This is an extremely minimal client. You need to know the API to be able to use this client. All this function does is:
Try to substitute each listed parameter into
endpoint, using the{parameter}notation.If a GET request (the default), then add all other listed parameters as query parameters.
If not a GET request, then send the other parameters in the request body, as JSON.
Convert the response to an R list using
jsonlite::fromJSON().
Usage
gh(
endpoint,
...,
per_page = NULL,
.per_page = NULL,
.token = NULL,
.destfile = NULL,
.overwrite = FALSE,
.api_url = NULL,
.method = "GET",
.limit = NULL,
.accept = "application/vnd.github.v3+json",
.send_headers = NULL,
.progress = TRUE,
.params = list(),
.max_wait = 600,
.max_rate = NULL
)Arguments
- endpoint
GitHub API endpoint. Must be one of the following forms:
METHOD path, e.g.GET /rate_limit,path, e.g./rate_limit,METHOD url, e.g.GET https://api.github.com/rate_limit,url, e.g.https://api.github.com/rate_limit.
If the method is not supplied, will use
.method, which defaults to"GET".- ...
Name-value pairs giving API parameters. Will be matched into
endpointplaceholders, sent as query parameters in GET requests, and as a JSON body of POST requests. If there is only one unnamed parameter, and it is a raw vector, then it will not be JSON encoded, but sent as raw data, as is. This can be used for example to add assets to releases. NamedNULLvalues are silently dropped. For GET requests, namedNAvalues trigger an error. For other methods, namedNAvalues are included in the body of the request, as JSONnull.- per_page, .per_page
Number of items to return per page. If omitted, will be substituted by
max(.limit, 100)if.limitis set, otherwise determined by the API (never greater than 100).- .token
Authentication token. Defaults to
GITHUB_PATorGITHUB_TOKENenvironment variables, in this order if any is set. Seegh_token()if you need more flexibility, e.g. different tokens for different GitHub Enterprise deployments.- .destfile
Path to write response to disk. If
NULL(default), response will be processed and returned as an object. If path is given, response will be written to disk in the form sent. gh writes the response to a temporary file, and renames that file to.destfileafter the request was successful. The name of the temporary file is created by adding a-<random>.gh-tmpsuffix to it, where<random>is an ASCII string with random characters. gh removes the temporary file on error.- .overwrite
If
.destfileis provided, whether to overwrite an existing file. Defaults toFALSE. If an error happens the original file is kept.- .api_url
Github API url (default: https://api.github.com). Used if
endpointjust contains a path. Defaults toGITHUB_API_URLenvironment variable if set.- .method
HTTP method to use if not explicitly supplied in the
endpoint.- .limit
Number of records to return. This can be used instead of manual pagination. By default it is
NULL, which means that the defaults of the GitHub API are used. You can set it to a number to request more (or less) records, and also toInfto request all records. Note, that if you request many records, then multiple GitHub API calls are used to get them, and this can take a potentially long time.- .accept
The value of the
AcceptHTTP header. Defaults to"application/vnd.github.v3+json". IfAcceptis given in.send_headers, then that will be used. This parameter can be used to provide a custom media type, in order to access a preview feature of the API.- .send_headers
Named character vector of header field values (except
Authorization, which is handled via.token). This can be used to override or augment the defaultUser-Agentheader:"https://github.com/r-lib/gh".- .progress
Whether to show a progress indicator for calls that need more than one HTTP request.
- .params
Additional list of parameters to append to
.... It is easier to use this than...if you have your parameters in a list already.- .max_wait
Maximum number of seconds to wait if rate limited. Defaults to 10 minutes.
- .max_rate
Maximum request rate in requests per second. Set this to automatically throttle requests.
Value
Answer from the API as a gh_response object, which is also a
list. Failed requests will generate an R error. Requests that
generate a raw response will return a raw vector.
See also
gh_gql() if you want to use the GitHub GraphQL API,
gh_whoami() for details on GitHub API token management.
Examples
## Repositories of a user, these are equivalent
gh("/users/hadley/repos", .limit = 2)
#> [
#> {
#> "id": 40423928,
#> "node_id": "MDEwOlJlcG9zaXRvcnk0MDQyMzkyOA==",
#> "name": "15-state-of-the-union",
#> "full_name": "hadley/15-state-of-the-union",
#> "private": false,
#> "owner": {
#> "login": "hadley",
#> "id": 4196,
#> "node_id": "MDQ6VXNlcjQxOTY=",
#> "avatar_url": "https://avatars.githubusercontent.com/u/4196?v=4",
#> "gravatar_id": "",
#> "url": "https://api.github.com/users/hadley",
#> "html_url": "https://github.com/hadley",
#> "followers_url": "https://api.github.com/users/hadley/followers",
#> "following_url": "https://api.github.com/users/hadley/following{/other_user}",
#> "gists_url": "https://api.github.com/users/hadley/gists{/gist_id}",
#> "starred_url": "https://api.github.com/users/hadley/starred{/owner}{/repo}",
#> "subscriptions_url": "https://api.github.com/users/hadley/subscriptions",
#> "organizations_url": "https://api.github.com/users/hadley/orgs",
#> "repos_url": "https://api.github.com/users/hadley/repos",
#> "events_url": "https://api.github.com/users/hadley/events{/privacy}",
#> "received_events_url": "https://api.github.com/users/hadley/received_events",
#> "type": "User",
#> "site_admin": false
#> },
#> "html_url": "https://github.com/hadley/15-state-of-the-union",
#> "description": {},
#> "fork": false,
#> "url": "https://api.github.com/repos/hadley/15-state-of-the-union",
#> "forks_url": "https://api.github.com/repos/hadley/15-state-of-the-union/forks",
#> "keys_url": "https://api.github.com/repos/hadley/15-state-of-the-union/keys{/key_id}",
#> "collaborators_url": "https://api.github.com/repos/hadley/15-state-of-the-union/collaborators{/collaborator}",
#> "teams_url": "https://api.github.com/repos/hadley/15-state-of-the-union/teams",
#> "hooks_url": "https://api.github.com/repos/hadley/15-state-of-the-union/hooks",
#> "issue_events_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues/events{/number}",
#> "events_url": "https://api.github.com/repos/hadley/15-state-of-the-union/events",
#> "assignees_url": "https://api.github.com/repos/hadley/15-state-of-the-union/assignees{/user}",
#> "branches_url": "https://api.github.com/repos/hadley/15-state-of-the-union/branches{/branch}",
#> "tags_url": "https://api.github.com/repos/hadley/15-state-of-the-union/tags",
#> "blobs_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/blobs{/sha}",
#> "git_tags_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/tags{/sha}",
#> "git_refs_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/refs{/sha}",
#> "trees_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/trees{/sha}",
#> "statuses_url": "https://api.github.com/repos/hadley/15-state-of-the-union/statuses/{sha}",
#> "languages_url": "https://api.github.com/repos/hadley/15-state-of-the-union/languages",
#> "stargazers_url": "https://api.github.com/repos/hadley/15-state-of-the-union/stargazers",
#> "contributors_url": "https://api.github.com/repos/hadley/15-state-of-the-union/contributors",
#> "subscribers_url": "https://api.github.com/repos/hadley/15-state-of-the-union/subscribers",
#> "subscription_url": "https://api.github.com/repos/hadley/15-state-of-the-union/subscription",
#> "commits_url": "https://api.github.com/repos/hadley/15-state-of-the-union/commits{/sha}",
#> "git_commits_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/commits{/sha}",
