The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

{IssueTrackeR}

CRAN status R-CMD-check GH Pages built

lint Coverage CodeFactor

{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 the development version of {IssueTrackeR} from GitHub with:

# 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\RtmpUfaBSS\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\RtmpUfaBSS\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\RtmpUfaBSS\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)
#> Running gh query ■■■■■■■■■■■■■■■■ 50% | ETA: 1sRunning gh query
#> ■■■■■■■■■■■■■■■■■■■■■■■ 75% | ETA: 1s ⠙ Running gh query 3 done (0.87/s) |
#> 3.4s⠹ Running gh query 4 done (0.71/s) | 5.6s⠸ Running gh query 5 done (0.67/s)
#> | 7.5s⠼ Running gh query 6 done (0.72/s) | 8.3s Running gh query
#> ■■■■■■■■■■■■■■■■ 50% | ETA: 1sRunning gh query ■■■■■■■■■■■■■■■■■■■■■■■ 75% |
#> ETA: 1s

Filtering and sorting

There are plenty of different filtering ways.

# 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"
)

See ?filter_issues and ?contains to explore more options.

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.