I have decided that the world needs another Covid-19 related R package. Not sure whether you agree, but the new package facilitates the direct download of various Covid-19 related data (including data on governmental measures) directly from authoritative sources. It also provides a flexible function and accompanying shiny app to visualize the spreading of the virus. Play around with the shiny app here if you like or hang around to learn more about the package.

Update 2020-03-30: I have decided that the world needs another Covid19 related R package. Not sure whether you agree, but the new package facilitates the direct download of various Covid-19 related data (including data on governmental measures) directly from the authoritative sources. A short blog post about it can be found here. It also provides a flexible function and accompanying shiny app to visualize the spreading of the virus. Play around with the shiny app here if you like.

Update 2020-03-30: I have decided that the world needs another Covid-19 related R package. Not sure whether you agree, but the new package facilitates the direct download of various Covid-19 related data (including data on governmental measures) directly from the authoritative sources. A short blog post about it can be found here. It also provides a flexible function and accompanying shiny app to visualize the spreading of the virus. Play around with the shiny app here if you like.

Update 2020-03-30: I have decided that the world needs another Covid19 related R package. Not sure whether you agree, but the new package facilitates the direct download of various Covid-19 related data (including data on governmental measures) directly from the authoritative sources. A short blog post about it can be found here. It also provides a flexible function and accompanying shiny app to visualize the spreading of the virus. Play around with the shiny app here if you like.

Assume that you have some new data that you want to explore. The new CRAN version of the ‘ExPanDaR’ package helps by providing a (customized) R notebook containing all building blocks of an exploratory data analysis with a few clicks. Install the Package and Start ExPanD First, you need to install the package. I recommend installing the Github development version of the package as it fixes some small bugs that I discovered directly after submitting to CRAN (sigh…).

The ‘ExPanDaR’ package offers a toolbox for interactive exploratory data analysis (EDA). Closing down for the year, I finally wrapped up a new version that now allows exploring all sorts of data interactively and generates notebooks containing the analysis all more or less with one line of code and a click. So here comes my little Christmas present for the wonderful RStats community! As CRAN is taking its well-deserved Christmas break, you will need to install the new version from Github.

Online appendices detailing the robustness of empirical analyses are paramount but they never let readers explore all reasonable researcher degrees of freedom. Simonsohn, Simmons and Nelson suggest a ‘specification curve’ that allows readers to eyeball how a main coefficient of interest varies across a wide arrange of specifications. I build on this idea by making it interactive: A shiny-based web app enables readers to explore the robustness of findings in detail along the whole curve.

Last week, we started a new course titled “Statistical Programming and Open Science Methods”. It is being offered under the research program of TRR 266 “Accounting for Transparency” and enables students to conduct data-based research so that others can contribute and collaborate. This involves making research data and methods FAIR (findable, accessible, interoperable and reusable) and results reproducible. All the materials of the course are available on GitHub together with some notes in the README on how to use them for self-guided learning.

The ‘ExPanDaR’ package offers a toolbox for interactive exploratory data analysis (EDA). You can read more about it here. The ‘ExPanD’ shiny app allows you to customize your analysis to some extent but often you might want to continue and extend your analysis with additional models and visualizations that are not part of the ‘ExPanDaR’ package. Thus, I am currently developing an option to export the ‘ExPanD’ data and analysis to an R Notebook.

Following up on a recent blog article that discussed how to use R to explore your researcher degrees of freedom, this post introduces a specification curve plot as suggested in Simonsohn, Simmons and Nelson. With this plot, you can eyeball how various researcher degrees of freedom affect your main outcome of interest. In a recent post, I introduced my in-development R package ‘rdfanaylsis’ that provides a coding environment allowing researchers to specify their researcher degrees of freedom ex ante and to systematically explore their effects on their findings ex post.