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DS102 Statistical Programming in R

Course Description: The Statistical Programming course teaches students how to load R and R Studio onto their PC. Students will then learn basic scripting commands and will be introduced to a vast library of functions to perform various statistical analyses.
Quarter Credit Hours 4.5
Course Length: 60 hours
Prerequisites: None
Proficiency Exam: No
Theory Hours: 30
Laboratory Hours: 30
Externship Hours: 0
Outside Hours: 15
Total Contact Hours: 60

Required Resources:

  • Ground-based students are required to bring a late model laptop computer (either PC or MacBook) to class every day.

  • Online students are required to have a late model laptop or desktop computer with internet access.

  • Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.

  • Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.

  • Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.

  • It is a requirement that you are able to download programming resources to your laptop/desktop for this class. (This means you need a steady internet high bandwidth connection.)

  • You are required to have a quiet place to study and to be able to focus on the material.

  • You are required to have uninterrupted weekly 1:1 video meetings with your mentor.

  • You are required to log into the Learning Management System (LMS) daily for at least 20 minutes.

  • Please follow and review each lesson page by page coding examples provided as this will ensure you have a full understanding for your final hands-on assignments.

Course Outline:

Module Lesson Number Lesson Name
DS102 Statistical Programming in R 1 Thinking Like a Programmer
2 Introduction to R
3 Variables, Functions, and For Loops
4 Vectors and Sample Statistics
5 Statistical Plots
6 Data Frames
7 t-Tests
8 Linear Regression
9 Data Exploration
10 Final Project

Educational Objectives:

Upon successful completion of this course, students will be able to:

  1. Load R onto your PC
  2. Load R Studio onto your PC
  3. Explain the idiosyncrasies of R compared to other languages
  4. Create a script file to execute repeated complex commands
  5. Access a data set
  6. Use R to manipulate data (filtering, coding, sorting, creating new variables)
  7. Use “If”, “For”, and “While”
  8. Use vectors in R
  9. Use some basic visualization tools

Lessons:

Week 1

  1. Introduction to R: Getting Started with R, What is R, Installation of R for Windows, Installation of R for Mac/Linux, Initiation as an R Programmer, The R Console, R Script Files, Finding Documentation and Help, and Summary.
  2. Calculating with R: Introduction, A Note About Errors, Strings, Arithmetic Operations, Functions, and Summary.
  3. Scripts and RStudio: Introduction, Scripts, The For Loop, and Summary.

Week 2

  1. Vectors and Sample Statistics: Introduction, Vectors, Logical Variables and Vectors, Sample Statistics, and Summary.
  2. Statistical Plots: Introduction, Installing ggplot2, Histograms, Box Plots, Normal Probability Plots, and Summary.
  3. Data Frames: Introduction, Data Frame Basics, Importing and Exporting Data, Analyzing Data Grouped by Factors, and Summary.
  4. Linear Regression: Introduction, Scatter Plots, Correlation, Linear Regression, and Summary.

Week 3

  1. Confidence Intervals and Hypothesis Tests: Introduction, Confidence Intervals on the Mean, Hypothesis Tests on the Mean, and Summary.
  2. Data Exploration: Introduction, Looking at the Big Picture, Comparing Countries, A Statistical Summary, and Summary.
  3. Final Project

Workshops:

Class: DS102 Topic presented Lesson
Week 1 R Language Basics 1
Getting to Know R Studio 2,3
Week 2 Stats and Plots in R 5
Working with Data Frames 6
Week 3 Hypothesis Testing 8
Practice Project 10

Points Distribution:

Exam Points Activity
L1 5 Exercise simple R fundamentals
L2 0 Calculating with R: Calculate confidence intervals in R using the qnorm() function
L3 11 Create a function and for loop to compute the diameter of a sphere
L4 0 Compute summary statistics on vectors
L5 11 Graph data using ggplot() and assess outliers and normality
L6 0 Use tidyr functions to manipulate a data frame
L7 0 Compute and explain linear regression
L8 11 Manipulate vectors and compute a t-test using R
L9 12 Explore data using R
L10 45 Final Project

Points Total:

Type Points
Professionalism, Attendance and Class Participation*: 5 points (5%)
Assignment/Hands-On/Homework: 45 points (45%)
Exam/Quiz Average: 5 points (5%)
Projects/Competencies/Research: 45 points: (45%)
Total points: 100 (100%)

Final Project:

Graph data, calculate correlations, regressions, and t-tests.