r-help-reprexes

A lesson on getting unstuck in R, debugging, making reproducible examples (for biologists)

https://github.com/carpentries-incubator/r-help-reprexes

Science Score: 44.0%

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Keywords

carpentries-incubator debugging english example help lesson minimal pre-alpha r reprex reproducible
Last synced: 4 months ago · JSON representation ·

Repository

A lesson on getting unstuck in R, debugging, making reproducible examples (for biologists)

Basic Info
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 2
  • Open Issues: 75
  • Releases: 0
Topics
carpentries-incubator debugging english example help lesson minimal pre-alpha r reprex reproducible
Created over 1 year ago · Last pushed 4 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Governance

README.md

RRRRR, I'm stuck!

DOI Website License

This is a lesson for beginner R coders in (ecological) data science. This lesson will teach how to identify and resolve problematic code by using minimal reproducible examples to ask for help from the community. While this lesson should be appropriate for all levels, it is merely a supplement and not a replacement for an intro to R lesson. As such, this lesson assumes a basic understanding of data structures, manipulation, and visualization in R. Familiarity with the tidyverse package is helpful but not required. An example of an introductory lesson that covers all pre-requisites is The Carpentries' Data Analysis and Visualization in R for Ecologists.

The website for this lesson can be found at https://carpentries-incubator.github.io/R-help-reprexes/.

This lesson is a template lesson that uses The Carpentries Workbench. Click on the link to learn more.

Lesson Developers and Current Maintainers:

Kaija Gahm (Lead)
Xochitl Ortiz Ross
Peter Laurin

The team of current maintainers aims to meet twice monthly to continue developing this curriculum. Please contact the lead, Kaija, if you are interested in attending these meetings.

How to contribute

Feedback is always welcome! If you wish to contribute to this lesson please see the CONTRIBUTING.md document for contributing guidelines and details on how to get involved with this project. Disclaimer: this lesson is still in its pre-alpha phase, which means we are still in the process of developing this lesson and are limiting contributions. Once our material has been more fully drafted we will welcome more feedback and collaboration. Nevertheless, please reach out if you are interested.

You can also look through the current list of issues for ideas for contributing to this training curriculum. Not all issues will be open to contributions, but you can look for the tag good_first_issue. This indicates that the issue does not require in-depth knowledge of the project and lesson infrastructure, and is a good opportunity for a new contributor to get involved.

This lesson is a template lesson that uses The Carpentries Workbench. Click on the link to learn more about how the page was created and how it can be edited.

The Data

This lesson uses data from the ratdat package (learn more on the CRAN page: https://cran.r-project.org/web/packages/ratdat/index.html).

Acknowledgements

The following people aided the development of this curriculum, by providing training, suggestions, reviews, and inspiration.

Our trainers:

Our first demo/focus group:

Members of the R-Ladies and DSLC Slack groups: * Cath Blatter * Alison Lanski * Alice Walsh * June Choe * Tan Ho * Jon Harmon * Arham Choudhury * Olivier Leroy * Gus Lipkin * Jannik Buhr

Cite this content

See CITATION.cff for citation information, including a list of authors. (Read more about the Citation File Format and how to use it.)

Contact

Please get in touch with Kaija Gahm with any questions about this lesson.

Links related to the course development

Link to Lesson Development Doc

Owner

  • Name: carpentries-incubator
  • Login: carpentries-incubator
  • Kind: organization

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: RRRRR... I'm stuck!
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Kaija
    family-names: Gahm
    email: kaija.gahm@gmail.com
    affiliation: UCLA
    orcid: 'https://orcid.org/0000-0002-4612-4426'
  - given-names: Xochitl
    family-names: Ortiz-Ross
    affiliation: UCLA
    email: xortizross@g.ucla.edu
    orcid: 'https://orcid.org/0000-0002-2598-8268'
  - given-names: Peter
    family-names: Laurin
    orcid: 'https://orcid.org/0000-0003-0294-514X'
    affiliation: UCLA
    email: laurinpeter0@gmail.com
identifiers:
  - type: doi
    value: 10.17605/OSF.IO/37JPQ
    description: OSF DOI (always resolves to latest version)
repository-code: 'https://github.com/kaijagahm/R-help-reprexes'
url: 'https://kaijagahm.github.io/R-help-reprexes/'
abstract: >
This is a lesson for beginner R coders in (ecological) data science. 
This lesson will teach how to identify and resolve problematic code by using minimal reproducible examples to ask for help from the community. 
While this lesson should be appropriate for all levels, it is merely a supplement and not a replacement for an intro to R lesson. 
As such, this lesson assumes a basic understanding of data structures, manipulation, and visualization in R. 
Familiarity with the tidyverse package is helpful but not required. 
An example of an introductory lesson that covers all pre-requisites is The Carpentries' Data Analysis and Visualization in R for Ecologists.
The website for this lesson can be found at https://kaijagahm.github.io/R-help-reprexes/.
This lesson is a template lesson that uses The Carpentries Workbench. Click on the link to learn more.

keywords:
  - reprex
  - R
  - debugging
  - error
  - coding
  - RStudio
  - reproducible example
  - MRE
  - minimal reproducible example
  - minimal
  - reproducible
  - example
  - question
license: CC-BY-4.0

GitHub Events

Total
  • Issues event: 56
  • Watch event: 3
  • Delete event: 85
  • Issue comment event: 82
  • Push event: 270
  • Pull request review comment event: 52
  • Pull request event: 105
  • Pull request review event: 71
  • Fork event: 1
  • Create event: 89
Last Year
  • Issues event: 56
  • Watch event: 3
  • Delete event: 85
  • Issue comment event: 82
  • Push event: 270
  • Pull request review comment event: 52
  • Pull request event: 105
  • Pull request review event: 71
  • Fork event: 1
  • Create event: 89

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 56
  • Total pull requests: 77
  • Average time to close issues: 3 months
  • Average time to close pull requests: 9 days
  • Total issue authors: 3
  • Total pull request authors: 5
  • Average comments per issue: 0.27
  • Average comments per pull request: 0.97
  • Merged pull requests: 46
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 42
  • Pull requests: 62
  • Average time to close issues: 2 months
  • Average time to close pull requests: 11 days
  • Issue authors: 2
  • Pull request authors: 4
  • Average comments per issue: 0.07
  • Average comments per pull request: 0.9
  • Merged pull requests: 34
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • kaijagahm (53)
  • xortizross (3)
  • peterlaurin (1)
Pull Request Authors
  • kaijagahm (36)
  • xortizross (36)
  • peterlaurin (14)
  • carpentries-bot (9)
  • tobyhodges (2)
Top Labels
Issue Labels
Ep2: First Aid/Problem (14) Ep3: Minimal Code (11) episode:why reprex (6) episode:datasets (5) episode:reproducible code (4) low priority (4) Ep1: Why Reprex (4) help wanted (3) type:discussion (3) type:narrative (2) type:bug (2) episode:understanding code (2) type:enhancement (2) good first issue (2) type:formatting (2) type:clarification (1) episode:asking questions (1) type:lesson design (1) type:workflow (1) type:documentation (1) high priority (1) Ep5: Asking Q (1) Ep4: Minimal Data (1) wontfix (1) enhancement (1) documentation (1) workflow (1) type:typo (1)
Pull Request Labels
type: package cache (6) episode:datasets (5) type: template and tools (5) type:documentation (2) help wanted (2) status:next agenda (1) type:narrative (1) episode: datasets (1) episode:understanding code (1) episode:reproducible code (1) episode:asking questions (1) type:lesson design (1)