sjmisc: Data and Variable Transformation Functions.

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Authors Daniel Lüdecke
Journal/Conference Name ANNALS OF STATISTICS
Paper Category
Paper Abstract Data preparation is a common task in research, which usually takes the most amountof time in the analytical process. There are typically two types of data transformation:arranging and reshaping data sets (like filtering observations or selecting variables, com-bining data sets etc.) and recoding and converting variables. Statistical software packagesshould provide convenient tools to fulfil these tasks.For theR Project for Statistical Computing, packages have been released recently that areknown to be part of thetidyverse. Some of those packages focus on the transformation ofdata sets. Packages with special focus on transformation ofvariables, which fit into theworkflow and design-philosophy of the tidyverse, are missing.sjmiscis a package for the statistical progamming languageR, which tries to fill this gap.Basically, this package complements thedplyrpackage (Wickham et al. 2017) in thatsjmisctakes over data transformation tasks on variables, like recoding, dichotomizing orgrouping variables, setting and replacing missing values, etc.The data transformation functions in this package all supportlabelled data(or labelledvectors), which is a common data structure in other statistical environments to storemeta-information about variables, like variable names, value labels or multiple definedmissing values. Working with labelled data is featured by packages likehaven(Wickhamand Miller 2018) orsjlabelled(Lüdecke 2018a).
Date of publication 2016
Code Programming Language R

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