#> "comments_url": "https://api.github.com/repos/hadley/15-state-of-the-union/comments{/number}",
#> "issue_comment_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues/comments{/number}",
#> "contents_url": "https://api.github.com/repos/hadley/15-state-of-the-union/contents/{+path}",
#> "compare_url": "https://api.github.com/repos/hadley/15-state-of-the-union/compare/{base}...{head}",
#> "merges_url": "https://api.github.com/repos/hadley/15-state-of-the-union/merges",
#> "archive_url": "https://api.github.com/repos/hadley/15-state-of-the-union/{archive_format}{/ref}",
#> "downloads_url": "https://api.github.com/repos/hadley/15-state-of-the-union/downloads",
#> "issues_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues{/number}",
#> "pulls_url": "https://api.github.com/repos/hadley/15-state-of-the-union/pulls{/number}",
#> "milestones_url": "https://api.github.com/repos/hadley/15-state-of-the-union/milestones{/number}",
#> "notifications_url": "https://api.github.com/repos/hadley/15-state-of-the-union/notifications{?since,all,participating}",
#> "labels_url": "https://api.github.com/repos/hadley/15-state-of-the-union/labels{/name}",
#> "releases_url": "https://api.github.com/repos/hadley/15-state-of-the-union/releases{/id}",
#> "deployments_url": "https://api.github.com/repos/hadley/15-state-of-the-union/deployments",
#> "created_at": "2015-08-09T03:22:26Z",
#> "updated_at": "2022-12-31T01:27:52Z",
#> "pushed_at": "2015-08-10T20:29:10Z",
#> "git_url": "git://github.com/hadley/15-state-of-the-union.git",
#> "ssh_url": "git@github.com:hadley/15-state-of-the-union.git",
#> "clone_url": "https://github.com/hadley/15-state-of-the-union.git",
#> "svn_url": "https://github.com/hadley/15-state-of-the-union",
#> "homepage": {},
#> "size": 4519,
#> "stargazers_count": 22,
#> "watchers_count": 22,
#> "language": "R",
#> "has_issues": true,
#> "has_projects": true,
#> "has_downloads": true,
#> "has_wiki": true,
#> "has_pages": false,
#> "has_discussions": false,
#> "forks_count": 7,
#> "mirror_url": {},
#> "archived": false,
#> "disabled": false,
#> "open_issues_count": 0,
#> "license": {},
#> "allow_forking": true,
#> "is_template": false,
#> "web_commit_signoff_required": false,
#> "topics": [],
#> "visibility": "public",
#> "forks": 7,
#> "open_issues": 0,
#> "watchers": 22,
#> "default_branch": "master",
#> "permissions": {
#> "admin": false,
#> "maintain": false,
#> "push": false,
#> "triage": false,
#> "pull": false
#> }
#> },
#> {
#> "id": 40544418,
#> "node_id": "MDEwOlJlcG9zaXRvcnk0MDU0NDQxOA==",
#> "name": "15-student-papers",
#> "full_name": "hadley/15-student-papers",
#> "private": false,
#> "owner": {
#> "login": "hadley",
#> "id": 4196,
#> "node_id": "MDQ6VXNlcjQxOTY=",
#> "avatar_url": "https://avatars.githubusercontent.com/u/4196?v=4",
#> "gravatar_id": "",
#> "url": "https://api.github.com/users/hadley",
#> "html_url": "https://github.com/hadley",
#> "followers_url": "https://api.github.com/users/hadley/followers",
#> "following_url": "https://api.github.com/users/hadley/following{/other_user}",
#> "gists_url": "https://api.github.com/users/hadley/gists{/gist_id}",
#> "starred_url": "https://api.github.com/users/hadley/starred{/owner}{/repo}",
#> "subscriptions_url": "https://api.github.com/users/hadley/subscriptions",
#> "organizations_url": "https://api.github.com/users/hadley/orgs",
#> "repos_url": "https://api.github.com/users/hadley/repos",
#> "events_url": "https://api.github.com/users/hadley/events{/privacy}",
#> "received_events_url": "https://api.github.com/users/hadley/received_events",
#> "type": "User",
#> "site_admin": false
#> },
#> "html_url": "https://github.com/hadley/15-student-papers",
#> "description": "Graphics & computing student paper winners @ JSM 2015",
#> "fork": false,
#> "url": "https://api.github.com/repos/hadley/15-student-papers",
#> "forks_url": "https://api.github.com/repos/hadley/15-student-papers/forks",
#> "keys_url": "https://api.github.com/repos/hadley/15-student-papers/keys{/key_id}",
#> "collaborators_url": "https://api.github.com/repos/hadley/15-student-papers/collaborators{/collaborator}",
#> "teams_url": "https://api.github.com/repos/hadley/15-student-papers/teams",
#> "hooks_url": "https://api.github.com/repos/hadley/15-student-papers/hooks",
#> "issue_events_url": "https://api.github.com/repos/hadley/15-student-papers/issues/events{/number}",
#> "events_url": "https://api.github.com/repos/hadley/15-student-papers/events",
#> "assignees_url": "https://api.github.com/repos/hadley/15-student-papers/assignees{/user}",
#> "branches_url": "https://api.github.com/repos/hadley/15-student-papers/branches{/branch}",
#> "tags_url": "https://api.github.com/repos/hadley/15-student-papers/tags",
#> "blobs_url": "https://api.github.com/repos/hadley/15-student-papers/git/blobs{/sha}",
#> "git_tags_url": "https://api.github.com/repos/hadley/15-student-papers/git/tags{/sha}",
#> "git_refs_url": "https://api.github.com/repos/hadley/15-student-papers/git/refs{/sha}",
#> "trees_url": "https://api.github.com/repos/hadley/15-student-papers/git/trees{/sha}",
#> "statuses_url": "https://api.github.com/repos/hadley/15-student-papers/statuses/{sha}",
#> "languages_url": "https://api.github.com/repos/hadley/15-student-papers/languages",
#> "stargazers_url": "https://api.github.com/repos/hadley/15-student-papers/stargazers",
#> "contributors_url": "https://api.github.com/repos/hadley/15-student-papers/contributors",
#> "subscribers_url": "https://api.github.com/repos/hadley/15-student-papers/subscribers",
#> "subscription_url": "https://api.github.com/repos/hadley/15-student-papers/subscription",
#> "commits_url": "https://api.github.com/repos/hadley/15-student-papers/commits{/sha}",
#> "git_commits_url": "https://api.github.com/repos/hadley/15-student-papers/git/commits{/sha}",
#> "comments_url": "https://api.github.com/repos/hadley/15-student-papers/comments{/number}",
#> "issue_comment_url": "https://api.github.com/repos/hadley/15-student-papers/issues/comments{/number}",
#> "contents_url": "https://api.github.com/repos/hadley/15-student-papers/contents/{+path}",
#> "compare_url": "https://api.github.com/repos/hadley/15-student-papers/compare/{base}...{head}",
#> "merges_url": "https://api.github.com/repos/hadley/15-student-papers/merges",
#> "archive_url": "https://api.github.com/repos/hadley/15-student-papers/{archive_format}{/ref}",
#> "downloads_url": "https://api.github.com/repos/hadley/15-student-papers/downloads",
#> "issues_url": "https://api.github.com/repos/hadley/15-student-papers/issues{/number}",
#> "pulls_url": "https://api.github.com/repos/hadley/15-student-papers/pulls{/number}",
#> "milestones_url": "https://api.github.com/repos/hadley/15-student-papers/milestones{/number}",
#> "notifications_url": "https://api.github.com/repos/hadley/15-student-papers/notifications{?since,all,participating}",
#> "labels_url": "https://api.github.com/repos/hadley/15-student-papers/labels{/name}",
#> "releases_url": "https://api.github.com/repos/hadley/15-student-papers/releases{/id}",
#> "deployments_url": "https://api.github.com/repos/hadley/15-student-papers/deployments",
#> "created_at": "2015-08-11T13:51:29Z",
#> "updated_at": "2019-08-18T16:49:40Z",
#> "pushed_at": "2015-08-21T15:27:51Z",
#> "git_url": "git://github.com/hadley/15-student-papers.git",
#> "ssh_url": "git@github.com:hadley/15-student-papers.git",
#> "clone_url": "https://github.com/hadley/15-student-papers.git",
#> "svn_url": "https://github.com/hadley/15-student-papers",
#> "homepage": {},
#> "size": 2956,
#> "stargazers_count": 14,
#> "watchers_count": 14,
#> "language": "R",
#> "has_issues": true,
#> "has_projects": true,
#> "has_downloads": true,
#> "has_wiki": true,
#> "has_pages": false,
#> "has_discussions": false,
#> "forks_count": 0,
#> "mirror_url": {},
#> "archived": false,
#> "disabled": false,
#> "open_issues_count": 0,
