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sjmisc

sjmisc: Data and Variable Transformation Functions - Published in JOSS (2018)

https://github.com/strengejacke/sjmisc

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 8 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

data-transformation data-wrangling labelled-data r recoding

Keywords from Contributors

prediction correlation standardization
Last synced: 6 months ago · JSON representation

Repository

Data transformation and utility functions for R

Basic Info
Statistics
  • Stars: 161
  • Watchers: 11
  • Forks: 24
  • Open Issues: 23
  • Releases: 10
Topics
data-transformation data-wrangling labelled-data r recoding
Created almost 11 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct

README.md

sjmisc - Data and Variable Transformation Functions

CRAN_Status_Badge    DOI    Documentation    downloads    total

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 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/ 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

DOI

Owner

  • Name: Daniel
  • Login: strengejacke
  • Kind: user
  • Location: Hamburg, Germany
  • Company: Universitätsklinikum Hamburg-Eppendorf

Senior researcher; systems theory, qualitative and quantitative research

JOSS Publication

sjmisc: Data and Variable Transformation Functions
Published
June 20, 2018
DOI
Volume 3, Issue 26, Page 754
Authors
Daniel Lüdecke ORCID
University Clinical Center Hamburg-Eppendorf
Editor
Thomas J. Leeper ORCID
Tags
data transformation data wrangling variable recoding
View PDF Review Thread Software Archive

GitHub Events

Total
  • Issues event: 1
  • Watch event: 4
Last Year
  • Issues event: 1
  • Watch event: 4

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 929
  • Total Committers: 13
  • Avg Commits per committer: 71.462
  • Development Distribution Score (DDS): 0.527
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Daniel Lüdecke d****e@u****e 439
Daniel m****l@d****e 417
iago-pssjd 4****d 44
iagogv a****a@g****m 13
iago-pssjd i****e@p****g 4
Alexander Bartel g****b@n****g 3
iago-pssjd i****e@s****s 2
Darío Hereñú m****a@g****m 2
larmarange j****h@l****t 1
iagogv 4****v 1
Thomas J. Leeper t****r@g****m 1
Christian Luz 3****z 1
Arfon Smith a****n 1
Committer Domains (Top 20 + Academic)
larmarange.net: 1 sjd.es: 1 ndevln.org: 1 pssjd.org: 1 danielluedecke.de: 1 uke.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 84
  • Total pull requests: 18
  • Average time to close issues: 16 days
  • Average time to close pull requests: about 2 months
  • Total issue authors: 45
  • Total pull request authors: 6
  • Average comments per issue: 1.89
  • Average comments per pull request: 2.06
  • Merged pull requests: 14
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • frankcsliu (7)
  • cschwem2er (6)
  • andresimi (6)
  • caayala (5)
  • iago-pssjd (5)
  • leeper (3)
  • benjaminwnelson (3)
  • phildias (3)
  • strengejacke (3)
  • b1azk0 (3)
  • dannyparsons (2)
  • sjPlot (2)
  • martinamorris (2)
  • konstruktur (2)
  • mwh3780 (2)
Pull Request Authors
  • iago-pssjd (11)
  • kant (2)
  • ndevln (2)
  • arfon (1)
  • leeper (1)
  • ceefluz (1)
Top Labels
Issue Labels
bug (2) enhancement (2) docs (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • cran 32,973 last-month
  • Total docker downloads: 46,123
  • Total dependent packages: 29
    (may contain duplicates)
  • Total dependent repositories: 45
    (may contain duplicates)
  • Total versions: 61
  • Total maintainers: 1
cran.r-project.org: sjmisc

Data and Variable Transformation Functions

  • Versions: 46
  • Dependent Packages: 24
  • Dependent Repositories: 43
  • Downloads: 32,973 Last month
  • Docker Downloads: 46,123
Rankings
Downloads: 2.4%
Stargazers count: 2.7%
Dependent packages count: 3.0%
Forks count: 3.2%
Dependent repos count: 3.9%
Average: 5.9%
Docker downloads count: 20.2%
Maintainers (1)
d.luedecke@uke.de
Last synced: 6 months ago
conda-forge.org: r-sjmisc
  • Versions: 15
  • Dependent Packages: 5
  • Dependent Repositories: 2
Rankings
Dependent packages count: 10.4%
Dependent repos count: 20.2%
Average: 23.4%
Stargazers count: 28.9%
Forks count: 34.1%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4 depends
  • dplyr * imports
  • insight * imports
  • magrittr * imports
  • methods * imports
  • purrr * imports
  • rlang * imports
  • sjlabelled >= 1.1.1 imports
  • stats * imports
  • tidyselect * imports
  • utils * imports
  • ggplot2 * suggests
  • graphics * suggests
  • haven >= 2.0.0 suggests
  • knitr * suggests
  • mice * suggests
  • nnet * suggests
  • rmarkdown * suggests
  • sjPlot * suggests
  • sjstats * suggests
  • stringdist * suggests
  • testthat * suggests
  • tidyr * suggests