CS 114: Programming in R

Ready to learn the basics of R programming? Our course covers structures, operations, control flow, data processing, acquisition, cleaning, and transformation. With expert instruction and hands-on learning, you'll gain the skills you need to succeed.

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Course curriculum

1. Ch 1.1 R Programming Language: Uses & Examples
2. R Programming Language
3. What is R used for?
4. Features of R
5. R Programming Examples
6. Lesson Summary
7. Frequently Asked Questions
8. Quiz 1.1
1. Introduction
2. Why Learn R?
3. Data Science and Statistics
4. Artificial Intelligence and Machine Learning
5. Other Fields of Study
6. Lesson Summary
7. Quiz 1.2
1. Introduction
2. R Programming
3. Setting Up an R Programming Environment
4. Lesson Summary
5. Quiz 1.3
1. Introduction
2. Putting the ''R'' in Versatile
3. What Is a Paradigm Anyway?
4. R is Functional
5. R is Object-Oriented
6. Lesson Summary
7. Quiz 1.4
1. Introduction
2. R Programming
3. Statements
4. Functions
5. Assigning Values to Variables
6. History of Commands
7. Comments
8. Help
9. Sample Statements - Hello World
10. Lesson Summary
11. Quiz 1.5
1. Introduction
2. Lesson Overview & Knowledge Required
3. Program Code
4. Code Application
5. Follow-Up Questions
1. Ch 2.1 Interactive Mode vs. Batch Mode in R Programming
2. Batch Processing
3. Interactive Processing
4. Data Processing in R
5. Comparing Batch Processing and Interactive Processing
6. Lesson Summary
7. Quiz 2.1
1. Introduction
2. Vital Variables
3. R Variables
4. Naming Conventions
5. Pick a Style and Stick to It!
6. Data Types
7. Assigning Values to Variables
8. What Variables Did I Create?
9. Lesson Summary
10. Quiz 2.2
1. Introduction
2. Vectors in R
3. Colon Operator
4. The Sequence Function
5. The c() Function
6. Accessing Elements
7. Lesson Summary
8. Quiz 2.3
1. Introduction
2. The R Programming Language
3. Calling Functions in R
4. Common Functions in R
5. Finding a Function
6. Lesson Summary
7. Quiz 2.4
1. Introduction
2. Getting Help with R
3. R Has Built-in Help
4. How to Use the R Help System
5. Summary
6. Quiz 2.5
1. Introduction
2. Lesson Overview & Knowledge Required
3. Program Code
4. Mean
5. Median
6. Range
7. Code Application
8. Follow-Up Questions
1. Ch 3.1 Data Structures in R Programming
2. What are Data Structures?
3. Types of Data Structures
4. Lesson Summary
5. Quiz 3.1
1. Introduction
2. Vectors and R
3. R Calculations
4. Square Root and Exponents
5. Statistical Functions
6. Common Properties of Vectors
7. Lesson Summary
8. Quiz 3.2
1. Introduction
2. Scalars in R
3. Lesson Summary
4. Quiz 3.3
1. Introduction
2. The R Character String
3. Creating a String
4. Character String Functions
5. Lesson Summary
6. Quiz 3.4
1. Introduction
2. Data Structures in R Programming
3. Lists and How to Create Them
4. Working with Lists
5. Lesson Summary
6. Quiz 3.5
1. Introduction
2. Data Frames in R Programming
3. Fundamental Properties
4. Creating a Data Frame Manually
5. Importing a CSV File
6. Manipulating Data Frames
7. Lesson Summary
8. Quiz 3.6