#> "license": {},
#> "allow_forking": true,
#> "is_template": false,
#> "web_commit_signoff_required": false,
#> "topics": [],
#> "visibility": "public",
#> "forks": 0,
#> "open_issues": 0,
#> "watchers": 14,
#> "default_branch": "master",
#> "permissions": {
#> "admin": false,
#> "maintain": false,
#> "push": false,
#> "triage": false,
#> "pull": false
#> }
#> }
#> ]
gh("/users/{username}/repos", username = "hadley", .limit = 2)
#> [
#> {
#> "id": 40423928,
#> "node_id": "MDEwOlJlcG9zaXRvcnk0MDQyMzkyOA==",
#> "name": "15-state-of-the-union",
#> "full_name": "hadley/15-state-of-the-union",
#> "private": false,
#> "owner": {
#> "login": "hadley",
#> "id": 4196,
#> "node_id": "MDQ6VXNlcjQxOTY=",
#> "avatar_url": "https://avatars.githubusercontent.com/u/4196?v=4",
#> "gravatar_id": "",
#> "url": "https://api.github.com/users/hadley",
#> "html_url": "https://github.com/hadley",
#> "followers_url": "https://api.github.com/users/hadley/followers",
#> "following_url": "https://api.github.com/users/hadley/following{/other_user}",
#> "gists_url": "https://api.github.com/users/hadley/gists{/gist_id}",
#> "starred_url": "https://api.github.com/users/hadley/starred{/owner}{/repo}",
#> "subscriptions_url": "https://api.github.com/users/hadley/subscriptions",
#> "organizations_url": "https://api.github.com/users/hadley/orgs",
#> "repos_url": "https://api.github.com/users/hadley/repos",
#> "events_url": "https://api.github.com/users/hadley/events{/privacy}",
#> "received_events_url": "https://api.github.com/users/hadley/received_events",
#> "type": "User",
#> "site_admin": false
#> },
#> "html_url": "https://github.com/hadley/15-state-of-the-union",
#> "description": {},
#> "fork": false,
#> "url": "https://api.github.com/repos/hadley/15-state-of-the-union",
#> "forks_url": "https://api.github.com/repos/hadley/15-state-of-the-union/forks",
#> "keys_url": "https://api.github.com/repos/hadley/15-state-of-the-union/keys{/key_id}",
#> "collaborators_url": "https://api.github.com/repos/hadley/15-state-of-the-union/collaborators{/collaborator}",
#> "teams_url": "https://api.github.com/repos/hadley/15-state-of-the-union/teams",
#> "hooks_url": "https://api.github.com/repos/hadley/15-state-of-the-union/hooks",
#> "issue_events_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues/events{/number}",
#> "events_url": "https://api.github.com/repos/hadley/15-state-of-the-union/events",
#> "assignees_url": "https://api.github.com/repos/hadley/15-state-of-the-union/assignees{/user}",
#> "branches_url": "https://api.github.com/repos/hadley/15-state-of-the-union/branches{/branch}",
#> "tags_url": "https://api.github.com/repos/hadley/15-state-of-the-union/tags",
#> "blobs_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/blobs{/sha}",
#> "git_tags_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/tags{/sha}",
#> "git_refs_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/refs{/sha}",
#> "trees_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/trees{/sha}",
#> "statuses_url": "https://api.github.com/repos/hadley/15-state-of-the-union/statuses/{sha}",
#> "languages_url": "https://api.github.com/repos/hadley/15-state-of-the-union/languages",
#> "stargazers_url": "https://api.github.com/repos/hadley/15-state-of-the-union/stargazers",
#> "contributors_url": "https://api.github.com/repos/hadley/15-state-of-the-union/contributors",
#> "subscribers_url": "https://api.github.com/repos/hadley/15-state-of-the-union/subscribers",
#> "subscription_url": "https://api.github.com/repos/hadley/15-state-of-the-union/subscription",
#> "commits_url": "https://api.github.com/repos/hadley/15-state-of-the-union/commits{/sha}",
#> "git_commits_url": "https://api.github.com/repos/hadley/15-state-of-the-union/git/commits{/sha}",
#> "comments_url": "https://api.github.com/repos/hadley/15-state-of-the-union/comments{/number}",
#> "issue_comment_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues/comments{/number}",
#> "contents_url": "https://api.github.com/repos/hadley/15-state-of-the-union/contents/{+path}",
#> "compare_url": "https://api.github.com/repos/hadley/15-state-of-the-union/compare/{base}...{head}",
#> "merges_url": "https://api.github.com/repos/hadley/15-state-of-the-union/merges",
#> "archive_url": "https://api.github.com/repos/hadley/15-state-of-the-union/{archive_format}{/ref}",
#> "downloads_url": "https://api.github.com/repos/hadley/15-state-of-the-union/downloads",
#> "issues_url": "https://api.github.com/repos/hadley/15-state-of-the-union/issues{/number}",
#> "pulls_url": "https://api.github.com/repos/hadley/15-state-of-the-union/pulls{/number}",
#> "milestones_url": "https://api.github.com/repos/hadley/15-state-of-the-union/milestones{/number}",
#> "notifications_url": "https://api.github.com/repos/hadley/15-state-of-the-union/notifications{?since,all,participating}",
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#> "body": "Hi, \r\n\r\nWhen using `rename_with()` and ending with duplicated column names, the error message tells us about the transformed, duplicated name and the location of the original columns.\r\n\r\nHowever, it would be much clearer to have the original column names in the error message.\r\n\r\nThe locations are not of much help if `rename_with()` is used in a long pipeline, nor is the transformed name if the function is not well described.\r\n\r\nHere is an example case:\r\n\r\n``` r\r\nlibrary(tidyverse)\r\nletter_sum = function(x){\r\n x %>% map_chr(~{str_split_1(.x, \"\") %>% map_int(~which(letters==tolower(.x))) %>% sum() %>% as.character()})\r\n}\r\nmtcars %>% rename_with(letter_sum)\r\n#> Error in `rename_with()`:\r\n#> ! Names must be unique.\r\n#> x These names are duplicated:\r\n#> * \"24\" at locations 4 and 11.\r\n#> * \"43\" at locations 5 and 6.\r\n```\r\n\r\n<sup>Created on 2024-03-28 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\nWhile I admit the transformation is exaggeratedly nonsensical, it shows that the error is not very useful in such cases.\r\n",
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#> "body": "Previous versions of dplyr would allow you to manipulate empty dataframes that had been constructed from empty matrices, e.g.\r\n\r\n```r\r\npackageurl <- \"https://cran.r-project.org/src/contrib/Archive/dplyr/dplyr_1.1.3.tar.gz\"\r\ninstall.packages(packageurl, repos=NULL, type=\"source\")\r\n#> Installing package into 'C:/Users/edwar/Documents/R/win-library/4.1'\r\n#> (as 'lib' is unspecified)\r\npackageVersion('dplyr')\r\n#> [1] '1.1.3'\r\nas.data.frame(matrix(nrow = 0, ncol = 0)) |> dplyr::slice(1)\r\n#> data frame with 0 columns and 0 rows\r\n```\r\n\r\nIn dplyr 1.1.4, this code causes an internal error:\r\n\r\n```r\r\npackageurl <- \"https://cran.r-project.org/src/contrib/dplyr_1.1.4.tar.gz\"\r\ninstall.packages(packageurl, repos=NULL, type=\"source\")\r\n#> Installing package into 'C:/Users/edwar/Documents/R/win-library/4.1'\r\n#> (as 'lib' is unspecified)\r\npackageVersion('dplyr')\r\n#> [1] '1.1.4'\r\nas.data.frame(matrix(nrow = 0, ncol = 0)) |> dplyr::slice(1)\r\n#> Error: Internal error: `template` must have a `names` attribute.\r\n```\r\n\r\nBoth versions are able to reliably manipulate empty dataframes that were not constructed from matrices (e.g. `data.frame() |> dplyr::slice(1)` works just fine).",
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#> "body": "``` r\r\nwaldo::compare(dplyr:::as_join_by(\"a\"), dplyr::join_by(a == a))\r\n#> `old$exprs[[1]]`: `\"a\" == \"a\"`\r\n#> `new$exprs[[1]]`: `a == a`\r\n```\r\n\r\n<sup>Created on 2024-03-08 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI can work around, but I wonder if this may cause some downstream gotchas.",
