uCertify

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.

(R-BASIC.AE1) / ISBN : 978-1-64459-542-8
Lessons
Lab
AI Tutor (Add-on)
Get A Free Trial

About This Course

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.

Skills You’ll Get

  • 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
Download Course Outline

1

Introduction

  • About This All-in-One
  • What You Can Safely Skip
  • Icons Used in This Course
  • Where to Go from Here
2

R: What It Does and How It Does It

  • 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
3

Working with Packages, Importing, and Exporting

  • Installing Packages
  • Examining Data
  • R Formulas
  • More Packages
  • Exploring the tidyverse
  • Importing and Exporting
4

Getting Graphic

  • Finding Patterns
  • Doing the Basics: Base R Graphics, That Is
  • Kicking It Up a Notch to ggplot2
  • Putting a Bow On It
5

Finding Your Center

  • 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
6

Deviating from the Average

  • Measuring Variation
  • Back to the Roots: Standard Deviation
  • Standard Deviation in R
7

Meeting Standards and Standings

  • Catching Some Zs
  • Standard Scores in R
  • Where Do You Stand?
  • Summarizing
8

Summarizing It All

  • How Many?
  • The High and the Low
  • Living in the Moments
  • Tuning in the Frequency
  • Summarizing a Data Frame
9

What’s Normal?

  • Hitting the Curve
  • Working with Normal Distributions
  • Meeting a Distinguished Member of the Family
10

The Confidence Game: Estimation

  • Understanding Sampling Distributions
  • An EXTREMELY Important Idea: The Central Limit Theorem
  • Confidence: It Has Its Limits!
  • Fit to a t
11

One-Sample Hypothesis Testing

  • 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
12

Two-Sample Hypothesis Testing

  • 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
13

Testing More than Two Samples

  • Testing More than Two
  • ANOVA in R
  • Another Kind of Hypothesis, Another Kind of Test
  • Getting Trendy
  • Trend Analysis in R
14

More Complicated Testing

  • Cracking the Combinations
  • Two-Way ANOVA in R
  • Two Kinds of Variables … at Once
  • After the Analysis
  • Multivariate Analysis of Variance
15

Regression: Linear, Multiple, and the General Linear Model

  • 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
16

Correlation: The Rise and Fall of Relationships

  • 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
17

Curvilinear Regression: When Relationships Get Complicated

  • What Is a Logarithm?
  • What Is e?
  • Power Regression
  • Exponential Regression
  • Logarithmic Regression
  • Polynomial Regression: A Higher Power
  • Which Model Should You Use?
18

In Due Time

  • A Time Series and Its Components
  • Forecasting: A Moving Experience
  • Forecasting: Another Way
  • Working with Real Data
19

Non-Parametric Statistics

  • Independent Samples
  • Matched Samples
  • Correlation: Spearman’s rS
  • Correlation: Kendall’s Tau
  • A Heads-Up
20

Introducing Probability

  • 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
21

Probability Meets Regression: Logistic Regression

  • Getting the Data
  • Doing the Analysis
  • Visualizing the Results
22

Tools and Data for Machine Learning Projects

  • The UCI (University of California-Irvine) ML Repository
  • Introducing the Rattle package
  • Using Rattle with iris
23

Decisions, Decisions, Decisions

  • Decision Tree Components
  • Decision Trees in R
  • Decision Trees in Rattle
  • Project: A More Complex Decision Tree
  • Suggested Project: Titanic
24

Into the Forest, Randomly

  • Growing a Random Forest
  • Random Forests in R
  • Project: Identifying Glass
  • Suggested Project: Identifying Mushrooms
25

Support Your Local Vector

  • Some Data to Work With
  • Separability: It’s Usually Nonlinear
  • Support Vector Machines in R
  • Project: House Parties
26

K-Means Clustering

  • How It Works
  • K-Means Clustering in R
  • Project: Glass Clusters
27

Neural Networks

  • Networks in the Nervous System
  • Artificial Neural Networks
  • Neural Networks in R
  • Project: Banknotes
  • Suggested Projects: Rattling Around
28

Exploring Marketing

  • Analyzing Retail Data
  • Enter Machine Learning
  • Suggested Project: Another Data Set
29

From the City That Never Sleeps

  • 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
30

Working with a Browser

  • Getting Your Shine On
  • Creating Your First shiny Project
  • Working with ggplot
  • Another shiny Project
  • Suggested Project
31

Dashboards — How Dashing!