1. Introduction
2. Data Frames in R Programming
3. Fundamental Properties
4. Creating a Data Frame Manually
5. Importing a CSV File
6. Manipulating Data Frames
7. Lesson Summary
8. Quiz 3.7
1. Introduction
2. Inheritance
3. Inheritance in S3
4. Inheritance in S4
5. Lesson Summary
6. Quiz 3.8
1. Introduction
2. Operation!
3. Data Structures
4. Operations on Data Structures
5. A Final Note
6. Lesson Summary
7. Quiz 3.9
1. Introduction
2. The R language
3. Arithmetic Operations in R
4. Range of Arithmetic Operations Supported by R
5. Building Arithmetic Operations
6. Lesson Summary
7. Quiz 3.10
1. Introduction
2. Lesson Overview & Knowledge Required
3. Understanding the Problem in Terms of R
4. Program Code
5. Code Application
6. Follow-Up Questions
1. Ch 4.1 Control Flow in R Programming: Definition & Example
2. Control Flow in R
3. What is a Programming Language
4. Lesson Summary
5. Quiz 4.1
1. Introduction
2. What Is a Conditional Expression?
3. Conditional Expression in R Language
4. Comparison and Logical Operators in R Programming
5. Lesson Summary
6. Frequently Asked Questions
7. Quiz 4.2
1. Introduction
2. Recursion in R
3. Factorial
4. Sum of Series
5. Towers of Hanoi
6. Advanced Towers
7. Fibonacci Sequence - Built-In Function
8. Lesson Summary
9. Quiz 4.3
1. Introduction
2. if-else Statements
3. Loops
4. lesson Summary
5. Quiz 4.4
1. Introduction
2. Repetition and Iteration
3. Types of iteration
4. Lesson Summary
5. Quiz 4.5
1. Introduction
2. R Programming vs. Compiled Languages
3. Types of Iteration
4. Some Background on Implied Iteration
5. Lesson Summary
6. Quiz 4.6
1. Introduction
2. What's Your Vector?
3. Vector Sequencing Expressions
4. Lesson Summary
5. Quiz 4.7
1. Introduction
2. Any and All Functions?!
3. Any
4. All
5. Lesson Summary
6. Quiz 4.8
1. Introduction
2. Data Structures
3. For loop
4. Summary
5. Quiz 4.9
1. Introduction
2. User-Defined Functions
3. Using User-Defined Functions
4. Lesson Summary
5. Quiz 4.10
1. Introduction
2. Flow of Control
3. Defining Functions
4. Flow of Control with Functions
5. Lesson Summary
6. Quiz 4.11
1. Lesson Overview & Knowledge Required
2. Program Code
3. Code Application
4. Follow-Up Questions
1. Assignment Location & Info
1. Ch 5.1 Data Processing: Definition & Functions
2. Digital Era
3. Data Processing
4. Operations in A Data Processing Program
5. Emerging Trends in Data Processing
6. Lesson Summary
7. Quiz 5.1
1. Introduction
2. Elementary Data Processing
3. Lesson Summary
4. Quiz 5.2
1. Introduction
2. Complex Data
3. Complex Relationships
4. Other Types of Complex Data
5. Lesson Summary
6. Quiz 5.3
1. Introduction
2. Data Science and Matrices in R
3. Matrices
4. Using Apply
5. Transpose: Brief Introduction
6. Lesson Summary
7. Quiz 5.4
1. Introduction
2. Working With Outliers in Our Data
3. Load Precip Sample Data
4. Box Plot
5. Two Variables
6. The Outliers Package
7. Only One Value
8. After Identifying Outliers
9. Methods for Other Data Types
10. Lesson Summary
11. Quiz 5.5
1. Introduction
2. Data Analysis
3. Data Extraction
4. Cleaning Data
5. Dropping Rows with NA Value