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#> "body": "**Brief description of the problem:**\r\n\r\nConducting exploratory (_i.e._ not production-environment) data analysis often requires multiple many-to-many joins. `dplyr`'s current behavior emits warnings for each join when not explicitly specifying `relationship = \"many-to-many\"` and significantly clutter the console output. \r\n\r\nThis behavior introduces verbosity into the exploratory analysis process, where \"many-to-many\" relationships are expected, anticipated, and managed. The repeated need to specify the relationship parameter for each join operation to avoid these warnings is cumbersome and detracts from the efficiency to use `dplyr` as an exploration tool. \r\n\r\nAn option to globally silence these warnings would streamline exploratory data analyses, allowing for a focus on substantive inquiry and result interpretation.\r\n\r\n**Desired output:**\r\n\r\nA global option in `dplyr` to silence warnings for many-to-many joins, enhancing experience by reducing repetitive boilerplate and focusing on relevant outputs.\r\n\r\n**Reprex:**\r\n\r\n```r\r\nlibrary(dplyr)\r\n \r\n # Example datasets demonstrating the necessity of chained inner_joins\r\n df1 <- data.frame(key = c(1, 2, 2, 3), value = c(\"A\", \"B\", \"C\", \"D\"))\r\n df2 <- data.frame(key = c(2, 2, 3, 3, 4), value2 = c(\"W\", \"X\", \"Y\", \"Z\", \"P\"))\r\n df3 <- data.frame(key = c(2, 3, 3, 4, 5), value3 = c(\"K\", \"L\", \"M\", \"N\", \"O\"))\r\n \r\n # Chained inner_join operations common in exploratory analysis\r\n df1 %>%\r\n inner_join(df2, by = \"key\") %>%\r\n inner_join(df3, by = \"key\")\r\n\r\n key value value2 value3\r\n1 2 B W K\r\n2 2 B X K\r\n3 2 C W K\r\n4 2 C X K\r\n5 3 D Y L\r\n6 3 D Y M\r\n7 3 D Z L\r\n8 3 D Z M\r\nWarning messages:\r\n1: In inner_join(., df2, by = \"key\") :\r\n Detected an unexpected many-to-many relationship between `x` and `y`.\r\nℹ Row 2 of `x` matches multiple rows in `y`.\r\nℹ Row 1 of `y` matches multiple rows in `x`.\r\nℹ If a many-to-many relationship is expected, set `relationship = \"many-to-many\"` to silence this warning.\r\n2: In inner_join(., df3, by = \"key\") :\r\n Detected an unexpected many-to-many relationship between `x` and `y`.\r\nℹ Row 5 of `x` matches multiple rows in `y`.\r\nℹ Row 1 of `y` matches multiple rows in `x`.\r\nℹ If a many-to-many relationship is expected, set `relationship = \"many-to-many\"` to silence this warning.\r\n```\r\n\r\nIn the above reprex, joining the objects have duplicate keys results in a many-to-many join, triggering a warning, which is sometimes longer than the output. The proposed feature would allow for operations without the need to explicitly suppress warnings for each operation, assuming a global setting had been enabled.\r\n\r\n## Hypothetical implementation 1\r\n\r\nIntroduce a much faster to type alias, i.e. \"rel\" for \"relationship\" and \"mm\" for \"many-to-many\", with other options for other relationships.\r\n\r\n```r\r\n df1 %>%\r\n inner_join(df2, by = \"key\", rel = 'mm') %>%\r\n inner_join(df3, by = \"key\", rel = 'mm') \r\n```\r\n\r\n## Hypothetical implementation 2\r\n\r\nIntroduce an option to silence these warnings for a session\r\n\r\n```r\r\noptions(dplyr.silence_many_to_many_warnings = TRUE)\r\n df1 %>%\r\n inner_join(df2, by = \"key\") %>%\r\n inner_join(df3, by = \"key\") \r\n\r\n```\r\n",
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#> "body": "For example, I now have the following Table 1 and Table 2. I want to find a `value2` from both sides that is closest to the `value` based on matching the IDs. However, closest() only supports an inequality involving one of: >, >=, <, or <=.\r\n\r\n```r\r\nlibrary(purrr)\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\ndf1 = tibble(\r\n ID = c(\"NO1\", \"NO1\", \"NO2\", \"NO3\", \"NO3\", \"NO3\", \"NO4\", \"NO5\", \r\n \"NO5\", \"NO5\", \"NO5\", \"NO6\", \"NO7\", \"NO7\", \"NO7\", \"NO7\", \r\n \"NO7\", \"NO8\", \"NO9\", \"NO9\"), \r\n value = c(11, 25, 22, 24, 29, 1.75, 26, 27, 55, 44, 2.1, 31, \r\n 35, 33, 410, 21, 1.44, 80, 75, 52))\r\ndf1\r\n#> # A tibble: 20 × 2\r\n#> ID value2\r\n#> <chr> <dbl>\r\n#> 1 NO1 11 \r\n#> 2 NO1 25 \r\n#> 3 NO2 22 \r\n#> 4 NO3 24 \r\n#> 5 NO3 29 \r\n#> 6 NO3 1.75\r\n#> 7 NO4 26 \r\n#> 8 NO5 27 \r\n#> 9 NO5 55 \r\n#> 10 NO5 44 \r\n#> 11 NO5 2.1 \r\n#> 12 NO6 31 \r\n#> 13 NO7 35 \r\n#> 14 NO7 33 \r\n#> 15 NO7 410 \r\n#> 16 NO7 21 \r\n#> 17 NO7 1.44\r\n#> 18 NO8 80 \r\n#> 19 NO9 75 \r\n#> 20 NO9 52\r\ndf2 = tibble(\r\n 编号 = c(\"NO1\", \"NO1\", \"NO2\", \"NO3\", \"NO3\", \"NO4\", \"NO5\", \"NO5\", \r\n \"NO5\", \"NO5\", \"NO6\", \"NO7\", \"NO7\", \"NO7\", \"NO8\", \"NO9\", \"NO9\"), \r\n 数值2 = c(11.2, 24.6, 21.6, 22, 29.5, 26.1, 26.9, 26.4, 54.3,\r\n 42.1, 30.2, 34.7, 31.2, 20.59, 79.8, 74.2, 50.9))\r\ndf2\r\n#> # A tibble: 17 × 2\r\n#> 编号 数值2\r\n#> <chr> <dbl>\r\n#> 1 NO1 11.2\r\n#> 2 NO1 24.6\r\n#> 3 NO2 21.6\r\n#> 4 NO3 22 \r\n#> 5 NO3 29.5\r\n#> 6 NO4 26.1\r\n#> 7 NO5 26.9\r\n#> 8 NO5 26.4\r\n#> 9 NO5 54.3\r\n#> 10 NO5 42.1\r\n#> 11 NO6 30.2\r\n#> 12 NO7 34.7\r\n#> 13 NO7 31.2\r\n#> 14 NO7 20.6\r\n#> 15 NO8 79.8\r\n#> 16 NO9 74.2\r\n#> 17 NO9 50.9\r\n```\r\n\r\nThe manual method I can think of right now is:\r\n\r\n```r\r\ndf1 %>%\r\n left_join(df2, join_by(编号), relationship = \"many-to-many\") %>%\r\n group_nest(编号, 数值) %>%\r\n mutate(数值2 = map2_dbl(数值, data, \\(x, y) y$数值2[which.min(abs(y$数值2 - x))]),\r\n 误差 = abs(数值 - 数值2)) %>%\r\n select(-data)\r\n#> # A tibble: 20 × 4\r\n#> 编号 数值 数值2 误差\r\n#> <chr> <dbl> <dbl> <dbl>\r\n#> 1 NO1 11 11.2 0.200\r\n#> 2 NO1 25 24.6 0.400\r\n#> 3 NO2 22 21.6 0.400\r\n#> 4 NO3 1.75 22 20.2 \r\n#> 5 NO3 24 22 2 \r\n#> 6 NO3 29 29.5 0.5 \r\n#> 7 NO4 26 26.1 0.100\r\n#> 8 NO5 2.1 26.4 24.3 \r\n#> 9 NO5 27 26.9 0.100\r\n#> 10 NO5 44 42.1 1.90 \r\n#> 11 NO5 55 54.3 0.700\r\n#> 12 NO6 31 30.2 0.800\r\n#> 13 NO7 1.44 20.6 19.2 \r\n#> 14 NO7 21 20.6 0.41 \r\n#> 15 NO7 33 34.7 1.70 \r\n#> 16 NO7 35 34.7 0.300\r\n#> 17 NO7 410 34.7 375. \r\n#> 18 NO8 80 79.8 0.200\r\n#> 19 NO9 52 50.9 1.10 \r\n#> 20 NO9 75 74.2 0.800\r\n```",
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#> "title": "Add `tidypolars` to the list of backends?",
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#> "body": "Hello, I've been working on [`tidypolars`](https://tidypolars.etiennebacher.com/) for a few months. It provides the `tidyverse` syntax while using [`polars`](https://rpolars.github.io/) as backend. `polars` provides functions to manipulate dataframes, but also strings, dates, and many more things. Although `tidypolars` doesn't support all functions from the `tidyverse` for now (`lubridate` support in particular is lagging behind), most of `dplyr`, `tidyr`, and `stringr` functions are covered (some arguments might not be supported yet).\r\n\r\nWould you accept adding `tidypolars` to the [list of backends](https://github.com/tidyverse/dplyr?tab=readme-ov-file#backends) in the README? That would give more visibility to `tidypolars` and would hopefully encourage people to contribute, which would be greatly appreciated given the important number of R / `tidyverse` functions to translate to `polars` syntax.\r\n\r\nHappy to make a PR if so.",
