List Things to Do

Manage a 'GitHub' problem using R: wrangle issues, labels and milestones. It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R.

{IssueTrackeR}

{IssueTrackeR} is an R package designed to retrieve and manage GitHub issues directly within R. This package allows users to efficiently track and handle issues from their GitHub repositories.

This package relies a lot on the package {gh} to use the GitHub API and retrieve data from GitHub.

Installation

You can install {IssueTrackeR} from CRAN:

install.packages("IssueTrackeR")

Development

You can install the development version of {IssueTrackeR} from GitHub:

# install.packages("pak")
pak::pak("TanguyBarthelemy/IssueTrackeR")

Features

• Retrieve Issues: Fetch issues from any (with sufficient rights) GitHub repository.
• Issue Management: Class S3 to manage the issues as a datasets within R.
• Filtering: Filter issues by labels, content (title, body and comments) and milestones.

Usage

library("IssueTrackeR")
#> Currently, the default options are:
#> - location for datasets is /tmp/RtmpAaWI8a/data
#> - owner: rjdverse
#> - repo: rjdemetra
#> 
#> Attaching package: 'IssueTrackeR'
#> The following objects are masked from 'package:base':
#> 
#>     append, sample

Retrieve information from GitHub

To get information from a repository, you can call the functions get_issues, get_labels and get_milestones

# From online
my_issues <- get_issues(
    source = "online",
    owner = "jdemetra",
    repo = "jdplus-main",
    verbose = FALSE
)
my_labels <- get_labels(
    source = "online",
    owner = "jdemetra",
    repo = "jdplus-main"
)
#> Repo: jdplus-main  owner: jdemetra 
#> Reading labels... Done!
#> 12 labels found.
my_milestones <- get_milestones(
    source = "online",
    owner = "jdemetra",
    repo = "jdplus-main"
)
#> Repo: jdplus-main  owner: jdemetra 
#> Reading milestones... 
#>  -  backlog ... Done!
#>  -  3.8.0 ... Done!
#> Done! 2 milestones found.

Save issues in local

You can also write the datasets in local with write_to_dataset():

write_to_dataset(
    x = my_issues,
    dataset_dir = tempdir()
)
#> The datasets will be exported to /tmp/RtmpAaWI8a/list_issues.yaml.

write_to_dataset(
    x = my_labels,
    dataset_dir = tempdir()
)
#> The datasets will be exported to /tmp/RtmpAaWI8a/list_labels.yaml.

write_to_dataset(
    x = my_milestones,
    dataset_dir = tempdir()
)
#> The datasets will be exported to /tmp/RtmpAaWI8a/list_milestones.yaml.

Options

It is also possible to set option for a R session:

# The directory containing the yaml files in local
options(IssueTrackeR.dataset.dir = tempdir())
# The default GitHub owner
options(IssueTrackeR.owner = "jdemetra")
# the default GitHub repository
options(IssueTrackeR.repo = "jdplus-main")

Retrieve issues from local

Then it’s possible to read Issues from local yaml files:

# From local
my_issues <- get_issues(source = "local")
my_labels <- get_labels(source = "local")
my_milestones <- get_milestones(source = "local")

Update full database

You can update your full database of issues, labels and milestones with update_database():

# From online
update_database(verbose = FALSE)

Contributing

Contributions are welcome! Please feel free to submit a pull request or report any issues.

License

This project is licensed under the MIT License. See the LICENSE file for details.