6. Replacing NA Values with Zero
7. Replacing NA with Average Values
8. Lesson Summary
9. Quiz 5.6
1. Introduction
2. Data Transformation
3. Lesson Summary
4. Quiz 5.7
1. Lesson Overview & Knowledge Required
2. Program Code
3. Code Application
4. Follow-up Questions
5. Code Application
1. Ch 6.1 Data Acquisition in R: Definition & Approaches
2. Data Acquisition in R
3. Data Formatted as Tables
4. Non-Standard Formatted Data
5. Excel Files
6. Data from Popular Statistics Programs
7. Lesson Summary
8. Quiz 6.1
1. Introduction
2. Data Types in R:
3. Atomic Vectors
4. Arrays and Matrices
5. Heterogeneous Data
6. List Data Structures for Heterogeneous Types
7. Data Frames for Heterogeneous Types
8. Lesson Summary
9. Quiz 6.2
1. Introduction
2. Reading Tab-Delimited Files Using R
3. Beyond the Default Arguments
4. Help in R
5. Lesson Summary
6. Quiz 6.3
1. Introduction
2. Reading CSV Files Using R
3. Beyond the Default Arguments
4. Lesson Summary
5. Quiz 6.4
1. Introduction
2. Reading Text Files in R
3. The read.table Function
4. Acquiring Data Using read.table in R
5. The read.fwf Function
6. Acquiring Data Using read.fwf in R
7. Lesson Summary
8. Quiz 6.5
1. Introduction
2. Reading Data into R with readLines
3. The readLines Function
4. Using readLines to Load Data
5. readLines Function Output Data Type
6. Processing readLines Data
7. Lesson Summary
8. Quiz 6.6
1. Introduction
2. Pattern Matching
3. Grep Function
4. Grep vs Grepl
5. Data Acquisition Usage
6. Lesson Summary
7. Quiz 6.7
1. Introduction
2. Separating Data into Fields
3. Using a Single Character for the 'split' Argument
4. Using More than One Character for the 'split' Argument
5. Using Regular Expressions (regex)
6. Lesson Summary
7. Quiz 6.8
1. Introduction
2. Are Your Rows Up to Par?
3. Rows/Data Standardization
4. R Language Implementation
5. Lesson Summary
6. Quiz 6.9
1. Introduction
2. Working with Data
3. Data Cleaning
4. Data Acquisition, Analysis and Reporting
5. Transforming Data to a Data Frame
6. Wide vs. Long Data
7. Lesson Summary
8. Quiz 6.10
1. Lesson Overview & Knowledge Required
2. Program Code
3. Code Application
4. Follow-Up Questions
1. Ch 7.1 Data Cleaning in R: Definition & Types
2. Cooking recipes for data!
3. Data Cleaning Definition
4. Data Cleaning Types/Steps
5. Lesson Summary
6. Quiz 7.1
1. Introduction
2. What's Your Type?
3. R's Data Types
4. Data Type Conversion
5. The NA Result
6. Vector Conversion
7. What's My Type Again?
8. Lesson Summary
9. Quiz 7.2
1. Introduction
2. Why Recoding?
3. How to Recode
4. Uses
5. Lesson Summary
6. Quiz 7.3
1. Introduction
2. Date Handling in R
3. Dates and Display Formats
4. Date Conversions
5. Lesson Summary
6. Quiz 7.4
1. Introduction
2. Preparing Data with R
3. Simple Character Operations in R
4. More Character Manipulation Functions
5. Lesson Summary
6. Quiz 7.5
1. Introduction
2. Removing Spaces From Strings
3. Dealing with Extra Spaces
4. Counting and Sorting
5. String Comparison
6. URL Encoding
7. Lesson Summary
8. Quiz 7.6
1. Introduction
2. String Padding in R
3. Padding with a Non-Space Character