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#> "body": "The `tidylog` package is an amazing supplement to the `dplyr` and `tidyr` packages. It allows immediate tracking/debugging of a pipeline by giving high-level information on the results of each step in the pipeline (see below). It's _especially_ useful in tracking joins, which I know was an important part of the error and warning handling made to the join functions in dplyr 1.1.0+.\r\n\r\nThe silly example below shows what messages `tidylog` provides about the process. \r\n\r\nI **_always_** use `tidylog` by default and I would love to also teach it to my students. But I don't because it works by masking regular tidyverse verbs with a logging version, which eliminates use of auto-complete. In order to implement auto-complete, the maintainer of the tidylog would have to constantly keep up with tidyverse updates even though they are completely separate (elbersb/tidylog#56)\r\n\r\nIt would be great if this kind of functionality were implemented **natively** and maintained by the relevant teams so that updates to APIs (e.g. new arguments) would also involve updating the log structure.\r\n\r\n``` r\r\nsuppressMessages({\r\n library(tidyverse)\r\n library(tidylog)\r\n})\r\n\r\ntbl1 <- dplyr::starwars |> \r\n filter(height > 90) |> \r\n select(name, height, mass, hair_color) |> \r\n summarize(\r\n across(c(height, mass), mean),\r\n name_list = list(name),\r\n .by = hair_color\r\n )\r\n#> filter: removed 9 rows (10%), 78 rows remaining\r\n#> select: dropped 10 variables (skin_color, eye_color, birth_year, sex, gender, …)\r\n#> summarize: now 12 rows and 4 columns, ungrouped\r\n \r\ntbl2 <- dplyr::starwars |>\r\n select(hair_color, films) |> \r\n unnest_longer(films) |> \r\n summarize(\r\n films_list = list(films),\r\n .by = hair_color\r\n )\r\n#> select: dropped 12 variables (name, height, mass, skin_color, eye_color, …)\r\n#> summarize: now 12 rows and 2 columns, ungrouped\r\n\r\njoined <- tbl1 |> \r\n full_join(\r\n tbl2,\r\n by = join_by(hair_color)\r\n )\r\n#> full_join: added one column (films_list)\r\n#> > rows only in tbl1 0\r\n#> > rows only in tbl2 0\r\n#> > matched rows 12\r\n#> > ====\r\n#> > rows total 12\r\n```\r\n\r\n<sup>Created on 2024-02-13 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n",
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#> "body": "Currently this is the message dplyr emits for `summarize()` after `group_by()` with multiple variables.\r\n\r\n``` r\r\nlibrary(dplyr)\r\n\r\nmtcars |>\r\n group_by(vs, am) |>\r\n summarize(mean_mpg = mean(mpg))\r\n#> `summarise()` has grouped output by 'vs'. You can override using the `.groups`\r\n#> argument.\r\n#> # A tibble: 4 × 3\r\n#> # Groups: vs [2]\r\n#> vs am mean_mpg\r\n#> <dbl> <dbl> <dbl>\r\n#> 1 0 0 15.0\r\n#> 2 0 1 19.8\r\n#> 3 1 0 20.7\r\n#> 4 1 1 28.4\r\n```\r\n\r\n<sup>Created on 2024-01-29 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI think this message is still confusing and would be more clear if the grouping message was about the output and it explicitly stated `.groups` is an argument in `summarize()`, e.g.,\r\n\r\n```\r\nThe output is grouped by `vs`. You can specify grouping structure of the output using the `.groups` argument in `summarize()`.\r\n```\r\n\r\nIf going this route some things to keep in mind:\r\n\r\n- Maybe \"result\" instead of \"output\" in two places in the message, or change the description of the `.groups` argument to say \"Grouping structure of the output.\" Basically, we should match what we're calling the \"thing\" that the function spits out.\r\n- It would be a nice-to-have if US/UK spelling of the function in the message matched what the spelling in the code that generates the message.\r\n\r\nAn alternative suggestion by @DavisVaughan was\r\n\r\n```\r\nsummarize() has computed your expressions grouped by (foo, bar), and has regrouped the output by (foo).\r\n```\r\n\r\nI think this is an improvement over the current message too, but I'd suggest going with something simpler like the one above.\r\n",
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#> "body": "I believe this is an unexplored performance issue, seemingly relating to dplyr::expand_across\r\n\r\nBenchmarked over a 1000 repetitions of processing ames data; There is a marked difference between direct mutation, and indirect mutation faciliated by across , seemingly both when using `where()` selection, and explicit all_of(c(..)) style selection. The latter speed degredation (of direct listing through all_of(c(...)) I think shows that the issue wont be related to checking properties a la the where() instant. \r\n\r\nI think the performance issue is significant, with direct mutation approx 3x faster than that mediated by across\r\n```\r\n# A tibble: 4 × 9\r\n expression min median *`itr/sec`* mem_alloc `gc/sec` n_itr n_gc total_time\r\n <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm>\r\n1 acrosswhere_func(ames_narrow) 3.69ms 4.26ms *219.* 1.75MB 7.70 966 34 4.42s\r\n2 across_all_of_func(ames_narrow) 3.3ms 3.83ms *256.* 64.73KB 8.20 969 31 3.78s\r\n3 direct_mutate_func(ames_narrow) 1.1ms 1.26ms *766.* 48.59KB 8.52 989 11 1.29s\r\n4 direct_mutate_with_class_detect_func(ames_narrow) 1.22ms 1.36ms *722.* 71.12KB 8.77 988 12 1.37s\r\n```\r\nI came across and considered whether this was related to #6897; however I believe it is something else.\r\nHere when using across I use the anonymous function syntax as advised.\r\n\r\nfirst a reprex and then my session info...\r\n\r\n```r\r\nlibrary(bench)\r\nlibrary(tidyverse)\r\nlibrary(modeldata)\r\noptions(\"lifecycle_verbosity\"=\"error\")\r\n\r\n\r\n(ames_narrow <- ames |> select(1:5))\r\n\r\nnum_op <- mean\r\nchar_op <- identity\r\n\r\nacrosswhere_func <- function(a){\r\n mutate(a,\r\n across(where(is.numeric),\\(x){num_op(x)}),\r\n across(where(is.character)|where(is.factor),\\(x){char_op(x)}))\r\n}\r\n\r\nacross_all_of_func <- function(a){\r\n mutate(a,\r\n across(all_of(c(\"Lot_Frontage\",\"Lot_Area\")),\\(x){num_op(x)}),\r\n across(all_of(c(\"MS_SubClass\",\"MS_Zoning\",\"Street\")),\\(x){char_op(x)}))\r\n}\r\n\r\n\r\ndirect_mutate_func <- function(a){\r\n mutate(a,\r\n Lot_Frontage = num_op(Lot_Frontage),\r\n Lot_Area = num_op(Lot_Area),\r\n MS_SubClass = char_op(MS_SubClass),\r\n MS_Zoning = char_op(MS_Zoning),\r\n Street = char_op(Street))\r\n}\r\n\r\ndirect_mutate_with_class_detect_func <- function(a){\r\n \r\n l <- map_lgl(a,\\(x)is.numeric(x))\r\n numnames <- names(l[l])\r\n l <- map_lgl(a,\\(x){is.character(x)|is.factor(x)})\r\n catnames <- names(l[l])\r\n \r\n mutate(a,\r\n Lot_Frontage = num_op(Lot_Frontage),\r\n Lot_Area = num_op(Lot_Area),\r\n MS_SubClass = char_op(MS_SubClass),\r\n MS_Zoning = char_op(MS_Zoning),\r\n Street = char_op(Street))\r\n}\r\n\r\nb1 <- mark(acrosswhere_func(ames_narrow),\r\n across_all_of_func(ames_narrow),\r\n direct_mutate_func(ames_narrow),\r\n direct_mutate_with_class_detect_func(ames_narrow),iterations = 1000L)\r\n\r\nselect(b1,1:9)\r\n\r\n```\r\nsession info \r\n```\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19045)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=English_United Kingdom.utf8 LC_CTYPE=English_United Kingdom.utf8 \r\n[3] LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=English_United Kingdom.utf8 \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods base \r\n\r\nother attached packages:\r\n [1] modeldata_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 \r\n [8] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0 bench_1.1.3 \r\n\r\nloaded via a namespace (and not attached):\r\n [1] rstudioapi_0.15.0 magrittr_2.0.3 hms_1.1.3 tidyselect_1.2.0 munsell_0.5.0 timechange_0.2.0 \r\n [7] colorspace_2.1-0 R6_2.5.1 rlang_1.1.3 fansi_1.0.4 tools_4.2.2 grid_4.2.2 \r\n[13] gtable_0.3.4 utf8_1.2.3 cli_3.6.2 withr_2.5.0 lifecycle_1.0.3 tzdb_0.4.0 \r\n[19] vctrs_0.6.5 glue_1.6.2 stringi_1.7.8 compiler_4.2.2 pillar_1.9.0 generics_0.1.3 \r\n[25] scales_1.2.1 profmem_0.6.0 pkgconfig_2.0.3 \r\n```",
