README

{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

# install.packages("remotes")
remotes::install_github("TanguyBarthelemy/IssueTrackeR")

Features

Usage

library("IssueTrackeR")
#> 
#> Attaching package: 'IssueTrackeR'
#> The following object is masked from 'package:base':
#> 
#>     append

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)
#> Running gh query ■■■■■■■■■■■■■■■■ 50% | ETA: 1sRunning gh query
#> ■■■■■■■■■■■■■■■■■■■■■■■ 75% | ETA: 1s
my_labels <- get_labels(source = "online", owner = "jdemetra", repo = "jdplus-main")
#> Reading labels... Done!
#> 12 labels found.
my_milestones <- get_milestones(source = "online", owner = "jdemetra", repo = "jdplus-main")
#> Reading milestones... 
#>  -  backlog ... Done!
#>  -  3.2.2 ... Done!
#>  -  3.2.3 ... Done!
#>  -  3.4.0 ... Done!
#>  -  3.5.0 ... Done!
#>  -  3.6.0 ... Done!
#> Done! 6 milestones found.

Save issues in local

You can also write the datasets in local with write_issues_to_dataset(), write_labels_to_dataset() and write_milestones_to_dataset():

write_issues_to_dataset(
    issues = my_issues, 
    dataset_dir = tempdir()
)
#> The datasets will be exported to C:\Users\UTZK0M\AppData\Local\Temp\RtmpeiMGSd\list_issues.yaml.

write_labels_to_dataset(
    labels = my_labels, 
    dataset_dir = tempdir()
)
#> The datasets will be exported to C:\Users\UTZK0M\AppData\Local\Temp\RtmpeiMGSd\list_labels.yaml.

write_milestones_to_dataset(
    milestones = my_milestones, 
    dataset_dir = tempdir()
)
#> The datasets will be exported to C:\Users\UTZK0M\AppData\Local\Temp\RtmpeiMGSd\list_milestones.yaml.

Options

# 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

# 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)
#> Running gh query ■■■■■■■■■■■■■■■■ 50% | ETA: 1sRunning gh query
#> ■■■■■■■■■■■■■■■■■■■■■■■ 75% | ETA: 1s Running gh query ■■■■■■■■■■■ 33% | ETA:
#> 3sRunning gh query ■■■■■■■■■■■■■■■■ 50% | ETA: 2sRunning gh query
#> ■■■■■■■■■■■■■■■■■■■■■ 67% | ETA: 2sRunning gh query ■■■■■■■■■■■■■■■■■■■■■■■■■■
#> 83% | ETA: 1s

Filtering and sorting

# Condition: issues containing "README" in its body OR title
filtered_issues <- filter_issues(
    x = my_issues,
    fields = c("body", "title"),
    values = "README",
    fields_logic_gate = "OR"
)

Sorting

Finally you can also sort your issues according to certain variable (quantitative) and impose sole filtering factors (conditions as in filter_issues()), which will be applied one after the other:

sorted_issues <- sort(
    x = my_issues,
    sorting_variables = list(list(object = "milestones", field = "due_on"),
                             list(object = "issues", field = "created_at")),
    filtering_factors = list(list(values = "bug",
                                  fields = "labels",
                                  values_logic_gate = "OR"),
                             list(values = "package", fields = "title")),
    milestones = my_milestones
)

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.