4. Padding using Default Options
5. Lesson Summary
6. Quiz 7.7
1. Introduction
2. Functions in R to Convert Character Case
3. Converting a String with Alphabets, Numbers and Special Characters
4. Lesson Summary
5. Quiz 7.8
1. Introduction
2. Locating Strings and Substrings is R Programming
3. What is a Substring?
4. Recoding
5. Lesson Summary
6. Quiz 7.9
1. Introduction
2. Finding String Lookalikes in R!
3. Approximate String Matching and String Distance
4. R Implementation and Examples
5. Lesson Summary
6. Quiz 7.10
1. Introduction
2. Softening Strings with Rough Edges in R
3. String Normalization
4. R Implementation
5. Lesson Summary
6. Quiz 7.11
1. Introduction
2. Data Errors vs. Technically Correct Data
3. Data Cleaning, Errors and Inconsistencies
4. Lesson Summary
5. Quiz 7.12
1. Introduction
2. Missing in Action
3. Special Values and Outliers
4. Handling Missing Values in R
5. Lesson Summary
6. Quiz 7.13
1. Introduction
2. Data Inconsistencies
3. Inconsistencies vs. Outliers
4. Inconsistencies vs. Errors
5. Example of Identifying and Correcting Data Inconsistencies
6. A Note About Mean Imputation
7. Lesson Summary
8. Quiz 7.14
1. Lesson Overview & Knowledge Required
2. Program Code
3. Code Application
4. Follow-Up Questions
1. Ch 8.1 Data Transformation in R Programming: Definition & Purpose
2. Introduction
3. Purpose of Data Transformation
4. Data Transformation R Functions
5. Power/Root Transformation Functions/Operations
6. Log Transformation Functions
7. Lesson Summary
8. Quiz 8.1
1. Introduction
2. What is Tidyverse?
3. R Programming
4. Using Packages in Tidyverse
5. Ggplot2 in Tidyverse
6. Tibble in Tidyverse
7. Tidyr in Tidyverse
8. Readr in Tidyverse
9. Dplyr in Tidyverse
10. Stringr in Tidyverse
11. Purr in Tidyverse
12. Forcats in Tidyverse
13. Lesson Summary
14. Quiz 8.2
1. Introduction
2. Get Out Your Pliers, It's Time for Dplyr!
3. Installing Dplyr
4. Load Dplyr
5. Dplyr Functions
6. Lesson Summary
7. Quiz 8.3
1. Introduction
2. Filter in R Programming
3. Dplyr
4. 'Mtcars' Dataset
5. Using ''Filter''
6. Lesson Summary
7. Quiz 8.4
1. Introduction
2. Library dplyr
3. Data Frame
4. Arrange Function
5. Another example
6. Lesson Summary
7. Quiz 8.5
1. Introduction
2. Select Functioning in R Programming
3. The Dplyr Package
4. An Example of Using Select
5. A Bit More on Select
6. Lesson Summary
7. Quiz 8.6
1. Introduction
2. Mutate in R
3. Using the Mutate Function
4. Tibble
5. Lesson Summary
6. Quiz 8.7
1. Introduction
2. What is Sum in R Programming?
3. The Summary Function in R Programming
4. Summary Tables in R Programming
5. The Group_By Function in R Programming
6. The ColSums Function in R
7. The T-Test Function in R
8. A Summary of Dplr and the Pipe Operator
9. Lesson Summary
10. Frequently Asked Questions
11. Quiz 8.8
1. Lesson Overview & Knowledge Required
2. Program Code
3. Install Packages
4. Convert Mtcars to Tibble
5. Add Column Name for the Car
6. Program Code
7. The Pipe Operator
8. Code Application
9. Follow-Up Questions
1. Ch 9.1 Programming in R - Assignment: Acquiring, Cleaning & Transforming Data
1. Final Exam