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#> "body": "I want to recode certain columns based on values of columns that have a similar naming. I'm using 1. `case_when()` to recode, 2. `cur_column()` to programmatically select the \"related\" columns, and 3. `across()` to automatically do this across the relevant columns.\r\n\r\nAs shown below, I can do this when specifying one column, but not with `cur_column()`. It complains that it must be used within `across()`, even though it is being used within `across()`\r\n\r\n``` r\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\nlibrary(stringr)\r\n\r\nnames_list <-\r\n c(\"x\", \"y\", \"color\")\r\n\r\n## works\r\n\r\nstarwars |>\r\n rename(color = hair_color) |>\r\n mutate(across(\r\n skin_color,\r\n \\(x)\r\n case_when(\r\n x == \"fair\" ~ x,\r\n is.na(!!sym(na.omit(str_extract(\"skin_color\", names_list)))) ~ NA,\r\n .default = \"other\"\r\n )\r\n )) |>\r\n select(color, skin_color)\r\n#> # A tibble: 87 × 2\r\n#> color skin_color\r\n#> <chr> <chr> \r\n#> 1 blond fair \r\n#> 2 <NA> <NA> \r\n#> 3 <NA> <NA> \r\n#> 4 none other \r\n#> 5 brown other \r\n#> 6 brown, grey other \r\n#> 7 brown other \r\n#> 8 <NA> <NA> \r\n#> 9 black other \r\n#> 10 auburn, white fair \r\n#> # ℹ 77 more rows\r\n\r\n## doesn't work\r\n\r\nstarwars |>\r\n rename(color = hair_color) |>\r\n mutate(across(\r\n skin_color,\r\n \\(x)\r\n case_when(\r\n x == \"fair\" ~ x,\r\n is.na(!!sym(na.omit(str_extract(cur_column(), names_list)))) ~ NA,\r\n .default = \"other\"\r\n )\r\n )) |>\r\n select(color, skin_color)\r\n#> Error in `cur_column()`:\r\n#> ! Must only be used inside `across()`.\r\n```\r\n\r\n<sup>Created on 2024-01-15 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\n<details style=\"margin-bottom:10px;\">\r\n<summary>\r\nSession info\r\n<\/summary>\r\n\r\n``` r\r\nsessioninfo::session_info()\r\n#> ─ Session info ───────────────────────────────────────────────────────────────\r\n#> setting value\r\n#> version R version 4.3.2 (2023-10-31)\r\n#> os macOS Sonoma 14.2.1\r\n#> system aarch64, darwin23.0.0\r\n#> ui unknown\r\n#> language (EN)\r\n#> collate en_AU.UTF-8\r\n#> ctype en_AU.UTF-8\r\n#> tz Australia/Sydney\r\n#> date 2024-01-15\r\n#> pandoc 2.19.2 @ /opt/homebrew/bin/ (via rmarkdown)\r\n#> \r\n#> ─ Packages ───────────────────────────────────────────────────────────────────\r\n#> package * version date (UTC) lib source\r\n#> cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> digest 0.6.33 2023-07-07 [1] CRAN (R 4.3.2)\r\n#> dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.2)\r\n#> evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.2)\r\n#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.2)\r\n#> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.2)\r\n#> fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.2)\r\n#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.2)\r\n#> glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.2)\r\n#> htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.2)\r\n#> knitr 1.45 2023-10-30 [1] CRAN (R 4.3.2)\r\n#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.2)\r\n#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.2)\r\n#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.2)\r\n#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.2)\r\n#> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.2)\r\n#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.3.2)\r\n#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.2)\r\n#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.3.2)\r\n#> R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.3.2)\r\n#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.2)\r\n#> reprex 2.1.0 2024-01-11 [1] CRAN (R 4.3.2)\r\n#> rlang 1.1.2 2023-11-04 [1] CRAN (R 4.3.2)\r\n#> rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.2)\r\n#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.2)\r\n#> stringi 1.8.3 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.2)\r\n#> styler 1.10.2 2023-08-29 [1] CRAN (R 4.3.2)\r\n#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.2)\r\n#> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.2)\r\n#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.2)\r\n#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.2)\r\n#> withr 2.5.2 2023-10-30 [1] CRAN (R 4.3.2)\r\n#> xfun 0.41 2023-11-01 [1] CRAN (R 4.3.2)\r\n#> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> \r\n#> [1] /opt/homebrew/Cellar/r/4.3.2/lib/R/library\r\n#> \r\n#> ──────────────────────────────────────────────────────────────────────────────\r\n```\r\n\r\n<\/details>",
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#> "body": "Hello,\r\n\r\nWhen using `arrange` on grouped data (and wanting to respect the grouping), we need to use the `.by_group` argument. However, if you make a syntax error - e.g. `by_group` or `.by_groups` - then the arranging fails to respect the groups silently. This has the potential to lead to serious errors in analysis.\r\n\r\n``` r\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\n\r\n# correct behaviour\r\nmtcars |>\r\n group_by(cyl) |>\r\n arrange(mpg, .by_group = TRUE)\r\n#> # A tibble: 32 × 11\r\n#> # Groups: cyl [3]\r\n#> mpg cyl disp hp drat wt qsec vs am gear carb\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2\r\n#> 2 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1\r\n#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1\r\n#> 4 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2\r\n#> 5 24.4 4 147. 62 3.69 3.19 20 1 0 4 2\r\n#> 6 26 4 120. 91 4.43 2.14 16.7 0 1 5 2\r\n#> 7 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1\r\n#> 8 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2\r\n#> 9 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2\r\n#> 10 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1\r\n#> # ℹ 22 more rows\r\n\r\n# syntax error leads to no grouping, but happens silently\r\nmtcars |>\r\n group_by(cyl) |>\r\n arrange(mpg, .by_groups = TRUE)\r\n#> # A tibble: 32 × 11\r\n#> # Groups: cyl [3]\r\n#> mpg cyl disp hp drat wt qsec vs am gear carb\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4\r\n#> 2 10.4 8 460 215 3 5.42 17.8 0 0 3 4\r\n#> 3 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4\r\n#> 4 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4\r\n#> 5 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4\r\n#> 6 15 8 301 335 3.54 3.57 14.6 0 1 5 8\r\n#> 7 15.2 8 276. 180 3.07 3.78 18 0 0 3 3\r\n#> 8 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2\r\n#> 9 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2\r\n#> 10 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4\r\n#> # ℹ 22 more rows\r\n```\r\n\r\n<sup>Created on 2024-01-08 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI assume this is happening because of the `...` argument combined with passing an object set to `TRUE`. I think there needs to be something here that either checks for arguments similar to `.by_group` but miss-spelt, or some other method to warn / throw an error in such circumstances. Failing to group silently seems a bit dangerous.",
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#> "body": "This code does not work:\r\n```r\r\nf <- function(df, var) filter(df, {{ var }})\r\nf(mtcars, am)\r\n# Error in `filter()`:\r\n# ℹ In argument: `vs`.\r\n# Caused by error:\r\n# ! `..1` must be a logical vector, not a double vector.\r\n```\r\n\r\nThis code works:\r\n```r\r\nf <- function(df, var) filter(df, {{ var }} == 0)\r\nf(mtcars, vs)\r\n```\r\n",
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#> "body": "We are building some packages on top of all the dplyr + dbplyr infrastructure (very grateful for that) and we build some classes like 'generated_cohort_set', 'cdm_reference', 'cdm_table', 'codelist' and so.\r\n\r\nOne problem that we are facing is that there are some functions (group_by, summarise, ...) that drop the classes (see reprex). \r\nI guess that this is on purpose, but wondering why and if it is something that could be considered to be implemented in the future?\r\n\r\nhere are the packages if you are curious: https://cran.r-project.org/web/packages/CDMConnector/index.html, https://cran.r-project.org/web/packages/DrugUtilisation/index.html, https://cran.r-project.org/web/packages/PatientProfiles/index.html, https://cran.r-project.org/web/packages/IncidencePrevalence/index.html ...)\r\n\r\n``` r\r\nx <- dplyr::tibble(a = 1)\r\nclass(x) <- c(\"my_class\", class(x))\r\nclass(x)\r\n#> [1] \"my_class\" \"tbl_df\" \"tbl\" \"data.frame\"\r\nx |> dplyr::mutate(b = 1) |> class()\r\n#> [1] \"my_class\" \"tbl_df\" \"tbl\" \"data.frame\"\r\nx |> dplyr::group_by(a) |> class()\r\n#> [1] \"grouped_df\" \"tbl_df\" \"tbl\" \"data.frame\"\r\n```\r\n\r\n<sup>Created on 2023-12-05 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nFYI @edward-burn @ablack3",
