Below you find various software/visualization projects that I developed for research or teaching purposes. I am fascinated by that possibilites that new technologies offer and I mainly work with R, Shiny and Plotly. Please contact me if you have any feedback or questions.

  • Software
    • deeplr: This R-package provides functionality for quick translations using the DeepL Translator from within R (install the package from Github). You will also find examples on Github. DeepL now has an official API and you need to apply for access, i.e. to get an API key
    • citationsr: The R package citationsr comprises functions that can be used to extract and analyze citation cases. When study A cites study B, it contains text fragments that refer to study B. We call study A a citing document and the text fragments it contains citation cases. Go to the github repo for an overview.
    • Visualizing Causal Scenarios [interactively]: A shiny app under development to visualize causal scenarios. You can find the corresponding paper here. The graph shown above is related to that project and the code shall be collected in an R package.
  • Visualization
    • Styling plotly layout & margins: A shiny app to illustrate different layout settings for Plotly R.
    • Styling plotly markers: A shiny app to illustrate different marker settings for Plotly R.
    • Guessing empirical distributions: Let your students guess what distributions of various variables look like and discuss them subsequently. You can find the app here and the code on github.
    • Measurement error (Bull’s eye): Illustration of systematic and random measurement error. Example is a single person that repeatedly measures his/her weight on a scale. Students can change the number of measurements (observations), i.e. how often the person measures his/her weight, as well as the random error and the systematic error underlying these repeated measurements. You can find the app here and the code on github.
    • Visualizing functions: Plot functions. User can choose a certain function, decide about the range for which the function should be plotted and the range of x- and y-values for which the plot is displayed. You can find the app here and the code on github.
    • Transformations of variables/data: A simple app to illustrate what happens to the the distribution of a variable when it is transformed. You can find the app here and the code on github.
    • Joint distributions (discrete variables): Again the idea is that students become familiar with the idea of joint distributions, i.e. develop a “distributional perspective” of data. You can find the app here and the code on github.
    • Systematic measurement error in subgroups: Illustration of how distributions of variables change as a consequence of measurement error. It also illustrates how distributions change if there are different systematic measurement errors across subgroups that operate simultaneously. You can find the app here and the code on github.