jbkunst/shiny-apps-educational

Shiny apps for educational purposes.

Shiny apps for educational purposes

• FOA: ♫ Tell me Y ♪
• Using apps locally
• Shiny apps
  • ARMA Process
  • Bias Variance
  • Decision Tree
  • Kmeans
  • Kmeans Images
  • Logistic Regression
  • Matrix Decompositions

FOA: ♫ Tell me Y ♪

The ideas/purposes behind every shiny app in this repository are:

• Being a complement for a ML, Statistics, Probability, R class. The application mainly shows and exemplifies a result that needs to be explained. By themselves, the apps are just an app.
• Point out other resources and links that are more formal and/or useful.
• TBD.

Using apps locally

There is a script run_app.R which contanins a helper function to download the repo in a temporal folder and then you can run the apps

To load that function:

source("https://raw.githubusercontent.com/jbkunst/shiny-apps-edu/master/run_app.R")

Then you can use it giving the folder name, for example:

run_app("kmeans")

Shiny apps

ARMA Process

Source code here. See the live version in shinyapps.io. Go to index.

Bias Variance

Source code here. See the live version in shinyapps.io. Go to index.

Decision Tree

Source code here. See the live version in shinyapps.io. Go to index.

Kmeans

Source code here. See the live version in shinyapps.io. Go to index.

Kmeans Images

Source code here. See the live version in shinyapps.io. Go to index.

Logistic Regression

Source code here. See the live version in shinyapps.io. Go to index.

Matrix Decompositions

Source code here. See the live version in shinyapps.io. Go to index.

shiny-apps-educational's Issues

Create DESCRIPTION files to specify packages

Bag of words!

https://twitter.com/jdwlbr/status/1622707312862568448

Ideas

• AUC ROC inspired by:
  • https://kennis-research.shinyapps.io/ROC-Curves/
  • http://www.bioestadistica.uma.es/analisis/roc1/
  • http://mfviz.com/binary-predictions/
  • http://www.navan.name/roc/
• Probability/Venn diagram
• Simpsons paradox
• Linear regression:
  • https://easystats.github.io/performance/
  • https://easystats.github.io/performance/#comprehensive-visualization-of-model-checks.
  • https://setosa.io/ev/ordinary-least-squares-regression/

check http://www.rossmanchance.com/applets/index.html?s=03