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#> "body": "One common thing that I want to do in the `dplyr` workflow is take a set of variables, apply some function to them, and create a new set of variables. `across` makes this really easy, but it almost always requires me updating the names in a second step with `rename_with`. I'm wondering if a function would be passed to `.names` that accepts the vector of column names and returns the resulting columns names.\r\n\r\nFor example, I would want:\r\n```r\r\ndata |> \r\n mutate(across(\r\n matches(\"emp_[0-9]+\"),\r\n ~ .x / emp_all, \r\n .names = \"share_{.col}\"\r\n )) |>\r\n rename_with(\r\n \\(str) gsub(\"^share_emp\", \"share_\", str)\r\n ) \r\n```\r\nto become:\r\n```r\r\ndata |> \r\n mutate(across(\r\n matches(\"emp_[0-9]+\"),\r\n ~ .x / emp_all, \r\n .names = \\(str) gsub(\"^share_emp\", \"share_\", str)\r\n ))\r\n```\r\n\r\nAdmittedly, I haven't thought of how that would play with other features of the function (e.g. multiple functions). Thanks for the consideration!",
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#> "title": "Feature request: allow additional predicates in join_by",
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#> "body": "`join_by` refuses to handle any predicates it doesn't recognize:\r\n\r\n```\r\nExpressions must use one of: `==`, `>=`, `>`, `<=`, `<`, `closest()`, `between()`, `overlaps()`, or\r\n `within()`.\r\n``` \r\n\r\nThis is understandable in a narrow sense but interferes with the ability of dplyr to work with external databases like duckdb that are far more expressive. This is also inconsistent with most of the other dplyr functions, that are quite happy passing along such functions. Consider manipulating the following `lazy_tbl` objects using the explicitly spatial functions that duckdb recognizes:\r\n\r\n\r\n```r\r\nlibrary(dplyr)\r\ndevtools::install_github(\"cboettig/duckdbfs@spatial-read\")\r\nlibrary(duckdbfs)\r\n\r\ncountries <- open_dataset(\"/vsicurl/https://github.com/cboettig/duckdbfs/raw/spatial-read/inst/extdata/world.gpkg\",\r\n format = \"sf\", tblname = \"countries\")\r\n\r\ncities <- open_dataset(\"/vsicurl/https://github.com/cboettig/duckdbfs/raw/spatial-read/inst/extdata/metro.fgb\",\r\n format = \"sf\", tblname = \"cities\")\r\n\r\nmelbourne <- st_point(c(144.9633, -37.814)) |> st_as_text()\r\n\r\n# most dplyr functions have no difficulty using these arguments\r\n\r\ncountries |> filter(st_contains(geom, ST_GeomFromText({melbourne})))\r\n```\r\n\r\nnote that `dplyr::filter` (and most other such functions) have no problem passing these `st_*` functions on to duckdb, allowing us to do spatial operations. Now a very natural operation is to do spatial joins, e.g. in SQL / DBI we can do:\r\n\r\n```\r\ncon <- cached_connection() # duckdbfs connection that holds the above tables\r\n\r\nx <- DBI::dbGetQuery(con,\"\r\nSELECT countries.iso_a3, cities.geom, countries.geom AS geometry\r\nFROM countries\r\nLEFT JOIN cities\r\nON st_contains(countries.geom, cities.geom)\r\n\") |> as_tibble()\r\n```\r\n\r\nAll I'd like is for dplyr to construct this syntax with it's usual wonderful magic, e.g. I want to write:\r\n\r\n```r\r\ncountries |> left_join(cities, join_by(st_contains(geom, geom))) \r\n```\r\n \r\nWouldn't that be nice? I think it could do so if only `join_by()` was not so fastidious in asserting that it doesn't understand anything but a hard-coded set of functions. Unfortunately this obviously errors with the message:\r\n\r\n```\r\nError in `join_by()`:\r\n! Expressions must use one of: `==`, `>=`, `>`, `<=`, `<`, `closest()`, `between()`, `overlaps()`, or\r\n `within()`.\r\nℹ Expression 1 is `st_contains(geom, geom)`.\r\n```\r\n\r\nWould it be possible to just relax this assertion, e.g. when the dataset in question is a remote src?\r\n\r\n\r\n\r\n\r\n",
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#> "body": "Have had to re-confirm this for myself a few times:\r\n\r\n```r\r\nfilter(mtcars, cyl < max(cyl), hp < max(hp)) |> dim()\r\n# [1] 18 11\r\n\r\n# vs\r\nfilter(filter(mtcars, cyl < max(cyl)), hp < max(hp)) |> dim()\r\n# [1] 17 11\r\n```\r\n\r\nThis in `?dplyr` hints at what's going on:\r\n\r\n> If multiple expressions are included, they are combined with the `&` operator.\r\n\r\nBut this behavior is a bit more subtle / worth calling out IMO. This came up again recently here:\r\n\r\nhttps://github.com/r-lib/lintr/pull/2305#discussion_r1398186673\r\n\r\nFWIW it's also really not clear from reading the `filter.data.frame` implementation without being well-versed in {dplyr} internals.",
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#> "body": "`case_match()` is great to have as a more pipe-able alternative to `case_when()` following modern tidyverse approaches. However, it lacks an equivalent capabilities to one nice feature of its predecessor `recode()`: namely, where non-recoded elements could be allowed to pass through into the output. While there is a workaround for this missing feature provided in the help examples, I'm realizing it may not generalize well across use cases.\r\n\r\nAn example similar to what I encountered:\r\n\r\n``` r\r\n# Some filenames:\r\nfilenames <- \r\n c(\"juniors_2022_OU.csv\", \"roster_2023_juniors_notre_dame.csv\", \"2022_SOU_juniors_roster.csv\")\r\n\r\n# Begin building tibble to (eventually) import & organize the content from these files:\r\ntibble::tibble(\r\n\r\n # Import filenames (in practice, would be a call to list.files() ):\r\n file_names = filenames,\r\n \r\n # Get key info like year and school out of filenames: \r\n \r\n # Year is straightforward:\r\n id_year = file_names |> stringr::str_extract(\"202[23]\"),\r\n \r\n #School, however, needs initialisms expanded to minimize ambiguity:\r\n \r\n # recode() can do this inside a single set of piped instructions. It\r\n # fills in the original data when items are not recoded (& same \r\n # type as recoded output):\r\n\r\n id_school_recode = \r\n file_names |> \r\n # Remove non-school-name content:\r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\") |>\r\n # Recode initialisms:\r\n dplyr::recode(\r\n \"OU\" = \"oklahoma\",\r\n \"SOU\" = \"southern_oregon\"\r\n ),\r\n \r\n # Using case_match(), though, non-recoded elements become NA.\r\n id_school_casematch = \r\n file_names |> \r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\") |> \r\n dplyr::case_match(\r\n \"OU\" ~ \"oklahoma\",\r\n \"SOU\" ~ \"southern_oregon\"\r\n )\r\n)\r\n#> # A tibble: 3 × 4\r\n#> file_names id_year id_school_recode id_school_casematch\r\n#> <chr> <chr> <chr> <chr> \r\n#> 1 juniors_2022_OU.csv 2022 oklahoma oklahoma \r\n#> 2 roster_2023_juniors_notre_dame.c… 2023 notre_dame <NA> \r\n#> 3 2022_SOU_juniors_roster.csv 2022 southern_oregon southern_oregon\r\n \r\n``` \r\n\r\nHelp docs for case_match() has an example that uses argument `.default = <<varname>>` (e.g. species) to fill back in original data. This would work in `mutate()`, but not here in `tibble()` - using the approach requires multiple arguments specifying the same column/variable, which tibble() forbids:\r\n\r\n``` r\r\ntibble::tibble(\r\n\r\n # Locate and remove non-name content:\r\n id_school_casematch = \r\n filenames |> \r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\"),\r\n\r\n # Recode names:\r\n id_school_casematch = \r\n id_school_casematch |> \r\n dplyr::case_match(\r\n \"OU\" ~ \"oklahoma\",\r\n \"SOU\" ~ \"southern_oregon\",\r\n .default = id_school_casematch\r\n )\r\n)\r\n#> Error in `tibble::tibble()`:\r\n#> ! Column name `id_school_casematch` must not be duplicated.\r\n#> Use `.name_repair` to specify repair.\r\n#> Caused by error in `repaired_names()`:\r\n#> ! Names must be unique.\r\n#> ✖ These names are duplicated:\r\n#> * \"id_school_casematch\" at locations 1 and 2.\r\n```\r\n\r\n(also, even when using mutate or standard variable creation [<-], specifying any *new* column/variable requires 2 separate arguments/calls. Admittedly my personal opinion, but that does seems less concise/readable to me, and/or not fully capitalizing on the pipe-ability that seems to be `case_match()`'s key offering.)\r\n\r\nCould `case_match()` perhaps have an option/default added, e.g. `case_match(.x, ..., .default = .x)`, that would mirror `recode()`'s capabilities? I recognize you'd still need equivalents to `recode()`'s checks ensuring that new & pass-through content have the same type--but wouldn't that be manageable, given the `vctrs` underpinnings of this function? \r\n",
