Yet another operator to simplify data preparation with memisc


The recently published version of the memisc package also contains an %$$% operator that simplifies routine data preparation steps that hitherto would involve calls to the function within(). It is analogous to the operator %$%, which is provided by the “magrittr” package, but is also defined by this package.

These operators are illustrated by the following code examples.

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An R equivalent to Stata’s ‘replace if’ in memisc


Version of package “memisc” was recently (3rd March 2023) published on CRAN. One of the new features of this version is the %if% operator which allows to assign values to subsets of observations. To see how it works, consider the following example:

I implemented this feature on suggestion from a colleague who missed such a feature for data preparation.

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RKernel: Yet another R kernel for Jupyter


The package ‘RKernel’ allows you to run R in Jupyter notebooks etc. I have been working on the package on and off since summer 2021. I started because I was dissatisfied with the default rich output that the already existing IRkernel produces. Also, the help system seemed to be broken with version <= 1.3 of ‘IRkernel’ when used with a more recent version of R (>=4.3?).1

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New Book: Data Management in R


My new book “Data Management in R: A Guide for Social Scientists” is now in print and is announced by SAGE to appear on 26 December 2020, right on time for Boxing Day!

Supporting material can be found on the page “Data Management in R: A Guide for Social Scientists”.

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A “normalizing” re-alignment of voting behaviour in East Germany?


I am currently working on a book chapter about “Sozialstruktur und Wahlverhalten in Ost- und Westdeutschland – Konvergenz, Divergenz oder Persistenz?” (Social Structure and Voting Behaviour in East and West Germany – Convergence, Divergence, or Persistence?). In this context I found the remarkable result shown in the following figure.

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Now available as open access article: “Multilevel Analysis with Few Clusters”


Our paper on “Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods” is now published as an open access article by the British Journal of Political Science (Elff, Heisig, Schaeffer, and Shikano 2021), along with a comment by Daniel Stegmueller (Stegmueller 2020) and a response by us (Elff, Heisig, Schaeffer, and Shikano 2021).

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