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. Let me show you how to do this by taking a quick look at country-level CO2 emissions.
Closing down for the year, I finally wrapped up a new ‘ExPanDaR’ version that now allows exploring all sorts of data interactively and generates notebooks containing the analysis on the fly. So here comes my little Christmas present for the wonderful RStats community!
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 the 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.
When starting a course you want to learn something about the background of your students. What do you do when the course is too large for an introduction round? Run a shiny survey!
The new version of the ‘ExPanDaR’ package will include the option to export your explorative data analysis to an R Notebook. This Notebook can then be used to extend your analysis. Let me show you how to do this.
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.