Package {IssueTrackeR}

IssueTrackeR: List Things to Do

Description

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.

Author(s)

Maintainer: Tanguy Barthelemy tanguy.barthelemy@insee.fr [artist, copyright holder]

Authors:

• Tanguy Barthelemy tanguy.barthelemy@insee.fr [artist, copyright holder]

See Also

Useful links:

• https://github.com/TanguyBarthelemy/IssueTrackeR

• https://tanguybarthelemy.github.io/IssueTrackeR/

• Report bugs at https://github.com/TanguyBarthelemy/IssueTrackeR/issues

Vector Merging

Description

Add elements to a vector.

Usage

append(x, values, after = length(x))

## S3 method for class 'IssuesTB'
append(x, values, after = nrow(x))

Arguments

Value

A vector containing the values in x with the elements of values appended after the specified element of x.

References

Becker RA, Chambers JM, Wilks AR (1988). The New S Language. Chapman and Hall/CRC, London. ISBN 053409192X.

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
closed_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "closed_issues.yaml"
)
new_issues <- append(open_issues, closed_issues)

Name of last commentator

Description

Retrieve the name of the last commentator

Usage

author_last_comment(x)

## S3 method for class 'IssueTB'
author_last_comment(x)

## S3 method for class 'IssuesTB'
author_last_comment(x)

Arguments

Value

A string with the name of the last person which leaves a comment. If there is no comments, it returns an empty string.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)
author_last_comment(all_issues)
author_last_comment(all_issues[1L, ])

Format the issue in a simpler format

Description

Format the issue in a simpler format

Usage

format_issues(raw_issues, raw_comments, verbose = TRUE)

Arguments

Value

a list representing an issue with simpler structure (with number, title, body and labels) of all issues.

Examples

## Not run: 
raw_issues <- gh::gh(
    repo = "rjdemetra",
    owner = "rjdverse",
    endpoint = "/repos/:owner/:repo/issues",
    .limit = Inf,
    .progress = FALSE
)
raw_comments <- gh::gh(
    repo = "rjdemetra",
    owner = "rjdverse",
    endpoint = "/repos/:owner/:repo/issues/comments",
    .limit = Inf,
    .progress = FALSE
)
all_issues <- format_issues(raw_issues = raw_issues,
                            raw_comments = raw_comments,
                            verbose = FALSE)

## End(Not run)

Format the label in a simpler format

Description

Format the label in a simpler format

Usage

format_labels(raw_labels, verbose = TRUE)

Arguments

Value

a list representing labels with simpler structure (with name, description, colour)

Examples

## Not run: 
# With labels
raw_labels <- gh::gh(
   repo = "rjdemetra",
   owner = "rjdverse",
   endpoint = "/repos/:owner/:repo/labels",
   .limit = Inf,
   .progress = FALSE
)
format_labels(raw_labels)

## End(Not run)

Format the milestones in a simpler format

Description

Format the milestones in a simpler format

Usage

format_milestones(raw_milestones, verbose = TRUE)

Arguments

Value

a list representing milestones with simpler structure (with title, description and due_on)

Examples

## Not run: 
# With milestones
milestones_jdplus_main <- gh::gh(
    repo = "jdplus-main",
    owner = "jdemetra",
    endpoint = "/repos/:owner/:repo/milestones",
    state = "all",
    .limit = Inf,
    .progress = FALSE
 )
format_milestones(milestones_jdplus_main)

## End(Not run)

Generate Age Matrix of Open Issues

Description

Generate Age Matrix of Open Issues

Usage

generate_age_mat(x, n = 3L)

## S3 method for class 'IssuesTB'
generate_age_mat(x, n = 3L)

## Default S3 method:
generate_age_mat(x, n)

Arguments

Value

Matrix of open issue counts by age category.

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
age_matrix <- IssueTrackeR:::generate_age_mat(issues, n = 2)

Retrieve all the visible repos from a user / an organisation

Description

Returns a list of repos.

Usage

get_all_repos(owner, public = TRUE, private = TRUE, verbose = TRUE)

Arguments

Value

A string with the list of repo of a user or an organisation.

Examples

## Not run: 
get_all_repos("rjdverse")

## End(Not run)

Retrieve information from the issues of GitHub

Description

use gh to ask the API of GitHub and et a list of issues with their labels and milestones.

