sjmisc-package: Data and Variable Transformation Functions

Description Author(s)


Purpose of this package

Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.

Design philosophy - consistent api

The design of this package follows, where appropriate, the tidyverse-approach, with the first argument of a function always being the data (either a data frame or vector), followed by variable names that should be processed by the function. If no variables are specified as argument, the function applies to the complete data that was indicated as first function argument.

There are two types of function designs:

transformation/recoding functions

Functions like rec() or dicho(), which transform or recode variables, typically return the complete data frame that was given as first argument, additionally including the transformed and recoded variables specified in the ...-ellipses argument. The variables usually get a suffix, so original variables are preserved in the data.

coercing/converting functions

Functions like to_factor() or to_label(), which convert variables into other types or add additional information like variable or value labels as attribute, also typically return the complete data frame that was given as first argument. However, the variables specified in the ...-ellipses argument are converted ("overwritten"), all other variables remain unchanged. Hence, these functions do not return any new, additional variables.


Daniel Lüdecke [email protected]

strengejacke/sjmisc documentation built on June 18, 2018, 3 a.m.