Shiny Apps Gallery

For educational purposes, the source code is shown with the apps.

More apps and presentations have been developed by students in the QHELP seminars.

Linear Regression:A learnr tutorial.

LexOPS finds characteristics of words from large corpora to be used in lexical studies.

Validating BLIM-simulated Data: This app does a series of BLIM simulations and shows the dependence of the Discrepancy Index from the noise. The app was inspired by a student app from the 2019 seminar.

Conditional Testing

Properties of Relations: This app allows you to define an arbitrary binary relation on a set of five items and shows you whether certain properties are fulfilled.

Modeling PISA Data by Clusters: An illustration of statistical learning and visualisation techniques using real-world data (PISA).

Distributions: An illustration of the effect of violations of assumptions for the F distribution.

QQplot: This Shiny app is meant to let you play around with a few different distributions (Normal, Skew Normal, Cauchy, Skew Cauchy), and check what the effect of different variables (scale, location and shape) on their qq-plot is.

Modeling the 2018 PISA Dataset based on PCA and Clustering: A series of interactive tutorials introducing principle component analysis, clustering, linear modelling and cross-validation for large datasets.

Learning Paths: A revision and extension of an app from the TquanT project.

Reliability: How would your house look like if the carpenter building the windows is not fully reliable?

n-Neighbourhood: Illustrating neighbourhoods in knowledge structures (inspired by the Fringe app from the TquanT project).

Tutorial on Knowledge Space Theory: A learnr tutorial including several R Shiny Apps developed within the QHELP and TquanT. projects.

Similarity Assessment: An app for automatic grading of assignments.

R & Shiny: An R refresher and a Shiny introduction, together with exercises.