Usage

get_issues(
  source = c("local", "online"),
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "open_issues.yaml",
  repo = getOption("IssueTrackeR.repo"),
  owner = getOption("IssueTrackeR.owner"),
  state = c("open", "closed", "all"),
  verbose = TRUE
)

get_labels(
  source = c("local", "online"),
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "list_labels.yaml",
  repo = getOption("IssueTrackeR.repo"),
  owner = getOption("IssueTrackeR.owner"),
  verbose = TRUE
)

get_milestones(
  source = c("local", "online"),
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "list_milestones.yaml",
  repo = getOption("IssueTrackeR.repo"),
  owner = getOption("IssueTrackeR.owner"),
  state = c("open", "closed", "all"),
  verbose = TRUE
)

Arguments

Details

The functions of get type are useful to retrieve object related to issues from GitHub. So it's possible to retrieve issues, labels and milestones.

The defaults value for the argument dataset_name depends on the function:

• defaults is "list_issues.yaml" for get_issues()

• defaults is "list_milestones.yaml" for get_milestones()

• defaults is "list_labels.yaml" for get_labels()

Value

The function get_issues returns an object of class IssuesTB. It is a list composed by object of class IssueTB. An object of class IssueTB represents an issue with simpler structure (with number, title, body and labels).

The function get_labels returns a list representing labels with simpler structure (with name, description, colour).

The function get_milestones returns a list representing milestones with simpler structure (with title, description and due_on).

Examples

## Not run: 
# From online

issues <- get_issues(source = "online", owner = "rjdverse", repo = NULL)
issues <- get_issues(source = "online")
print(issues)

labels <- get_labels(source = "online")
print(labels)

milestones <- get_milestones(source = "online")
print(milestones)

## End(Not run)

# From local

path <- system.file("data_issues", package = "IssueTrackeR")
issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
milestones <- get_milestones(
    source = "local",
    dataset_dir = path,
    dataset_name = "list_milestones.yaml"
)
labels <- get_labels(
    source = "local",
    dataset_dir = path,
    dataset_name = "list_labels.yaml"
)

Number of comments

Description

Number of comments of an Issue or set of issues

Usage

get_nbr_comments(x)

## S3 method for class 'IssueTB'
get_nbr_comments(x)

## S3 method for class 'IssuesTB'
get_nbr_comments(x)

Arguments

Value

An integer or an integer vector with the number of comments of the different issues in x.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)
get_nbr_comments(all_issues)
get_nbr_comments(all_issues[1L, ])

GitHub API Error Handling Utilities

Description

A collection of functions to detect GitHub API errors and generate user-friendly error messages.

Usage

has_timeout(msg)

need_auth(msg)

api_rate_reached(msg)

has_no_http(msg)

is_not_found(msg)

is_orgs_call(msg)

is_user_call(msg)

is_repo_call(msg)

wrong_repo_msg(owner, repo)

wrong_username_msg(owner)

wrong_org_name_msg(owner)

weird_msg(msg)

Arguments

Value

For detection functions: Logical TRUE if the error condition is met. For message functions: Character vector with the error message.

Error Detection Functions

These functions check error messages for specific patterns:

• has_timeout(): Detects timeout errors

• need_auth(): Detects authentication errors

• api_rate_reached(): Detects rate limit errors

• has_no_http(): Detects HTTP connection errors

• is_not_found(): Detects resource not found errors

• is_orgs_call(): Detects organization API calls

• is_user_call(): Detects user API calls

• is_repo_call(): Detects repository API calls

Error Message Functions

These functions generate formatted error messages with troubleshooting tips:

• timeout_msg: Timeout error message

• auth_msg: Authentication error message

• api_rate_msg: Rate limit error message

• no_resource_msg: Resource not found error message

• no_http_msg: HTTP connection error message

• wrong_repo_msg(): Repository not found error message

• wrong_username_msg(): User not found error message

• wrong_org_name_msg(): Organization not found error message

• weird_msg(): Generic error message for unknown errors

Examples

# Check for error conditions
error_msg <- "API rate limit exceeded for 123.123.123.123"
IssueTrackeR:::api_rate_reached(error_msg)

# Check API call type
api_url <- "https://api.github.com/repos/owner/repo"
IssueTrackeR:::is_repo_call(api_url)
IssueTrackeR:::is_not_found(error_msg)

# Handle unknown errors
unknown_error <- "Unknown error occurred"
IssueTrackeR:::weird_msg(unknown_error)

Create a new IssueTB object

Description

Create a new IssueTB object

Usage

new_issue(x = NULL, ...)

