Data wrangling within the #tidyverse – the design philosophy behind the sjmisc-package #rstats

I’m pleased to announce sjmisc 2.3.0, which was just updated on CRAN. The update might break existing code – however, functions were largely revised to work seamlessly within the tidyverse. In the long run, consistent design makes working with sjmisc more intuitive.

Basically, sjmisc covers two domains of functionality:

  • Reading and writing data between R and other statistical software packages like SPSS, SAS or Stata and working with labelled data; this includes easy ways to get and set label attributes, to convert labelled vectors into factors (and vice versa), or to deal with multiple declared missing values etc.
  • Data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data.

This posting briefly describes some of the changes to the function design that do data transformation tasks.

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