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recodeflow

Harmonizing data into a common format.

https://github.com/big-life-lab/recodeflow

Science Score: 36.0%

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
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 8 committers (12.5%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Harmonizing data into a common format.

Basic Info
Statistics
  • Stars: 6
  • Watchers: 4
  • Forks: 1
  • Open Issues: 19
  • Releases: 0
Created about 6 years ago · Last pushed 11 months ago
Metadata Files
Readme Changelog Contributing License

README.md

recodeflow

Lifecycle: development R-CMD-check

Introduction

What is recodeflow?

recodeflow recodes variables from multiple data sets into harmonized variables.

recodeflow has basic functions and templates required to define, recode, and harmonize variables for any dataset.

Why should I use recodeflow?

Recoding and cleaning your data is typically the most time consuming step of your project. Existing functions such as sjmisc::rec() and dplyr:recode() work well but they are limited to recoding one variable at a time.

The recodeflow package takes data cleaning and recoding one step further. recodeflow allows you to recode multiple variables at the same time, and harmonize variables across similar databases even when the variables and variables' categories change.

recodeflow also helps to reduce errors, document the recode process, and ensures your new variables have labels and other metadata.

Even if your project has few variables,recodeflow can save you time.

How does recodeflow work?

Use the worksheets variables and variable_details to list your variables and state how to recode the each variable.

Once your variables are defined, use recodeflow functions to clean and recode your data. The main recodeflow function is rec_with_table which recodes variables within you dataset(s) based on how you've defined the variable in the worksheets variables and variable_details.

What's included in recodeflow?

The recodeflow package includes:

  • functions required to clean and recode variables.

  • worksheets:

    • variables a list of variables to recode and
    • variable_details mapping of variables across datasets and a list of instructions for recoding variables.

We've also created the following documentation to help you understand recodeflow:

  • how to guides examples of how to use recodeflow and adapt recodeflow for your dataset,
  • articles that describe package elements (e.g., variables) in detail,
  • references that describe all recodeflow functions, and
  • example data to demonstrate recodeflow functions and templates.

Where is recodeflow used?

Currently recodeflow is used in packages that harmonize health surveys and health administrative databases.

  • cchsflow is a package that harmonizes variables across cycles of the Canadian Community Health Survey (CCHS). cchsflow is published.

  • raiflow is a package that will harmonize variables within the Resident Assessments Instruments (RAI) from various sources: Canada's Continuing Care Reporting System (CCRS) and Ontario's Resident Assessment Instrutment for Home Care (RAI-HC). raiflow is currently underdevelopment.

Requirements

Roadmap

Projects on the roadmap are at the Github repository recodeflow under the projects tab.

Contributing

Please follow the recodeflow contribution guide if you would like to contribute to the recodeflow package.

Owner

  • Name: Big Life Lab
  • Login: Big-Life-Lab
  • Kind: organization
  • Email: dmanuel@ohri.ca
  • Location: Ottawa

https://projectbiglife.ca

GitHub Events

Total
  • Issues event: 5
  • Delete event: 26
  • Member event: 2
  • Issue comment event: 43
  • Push event: 133
  • Pull request review event: 99
  • Pull request review comment event: 80
  • Pull request event: 36
  • Create event: 14
Last Year
  • Issues event: 5
  • Delete event: 26
  • Member event: 2
  • Issue comment event: 43
  • Push event: 133
  • Pull request review event: 99
  • Pull request review comment event: 80
  • Pull request event: 36
  • Create event: 14

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 237
  • Total Committers: 8
  • Avg Commits per committer: 29.625
  • Development Distribution Score (DDS): 0.65
Past Year
  • Commits: 6
  • Committers: 2
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.333
Top Committers
Name Email Commits
lurn l****x@g****m 83
Yulric Sequeira y****s@g****m 51
Rhiannon Roberts r****s@m****a 44
Rostyslav r****e@g****m 39
Carol Bennett c****2@g****m 10
Warsame Yusuf w****8@U****a 5
Doug Manuel d****l@g****m 3
Doug Manuel D****l 2
Committer Domains (Top 20 + Academic)
uottawa.ca: 1 mail.mcgill.ca: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 20
  • Total pull requests: 77
  • Average time to close issues: about 1 year
  • Average time to close pull requests: 3 months
  • Total issue authors: 7
  • Total pull request authors: 7
  • Average comments per issue: 1.8
  • Average comments per pull request: 1.71
  • Merged pull requests: 53
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 44
  • Average time to close issues: 3 months
  • Average time to close pull requests: 27 days
  • Issue authors: 3
  • Pull request authors: 3
  • Average comments per issue: 1.33
  • Average comments per pull request: 1.64
  • Merged pull requests: 28
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • DougManuel (5)
  • reikookamoto (4)
  • zargot (3)
  • yulric (3)
  • JuanLiOHRI (3)
  • barracuda156 (1)
  • Rhan43 (1)
Pull Request Authors
  • yulric (39)
  • zargot (13)
  • reikookamoto (11)
  • DougManuel (9)
  • LurN (4)
  • rvyuha (2)
  • CBjerke (1)
Top Labels
Issue Labels
enhancement (3) bug (1)
Pull Request Labels
enhancement (2)

Packages

  • Total packages: 1
  • Total downloads:
    • cran 257 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: recodeflow

Interface Functions for PMML Creation, and Data Recoding

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 257 Last month
Rankings
Stargazers count: 24.2%
Forks count: 28.8%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 39.9%
Downloads: 81.2%
Maintainers (1)
rvyuha@toh.ca
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.1.0 depends
  • XML >= 3.98 imports
  • dplyr * imports
  • haven * imports
  • magrittr * imports
  • sjlabelled * imports
  • stringr * imports
  • tidyr * imports
  • survival * suggests
  • testthat >= 2.1.0 suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/test-coverage.yaml actions
  • actions/checkout v3 composite
  • actions/upload-artifact v3 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite