ITE 203: Introduction to R Programming

Course Description

Introduction to R Programming provides students with a foundational understanding of R, a powerful language for statistical computing and data analysis. The course covers essential programming concepts in R, including data structures, control flow, and functions, while emphasizing its use for statistical analysis and data visualization. Students will learn how to manipulate datasets, perform basic statistical tests, and create compelling visualizations. By the end of the course, students will be able to use R to efficiently analyze and interpret data, making it an essential tool for data science and research applications.(3 credits)

Prerequisite

  • None

Student Learning Outcomes (SLOs)

Students who successfully complete this course will be able to:

  1. Explain fundamental programming concepts in R, including data types, data structures, control flow, and functions, and illustrate.
  2. Manipulate datasets in R by cleaning, organizing, and transforming data to prepare it for statistical analysis, using functions such as filter(), mutate(), and select().
  3. Conduct basic statistical analyses in R, including descriptive statistics, hypothesis testing, and correlation, to interpret and summarize data insights.
  4. Create data visualizations in R using libraries such as ggplot2 to effectively communicate data patterns, trends, and relationships to diverse audiences.
  5. Develop custom functions in R to automate repetitive tasks and optimize data analysis workflows for efficiency and scalability.
  6. Evaluate the quality and integrity of datasets by checking for issues such as missing values, duplicates, and outliers, and apply data preparation techniques to address these issues.
  7. Create SQL for database access using R.

Course Activities and Grading

AssignmentsWeight

Discussions (Weeks 1-8)

6%

Quizzes, Challenges & Checkpoints (Weeks 1-8)

34%

Labs (Weeks 1-8)

60%

Total

100%

Required Textbooks

  • This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.

Course Schedule

Week

SLOs

Readings and Exercises

Assignments

1

1

Topic: Introduction to R

  •  Readings:
    • Data Analysis with R Programming - Module 1

 

  • Read assigned material
  • Review the lecture material
  • Participate in the Discussions
  • Submit the Week 1 challenge

2

1

Topic: Installing and Using R

  •  Readings:
    • Data Analysis with R Programming - Module 1
    • Introduction to R Programming for Data Science - Modules 2 & 3
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 2 quizzes and labs

3

1

Topic: Using RStudio

  •  Readings:
    • Data Analysis with R Programming - Module 2
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 3 challenge and labs

4

2,3,6

Topic: Working with Data in R (Part 1)

  •  Readings:
    • Data Analysis with R Programming - Module 3
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 4 challenge and labs

5

2,3,5,6

Topic: Working with Data in R (Part 2)

  • Readings:
    • Data Analysis with R - Modules 2-5
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 5 quizzes and labs

6

3,4

Topic: Visualizations with R

  •  Readings:
    • Data Visualization with R - Modules 1-4 
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 6 quizzes and labs

7

7

Topic: R, SQL and Databases

  •  Readings:
    • SQL for Data Science with R - Modules 1, 2, 4 & 5
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 7 quizzes and labs

8

1,2,3

Topic: Data Science with R

  •  Readings:
    • Data Science with R - Capstone Project - Modules 1-5
  • Read assigned material
  • Participate in the Discussions
  • Submit the Week 8 checkpoints and labs
  • Complete Course Evaluation

COSC Accessibility Statement

Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.

COSC Policies, Course Policies, Academic Support Services and Resources

Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.