  • The shinydashboard Package
  • Exploring Dashboard Layouts
  • Working with the Sidebar
  • Interacting with Graphics

1

R: What It Does and How It Does It

  • Performing Basic Operations
  • Creating and Using Custom Functions
  • Creating and Working with Data Frames
  • Working with Matrices
  • Using for Loops and if-else Statements
2

Working with Packages, Importing, and Exporting

  • Analyzing Data
3

Getting Graphic

  • 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
4

Finding Your Center

  • Calculating the Mean, Median, and Mode
5

Deviating from the Average

  • Finding Variance and Standard Deviation
6

Meeting Standards and Standings

  • Calculating Percentiles
  • Finding Nth Smallest and Nth Largest Elements
  • Handling Tied Ranks
7

Summarizing It All

  • Calculating Skewness and Kurtosis in Data
  • Analyzing Frequency in Data
8

What’s Normal?

  • Exploring Quantiles of a Normal Distribution
  • Visualizing the Normal Distribution Curve
9

The Confidence Game: Estimation

  • Simulating the Central Limit Theorem
  • Calculating Confidence Intervals Using the T-Distribution
10

One-Sample Hypothesis Testing

  • Performing the Z-Test
  • Analyzing a T-Distribution
11

Two-Sample Hypothesis Testing

  • Performing a Z-Test for Two Samples
  • Performing a T-Test for Two Samples
  • Visualizing F Distributions
12

Testing More than Two Samples

  • Performing Repeated Measures ANOVA
  • Performing Trend Analysis
13

More Complicated Testing

  • Performing Two-Way ANOVA
  • Performing Mixed ANOVA
14

Regression: Linear, Multiple, and the General Linear Model

  • Creating a Linear Regression Model
  • Creating a Multiple Regression Model
  • Performing ANCOVA
15

Correlation: The Rise and Fall of Relationships

  • Performing Correlation Analysis
  • Performing Partial Correlation Analysis
16

Curvilinear Regression: When Relationships Get Complicated

  • Creating a Power Regression Model
  • Creating an Exponential Regression Model
  • Creating a Logarithmic Regression Model
  • Creating a Polynomial Regression Model
17

In Due Time

  • Analyzing Time Series Data
  • Creating Forecasts Using Moving Averages
18

Non-Parametric Statistics

  • 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
19

Introducing Probability

  • Exploring Binomial Distribution
20

Probability Meets Regression: Logistic Regression

  • Creating a Logistic Regression Model
21

Tools and Data for Machine Learning Projects

  • Performing EDA
22

Decisions, Decisions, Decisions

  • Creating a Decision Tree Model
23

Into the Forest, Randomly

  • Creating a Random Forest Model
24

Support Your Local Vector

  • Creating an SVM Model
25

K-Means Clustering

  • Creating Clusters
26

Neural Networks

  • Creating a Neural Network Model
27

Exploring Marketing

  • Performing RFM Analysis
28

From the City That Never Sleeps

  • Performing Advanced Data Analysis
29

Working with a Browser

  • Analyzing Data Using the shiny App
30

Dashboards — How Dashing!

  • Creating a shiny Dashboard

Any questions?
Check out the FAQs

Get more information about this R programming course and its application.

Contact Us Now

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.

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.

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

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.

Learn R: The Language of Data Science

  Level up your data skills with this comprehensive yet beginner-friendly course.

$239.99

Buy Now

Related Courses

All Course