## S3 method for class 'IssueTB'
new_issue(x, ...)

## S3 method for class 'data.frame'
new_issue(x, ...)

## S3 method for class 'list'
new_issue(x, ...)

## S3 method for class 'IssuesTB'
new_issue(x, ...)

## Default S3 method:
new_issue(
  x,
  title = NA_character_,
  body = NA_character_,
  number = NA_integer_,
  state = NA_character_,
  created_at = Sys.Date(),
  closed_at = as.Date(NA_integer_),
  labels = NULL,
  milestone = NA_character_,
  repo = NA_character_,
  owner = NA_character_,
  url = NA_character_,
  html_url = NA_character_,
  comments = NULL,
  creator = NA_character_,
  assignee = NA_character_,
  state_reason = NA_character_,
  ...
)

Arguments

Value

a IssueTB object.

Examples

# Empty issue
issue1 <- new_issue()

# Custom issue
issue1 <- new_issue(
    title = "Nouvelle issue",
    body = "Un nouveau bug pour la fonction...",
    number = 47,
    created_at = Sys.Date()
)

issue2 <- new_issue(x = issue1)

Create a new IssuesTB object

Description

Create a new IssuesTB object

Usage

new_issues(x = NULL, ...)

## S3 method for class 'IssueTB'
new_issues(x, ...)

## S3 method for class 'IssuesTB'
new_issues(x, ...)

## S3 method for class 'data.frame'
new_issues(x, ...)

## S3 method for class 'list'
new_issues(x, ...)

## Default S3 method:
new_issues(
  x,
  title,
  body,
  number,
  state,
  created_at = Sys.Date(),
  closed_at = as.Date(NA_integer_),
  labels = list(),
  comments = list(),
  milestone = NA_character_,
  repo = NA_character_,
  owner = NA_character_,
  url = NA_character_,
  html_url = NA_character_,
  creator = NA_character_,
  assignee = NA_character_,
  state_reason = NA_character_,
  ...
)

Arguments

Value

a IssuesTB object.

Examples

# Empty list of issues
issues1 <- new_issues()

# List of issues from issue
issue1 <- new_issue(
    title = "Une autre issue",
    state = "open",
    body = "J'ai une question au sujet de...",
    number = 2,
    created_at = Sys.Date()
)
issues2 <- new_issues(x = issue1)

# Custom issues
issues3 <- new_issues(
    title = "Une autre issue",
    state = "open",
    body = "J'ai une question au sujet de...",
    number = 2,
    created_at = Sys.Date()
)

issues4 <- new_issues(
    title = c("Nouvelle issue", "Une autre issue"),
    body = c("Un nouveau bug pour la fonction...",
             "J'ai une question au sujet de..."),
    state = c("open", "closed"),
    number = 1:2,
    created_at = Sys.Date()
)

Plot an IssuesTB object

Description

Visualize the evolution of an issue tracker backlog.

Two types of plots are available:

• "historic": displays the distribution of open issues by age.

• "created-closed": displays backlog size together with the numbers of newly created and newly closed issues.

Usage

## S3 method for class 'IssuesTB'
plot(x, type = c("historic", "created-closed"), n = 3L, ...)

Arguments

Details

When type = "historic", a stacked area chart is produced showing the number of open issues by age over time. This visualization highlights the evolution and aging of the backlog.

The first classes correspond to one-year intervals (0-1y, 1-2y, ..., (n-1)-ny) and the last class groups all issues older than n years.

When type = "created-closed", the total number of open issues is displayed together with the monthly numbers of newly created and newly closed issues. This visualization helps assess whether issue creation and resolution rates are balanced over time.

All statistics are aggregated monthly, from the month of the first issue creation to the current date.

Value

Invisibly returns x.