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Introduction to Programming in Java

Database Fundamentals

CS 373: Introduction to Artificial Intelligence

Ch 1. Introduction to R Programming

Ch 1.2 R Programming: Purpose & Real-World Applications

Ch 1.3 R Programming Environment: Steps, Purpose & Set-up

Ch 1.4 Programming Paradigm of R: Definition & Type

Ch 1.5 Constructing R Programming Language Statements: Examples & Statements

Ch 1.6 Practical Application Lesson: Constructing R Programming Language Statements

Ch 2. Characteristics of R Programming

Ch 2.2 Variables in R Programming

Ch 2.3 Basic Vectors in R Programming: Definition & Purpose

Ch 2.4 Built-In Functions in R Programming

Ch 2.5 Using the Built-In Help Function in R Programming

Ch 2.6 Practical Application for Programming in R: Using Built-In Functions in R

Ch 3. Structures & Operations of R Programming

Ch 3.2 Vector Data Structure in R Programming: Purpose & Examples

Ch 3.3 Scalar Data Type in R Programming: Definition & Function

Ch 3.4 Character Strings in R Programming: Type & Purpose

Ch 3.5 Lists in R Programming: Purpose & Examples

Ch 3.6 Data Frames in R Programming: Properties & Manipulation

Ch 3.7 Data Frames in R Programming: Properties & Manipulation

Ch 3.8 Inheritance in R Programming: Types & Examples

Ch 3.9 Operations on Data Structures in R Programming

Ch 3.10 Arithmetic Operations in R Programming

Ch 3.11 Practical Application for Programming in R: Operations & Data Structures in R Programming

Ch 4. Control Flow in R Programming

Ch 4.2 Conditional Expressions in R Programming

Ch 4.3 Recursion in R Programming: Application & Examples

Ch 4.4 Creating Flow Altering Conditional Statements in R Programming

Ch 4.5 Iteration in R Programming: Definition & Instruction Creation

Ch 4.6 Implied Iteration in R Programming

Ch 4.7 Vector Sequencing in R Programming

Ch 4.8 Any and All Functions in R Programming

Ch 4.9 Repetition Across Data Structures in R Programming

Ch 4.10 User Defined Function in R Programming: Creation & Use

Ch 4.11 Functions That Serve As Flow of Control in R Programming

Ch 4.12 Practical Application for Programming in R: Using Flow Control Functions

Ch 4.13 Required Assignments Reminder

Ch 5. Data Processing in R Programming

Ch 5.2 Complex Data Processing in R Programming

Ch 5.3 Complex Data Set Operations & Transformations in R Programming

Ch 5.4 Manipulating Matrices Using Apply & Transpose in R Programming

Ch 5.5 Finding Outliers in a Matrix in R Programming

Ch 5.6 Extraction & Cleaning of Data in R Programming

Ch 5.7 Transformation of Data in R Programming

Ch 5.8 Practical Application for Programming in R: Data Processing in R

Ch 6. Data Acquisition in R Programming

Ch 6.2 Pairing Data Structures to Data Types in R Programming

Ch 6.3 Acquiring Data Using read.delim & read.delim2 Functions in R

Ch 6.4 Acquiring Data Using read.csv & read.csv2 Functions in R

Ch 6.5 Acquiring Data Using read.table & read.fwf Functions in R

Ch 6.6 Acquiring Data Using readLines Functions in R Programming

Ch 6.7 Grep & Grepl Functions in R: Definition & Purpose

Ch 6.8 Splitting Lines Into Separate Fields in R Programming

Ch 6.9 Standardizing Rows in R Programming

Ch 6.10 Transforming Data to Data Frame in R Programming

Ch 6.11 Practical Application for Programming in R: Acquiring Data from Multiple Sources

Ch 7. Cleaning Data in R Programming

Ch 7.2 Data Type Conversion in R Programming: Purpose & Functions

Ch 7.3 Recoding Data in R Programming: Process & Application

Ch 7.4 Date Conversion in R Programming

Ch 7.5 Character Manipulation in R Programming

Ch 7.6 Removing Space From Strings in R Programming

Ch 7.7 String Padding to a Specified Width in R Programming

Ch 7.8 Transforming Strings to Upper & Lower Case in R Programming

Ch 7.9 Locating Strings & Substrings in R Programming

Ch 7.10 Approximate String Matching & String Distance in R Programming

Ch 7.11 String Normalization in R Programming

Ch 7.12 Detecting Data Errors in R Programming

Ch 7.13 Missing Values, Special Values & Outliers in R Programming

Ch 7.14 Inconsistent Data in R Programming

Ch 7.15 Practical Application for Programming in R: Data Cleaning in R

Ch 8. Transforming Data in R Programming