Traditional Teaching Methods

Teaching Statistics with RStudio

• Many statistics courses incorporate R for data analysis and visualization.
• Typically, lectures cover statistical concepts, and practical workshops focus on R programming.
• Including R programming examples in lectures can be challenging due to window switching and limited student engagement.

Traditional Teaching Methods: Screenshots and Code

Traditional Teaching Methods: Screenshots and Code

Static Screenshot in RStudio

Traditional Teaching Methods: Screenshots and Code

Static Code in RStudio (Better)

# Load necessary package
library(stats)

# Conduct a two-sample t-test
set.seed(123)
group_A <- rnorm(30, mean = 100, sd = 15)  # Sample data for group A
group_B <- rnorm(30, mean = 105, sd = 15)  # Sample data for group B

# Perform t-test
t.test(group_A, group_B)

Traditional Teaching Methods: Screenshots and Code

Static Code in RStudio (Better)

# Load necessary package
library(stats)

# Conduct a two-sample t-test
set.seed(123)
group_A <- rnorm(30, mean = 100, sd = 15)  # Sample data for group A
group_B <- rnorm(30, mean = 105, sd = 15)  # Sample data for group B

# Perform t-test
t.test(group_A, group_B)

    Welch Two Sample t-test

data:  group_A and group_B
t = -2.3755, df = 56.559, p-value = 0.02094
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -15.448139  -1.315124
sample estimates:
mean of x mean of y 
 99.29344 107.67508 

Traditional Teaching Methods: Screenshots and Code

Code and Output

• Code is usually shown as static screenshots or pre-written blocks.
• Students struggle to follow along without hands-on practice.

Traditional Teaching Methods: Live Coding

Introducing webR

What is webR?

• webR is a version of R that runs directly in a web browser.
• It eliminates the need for local installation or server setup.
• It just works (even on your phone).
• Can be easily implemented using quarto.

Why webR is a Game Changer

• webR transforms traditional lectures into dynamic and interactive sessions.
• It helps maintain student interest and improves comprehension through hands-on practice.
• Simplifies the teaching process, allowing instructors to focus on content delivery rather than technical setup.

Benefits of webR

• Students can run code directly in the browser, fostering interactive learning.
• No need for software installation, making it accessible to all students.
• Ideal for live coding sessions, demonstrations, and exploratory data analysis.

webR Demo 1

Linear Regression

We’ll first start with the webR team’s demo example, the statistician’s way of saying, “Hello world!”—linear regression:

webR Demo 1

Linear Regression

You can continue your code from the previous slide! Just make sure you remember what variables you used!

webR Demo 2

Summarize Data

Glancing at data frames:

webR Demo 3

Mixing Active and Non-Active R Code

For if-else statements:

if (...) {
  # Statements for TRUE
} else {
  # Statements for FALSE
}

webR Browser

What Can You Currently Do With webR?

Use Cases

• Download R package datasets to .csv, .xlsx, etc., on-the-fly: tidyverse.r-universe.dev/ggplot2
• Shiny apps without a server: github.com/georgestagg/shiny-standalone-webr-demo
• Create interactive, engaging teaching materials (that work even in lecture environments).

• Not all R packages are properly supported/implemented yet (e.g. plotly, leaflet).
• May take a few minutes to download all required packages for specific lecture highlights (varies by internet speed).
• Time to render revealjs documents are longer (more for older machines).

Summary

webR offers a powerful tool for teaching statistics and data visualization. By enabling interactive coding directly within presentations, it enhances student engagement and simplifies the instructional process.

References