Examples

all_issues <- rbind(
    get_issues(
        source = "local",
        dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
        dataset_name = "open_issues.yaml"
    ),
    get_issues(
        source = "local",
        dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
        dataset_name = "closed_issues.yaml"
    )
)

plot(all_issues, type = "historic")
plot(all_issues, type = "created-closed")

Display IssueTB and IssuesTB object

Description

Display IssueTB and IssuesTB with formatted output in the console

Usage

## S3 method for class 'IssueTB'
print(x, ...)

## S3 method for class 'IssuesTB'
print(x, ...)

## S3 method for class 'summary.IssueTB'
print(x, ...)

## S3 method for class 'summary.IssuesTB'
print(x, ...)

## S3 method for class 'LabelsTB'
print(x, ...)

## S3 method for class 'summary.LabelsTB'
print(x, ...)

Arguments

Details

This function displays an issue (IssueTB object) or a list of issues (IssuesTB object) with a formatted output.

Value

invisibly (with invisible()) NULL.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)

# Display one issue
print(all_issues[1, ])

# Display several issues
print(all_issues[1:10, ])

# Display the summary of one issue
summary(all_issues[2, ])

# Display the summary of
summary(all_issues[1:10, ])

Combining Issues

Description

S3 method for combining IssueTB objects by rows.

Usage

## S3 method for class 'IssueTB'
rbind(...)

## S3 method for class 'IssuesTB'
rbind(...)

Arguments

Value

An IssueTB object containing the combined rows of all input objects.

See Also

rbind for the generic function

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
new_issues <- rbind(open_issues[1, ], open_issues[-1, ])
path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
closed_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "closed_issues.yaml"
)
new_issues <- rbind(open_issues, closed_issues)

Reset options

Description

Reset options

Usage

reset_options()

Value

NULL invisibly

Examples

getOption("IssueTrackeR.owner")
options(IssueTrackeR.owner = "TanguyBarthelemy")
getOption("IssueTrackeR.owner")
reset_options()
getOption("IssueTrackeR.owner")

Random Sampling

Description

Generic function for drawing a random sample from an object.

For objects of class IssuesTB, this method returns a random subset of the issues.

Usage

sample(x, size, replace = FALSE, prob = NULL)

## S3 method for class 'IssuesTB'
sample(x, size = nrow(x), replace = FALSE, prob = NULL)

## Default S3 method:
sample(x, size, replace = FALSE, prob = NULL)

Arguments

Details

The arguments and overall behavior are consistent with base::sample(). For details about the sampling algorithm, probability weights, and special cases, refer to the original documentation: https://stat.ethz.ch/R-manual/R-devel/library/base/html/sample.html

Value

• For IssuesTB objects, an object of the same class containing the sampled issues.

• For all other objects, the result of base::sample().

See Also

base::sample()

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
new_issues <- sample(open_issues, size = 5L)

Subsetting Vectors, Matrices and Data Frames

Description

Return subsets of vectors, matrices or data frames which meet conditions.

Usage

## S3 method for class 'IssuesTB'
subset(x, ...)

Arguments

Details

This is a generic function, with methods supplied for matrices, data frames and vectors (including lists). Packages and users can add further methods.

For ordinary vectors, the result is simply x[subset & !is.na(subset)].

For data frames, the subset argument works on the rows. Note that subset will be evaluated in the data frame, so columns can be referred to (by name) as variables in the expression (see the examples).

The select argument exists only for the methods for data frames and matrices. It works by first replacing column names in the selection expression with the corresponding column numbers in the data frame and then using the resulting integer vector to index the columns. This allows the use of the standard indexing conventions so that for example ranges of columns can be specified easily, or single columns can be dropped (see the examples).

The drop argument is passed on to the indexing method for matrices and data frames: note that the default for matrices is different from that for indexing.

Factors may have empty levels after subsetting; unused levels are not automatically removed. See droplevels for a way to drop all unused levels from a data frame.

Value

An object similar to x contain just the selected elements (for a vector), rows and columns (for a matrix or data frame), and so on.

Warning

This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like [, and in particular the non-standard evaluation of argument subset can have unanticipated consequences.

Author(s)

Peter Dalgaard and Brian Ripley

See Also

[, transform droplevels

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
new_issues <- subset(open_issues, number < 150)

Compute a summary of an issue or a list of issues

Description

Compute a summary of an issue or a list of issues

Usage

## S3 method for class 'IssueTB'
summary(object, ...)

## S3 method for class 'IssuesTB'
summary(object, with_labels = FALSE, ...)

## S3 method for class 'LabelsTB'
summary(object, ...)

Arguments

Details

This function compute the summary of an issue (IssueTB object) with adding some information (number of comments, ...). For a list of issues (IssuesTB object), it just summarise the information with statistics by modalities.

