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https://github.com/strengejacke/sjmisc
Data transformation and utility functions for R
https://github.com/strengejacke/sjmisc
data-transformation data-wrangling labelled-data r recoding
Last synced: about 22 hours ago
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Data transformation and utility functions for R
- Host: GitHub
- URL: https://github.com/strengejacke/sjmisc
- Owner: strengejacke
- License: gpl-3.0
- Created: 2015-03-21T06:31:54.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2024-05-13T13:20:38.000Z (8 months ago)
- Last Synced: 2025-01-03T18:09:02.196Z (8 days ago)
- Topics: data-transformation, data-wrangling, labelled-data, r, recoding
- Language: R
- Homepage: https://strengejacke.github.io/sjmisc
- Size: 6.83 MB
- Stars: 159
- Watchers: 13
- Forks: 24
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - strengejacke/sjmisc - Data transformation and utility functions for R (R)
README
# sjmisc - Data and Variable Transformation Functions 
[](https://cran.r-project.org/package=sjmisc) [](https://doi.org/10.21105/joss.00754) [](https://strengejacke.github.io/sjmisc/) [](https://cranlogs.r-pkg.org:443/) [](https://cranlogs.r-pkg.org:443/)
Data preparation is a common task in research, which usually takes the most amount of time in the analytical process. Packages for data preparation have been released recently as part of the _tidyverse_, focussing on the transformation of data sets. Packages with special focus on transformation of _variables_, which fit into the workflow and design-philosophy of the tidyverse, are missing.
**sjmisc** tries to fill this gap. Basically, this package complements the **dplyr** package in that **sjmisc** takes over data transformation tasks on variables, like recoding, dichotomizing or grouping variables, setting and replacing missing values, etc. A distinctive feature of **sjmisc** is the support for labelled data, which is especially useful for users who often work with data sets from other statistical software packages like _SPSS_ or _Stata_.
The functions of **sjmisc** are designed to work together seamlessly with other packages from the tidyverse, like **dplyr**. For instance, you can use the functions from **sjmisc** both within a pipe-workflow to manipulate data frames, or to create new variables with `mutate()`. See `vignette("design_philosophy", "sjmisc")` for more details.
## Contributing to the package
Please follow [this guide](https://github.com/strengejacke/sjmisc/blob/master/.github/CONTRIBUTING.md) if you like to contribute to this package.
## Installation
### Latest development build
To install the latest development snapshot (see latest changes below), type following commands into the R console:
```r
library(devtools)
devtools::install_github("strengejacke/sjmisc")
```
### Officiale, stable release
To install the latest stable release from CRAN, type following command into the R console:
```r
install.packages("sjmisc")
```
## References, documentation and examples
Please visit [https://strengejacke.github.io/sjmisc/](https://strengejacke.github.io/sjmisc/) for documentation and vignettes.
## Citation
In case you want / have to cite my package, please cite as (see also `citation('sjmisc')`):
Lüdecke D (2018). sjmisc: Data and Variable Transformation Functions. _Journal of Open
Source Software_, *3*(26), 754. doi: 10.21105/joss.00754
[](https://doi.org/10.21105/joss.00754)