum Test
- Performing the Cochran’s Q Test
- Performing the Friedman Rank-Sum Test
- Exploring Binomial Distribution
- Creating a Logistic Regression Model
- Performing EDA
- Creating a Decision Tree Model
- Creating a Random Forest Model
- Creating an SVM Model
- Creating Clusters
- Creating a Neural Network Model
- Performing RFM Analysis
- Performing Advanced Data Analysis
- Analyzing Data Using the shiny App
- Creating a shiny Dashboard
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What are the benefits of learning R programming?+
Some of the many benefits are listed below:
- R is the most versatile and adaptable programming language.
- It supports a wide range of LM algorithms for predictive modeling.
- It is an open-source software available for free usage and distribution.
What are the prerequisites?+
There are no prerequisites for taking this R programming course. It is a beginner-friendly course that starts with the basic programming concepts and gradually proceeds to advanced topics.
Can this course help with data science/data analytics projects?+
Yes, R programming is used for data science and data analytics projects. You’ll gain a strong foundation on the following R programming and data analysis techniques:
- Cleaning and preprocessing data
- Perform exploratory data analysis (EDA)
- Build statistical models
- Implement ML algorithms
- Create interactive data visualizations
- Develop data-driven solutions
How does this course help with career development?+
Learning R programming can benefit your career by making you a highly sought-after candidate for data science, analytics, and research roles. It opens doors to many exciting job opportunities with higher salaries.
Keep going
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