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.

The functions of sjmisc are designed to work together seamlessly with other packes from the tidyverse, like dplyr. For instance, you can use the functions from sjmisc both within a pipe-worklflow to manipulate data frames, or to create new variables with mutate(). See vignette("design_philosophy", "sjmisc") for more details.

To install the latest development snapshot (see latest changes below), type following commands into the R console:

library(devtools)
devtools::install_github("strengejacke/sjmisc")

Please note the package dependencies when installing from GitHub. The GitHub version of this package may depend on latest GitHub versions of my other packages, so you may need to install those first, if you encounter any problems. Here's the order for installing packages from GitHub:

sjlabelled → sjmisc → sjstats → ggeffects → sjPlot

To install the latest stable release from CRAN, type following command into the R console:

install.packages("sjmisc")

A cheatsheet can be downloaded from here (PDF) or from the RStudio cheatsheet collection.

For more examples, see package vignettes (browseVignettes("sjmisc")).

In case you want / have to cite my package, please use citation('sjmisc') for citation information.