Value

invisibly (with invisible()) NULL.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)

# Summarise one issue
summary(all_issues[1, ])

# Summarise several issues
summary(all_issues[1:10, ])

Unique issues of an IssuesTB Object

Description

Keep only different issues from a IssuesTB Object

Usage

## S3 method for class 'IssuesTB'
unique(x, incomparables = FALSE, ...)

Arguments

Details

This method is consistent with base::unique(). For details about the generic function and its default methods, refer to the original documentation: https://stat.ethz.ch/R-manual/R-devel/library/base/html/unique.html

Value

An IssuesTB object containing only unique issues.

See Also

base::unique(), base::duplicated()

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
open_issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
new_issues <- unique(open_issues)

Update database

Description

Update the different local database (issues, labels and milestones) with the online reference.

Usage

update_database(
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  datasets_name = c(open = "open_issues.yaml", closed = "closed_issues.yaml", labels =
    "list_labels.yaml", milestones = "list_milestones.yaml"),
  verbose = TRUE,
  ...
)

Arguments

Value

invisibly (with invisible()) TRUE.

Examples

## Not run: 
update_database(dataset_dir = tempdir())

## End(Not run)

Check for comments in GitHub Issues

Description

Function to filter issues with (or without) comments.

Usage

with_comments(x, negate = FALSE)

## S3 method for class 'IssuesTB'
with_comments(x, negate = FALSE)

Arguments

Value

An object IssuesTB with issues that satisfy the condition.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)
with_comments(all_issues)
with_comments(all_issues, negate = TRUE)

Check for labels in GitHub Issues

Description

Generic function to filter issues with labels

Usage

with_labels(x, ...)

## S3 method for class 'IssuesTB'
with_labels(x, ...)

Arguments

Value

An object IssuesTB with issues that satisfy the condition.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)
with_labels(all_issues, pattern = "Bug")

Check for text in GitHub Issues

Description

Generic function to filter issues with a given text pattern in the title, body, or comments of a GitHub Issue object or a collection of Issues.

Usage

with_text(x, ...)

## S3 method for class 'IssuesTB'
with_text(x, ..., in_title = TRUE, in_body = TRUE, in_comments = TRUE)

Arguments

Value

An object IssuesTB with issues that satisfy the condition.

Examples

all_issues <- get_issues(
    source = "local",
    dataset_dir = system.file("data_issues", package = "IssueTrackeR"),
    dataset_name = "open_issues.yaml"
)
with_text(all_issues, pattern = "Excel")

Save datasets in a yaml file

Description

Save datasets in a yaml file

Usage

write_to_dataset(
  x,
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name,
  overwrite = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'IssuesTB'
write_to_dataset(
  x,
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "list_issues.yaml",
  overwrite = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'LabelsTB'
write_to_dataset(
  x,
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "list_labels.yaml",
  overwrite = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'MilestonesTB'
write_to_dataset(
  x,
  dataset_dir = getOption("IssueTrackeR.dataset.dir"),
  dataset_name = "list_milestones.yaml",
  overwrite = TRUE,
  verbose = TRUE,
  ...
)

## Default S3 method:
write_to_dataset(...)

Arguments

Details

Depending on the object, the defaults value of the argument dataset_name (by default) is:

• "list_issues.yaml" for issues;

• "list_labels.yaml" for labels;

• "list_milestones.yaml" for milestones.

Value

invisibly (with invisible()) TRUE if the export was successful and an error otherwise.

Examples

path <- system.file("data_issues", package = "IssueTrackeR")
issues <- get_issues(
    source = "local",
    dataset_dir = path,
    dataset_name = "open_issues.yaml"
)
milestones <- get_milestones(
    source = "local",
    dataset_dir = path,
    dataset_name = "list_milestones.yaml"
)
labels <- get_labels(
    source = "local",
    dataset_dir = path,
    dataset_name = "list_labels.yaml"
)

write_to_dataset(x = issues, dataset_dir = tempdir())
write_to_dataset(x = labels, dataset_dir = tempdir())
write_to_dataset(x = milestones, dataset_dir = tempdir())

write_to_dataset(x = issues, dataset_dir = tempdir(),
                 dataset_name = "my_issues")
write_to_dataset(x = labels, dataset_dir = tempdir(),
                 dataset_name = "my_labels")
write_to_dataset(x = milestones, dataset_dir = tempdir(),
                 dataset_name = "my_milestones")