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#> "body": "Brief description of the problem:\r\n\r\nTransposing a DataFrame in dplyr currently requires a combination of pivot_longer() and pivot_wider() functions. This process is not as straightforward as using the base R t() function and can be confusing for users, especially those new to dplyr or coming from a base R background. An intuitive, one-step transposition function would greatly simplify the data manipulation process in dplyr.\r\n\r\nExpected output:\r\n\r\nA new function, possibly named transpose_df(), that allows users to transpose a DataFrame in a single step, mirroring the simplicity and ease of use of the base R t() function.\r\n\r\n----------------------------------------------------------------------------------------\r\nlibrary(dplyr)\r\nlibrary(tidyr)\r\n\r\n# Sample dataframe\r\ndf <- tibble::tibble(\r\n x = 1:3,\r\n y = 4:6,\r\n z = 7:9\r\n)\r\n\r\n# Current method for transposing in dplyr/tidyr\r\ntransposed_df <- df %>%\r\n pivot_longer(cols = everything(), names_to = \"variable\", values_to = \"value\") %>%\r\n pivot_wider(names_from = \"variable\", values_from = \"value\")\r\n\r\n# Print the transposed dataframe\r\nprint(transposed_df)\r\n",
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#> "body": "i.e. it tells you to run the lifecycle function, but there is no warning there:\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\nrlang::local_interactive()\r\n\r\ndf <- tibble(x = 1)\r\n\r\nmutate(df, across(x, mean, na.rm = TRUE))\r\n#> Warning: There was 1 warning in `mutate()`.\r\n#> ℹ In argument: `across(x, mean, na.rm = TRUE)`.\r\n#> Caused by warning:\r\n#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.\r\n#> Supply arguments directly to `.fns` through an anonymous function instead.\r\n#> \r\n#> # Previously\r\n#> across(a:b, mean, na.rm = TRUE)\r\n#> \r\n#> # Now\r\n#> across(a:b, \\(x) mean(x, na.rm = TRUE))\r\n#> This warning is displayed once every 8 hours.\r\n#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was\r\n#> generated.\r\n#> # A tibble: 1 × 1\r\n#> x\r\n#> <dbl>\r\n#> 1 1\r\n\r\nlifecycle::last_lifecycle_warnings()\r\n#> list()\r\n\r\ndplyr::last_dplyr_warnings()\r\n#> [[1]]\r\n#> <warning/rlang_warning>\r\n#> Warning in `mutate()`:\r\n#> ℹ In argument: `across(x, mean, na.rm = TRUE)`.\r\n#> Caused by warning:\r\n#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.\r\n#> Supply arguments directly to `.fns` through an anonymous function instead.\r\n#> \r\n#> # Previously\r\n#> across(a:b, mean, na.rm = TRUE)\r\n#> \r\n#> # Now\r\n#> across(a:b, \\(x) mean(x, na.rm = TRUE))\r\n#> This warning is displayed once every 8 hours.\r\n#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was\r\n#> generated.\r\n#> ---\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. ├─dplyr::mutate(df, across(x, mean, na.rm = TRUE))\r\n#> 2. ├─dplyr:::mutate.data.frame(df, across(x, mean, na.rm = TRUE)) at dplyr/R/mutate.R:146:3\r\n#> 3. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by) at dplyr/R/mutate.R:181:3\r\n#> 4. │ ├─base::withCallingHandlers(...) at dplyr/R/mutate.R:268:3\r\n#> 5. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns) at dplyr/R/mutate.R:273:7\r\n#> 6. │ └─mask$eval_all_mutate(quo) at dplyr/R/mutate.R:380:9\r\n#> 7. │ └─dplyr (local) eval() at dplyr/R/data-mask.R:94:7\r\n#> 8. └─dplyr::across(x, mean, na.rm = TRUE)\r\n```\r\n\r\n<sup>Created on 2023-11-03 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>",
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#> "body": "Maybe because of a delayed evaluation issue ?\r\n\r\n``` r\r\nbugged <- function() {\r\n withr::local_options(warn = 2)\r\n rlang::warn(\"oops\") # or warning(\"oops\")\r\n 24\r\n}\r\n\r\n# as expected\r\n\r\nbugged()\r\n#> Error: (converted from warning) oops\r\nsubset(cars, speed == bugged())\r\n#> Error: (converted from warning) oops\r\n\r\n# unexpected\r\n\r\ndplyr::filter(cars, speed == bugged())\r\n#> Warning: There was 1 warning in `dplyr::filter()`.\r\n#> ℹ In argument: `speed == bugged()`.\r\n#> Caused by warning:\r\n#> ! oops\r\n#> speed dist\r\n#> 1 24 70\r\n#> 2 24 92\r\n#> 3 24 93\r\n#> 4 24 120\r\n```\r\n\r\n<sup>Created on 2023-10-25 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n",
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#> "body": "Hi,\r\n\r\nit took me a while to get to this (very artificial) reduced example, but I managed to get it: For some reason, using `cli::warn()` inside a nested and grouped mutate function results in the described error.\r\n\r\nRemoving `cli_warn()` also removes the error and results in a sane answer.\r\n\r\nI am using R 4.2.1, with dplyr 1.1.3 and cli 3.6.1.\r\n\r\n---\r\n\r\nBrief description of the problem\r\n\r\n``` r\r\nlibrary(cli)\r\nlibrary(dplyr)\r\n#> \r\n#> Attache Paket: 'dplyr'\r\n#> Die folgenden Objekte sind maskiert von 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> Die folgenden Objekte sind maskiert von 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\n\r\nsome_warning <- function() {\r\n cli::cli_warn(\"some warning\")\r\n return(1)\r\n}\r\n\r\ncreate_new_col <- function() {\r\n tibble(col = TRUE) |>\r\n mutate(\r\n new_col = some_warning(),\r\n .by = col\r\n ) |>\r\n pull(new_col)\r\n}\r\n\r\ntibble(group = TRUE) |>\r\n mutate(new_col = create_new_col(), .by = group)\r\n#> Error in vapply(.x, .f, .mold, ..., USE.NAMES = FALSE): Werte müssen die Länge 1 haben,\r\n#> Ergebnis von FUN(X[[1]]) hat aber Länge 0\r\n```\r\n\r\n<sup>Created on 2023-10-10 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\n",
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#> "body": "Extracted from https://github.com/tidyverse/dplyr/issues/6933\r\n\r\nSometimes the function you apply in `across()` returns a tibble who's column names already uniquely identify that function (`TTR::TDI()` or `TTR::MACD()`), so you don't need to name the `list()` as that would be repetitive, and you don't need the `_1` or `_2` you get automatically.\r\n\r\nIt would be nice if you could set `.names = \"{.col}_\"` when `.unpack = TRUE` to handle this special case, but that runs the name uniqueness check too early. It creates two names called `var1_`, which do eventually get resolved _after_ the unpacking is done, and a second name uniqueness check is already done in `df_unpack()`, so I think we can skip the ones in `across_setup()` when `.unpack = TRUE`.\r\n\r\nEasiest way to do this is the probably give `across_setup()` a `name_repair` argument that we can set to `\"minimal\"` when `.unpack = TRUE`.\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\n\r\ndf <- tibble(\r\n var1 = seq(1, 100, 1)\r\n)\r\n\r\nmetric_a <- function(x) {\r\n tibble::tibble(a = x, a_info = x + 1)\r\n}\r\nmetric_b <- function(x) {\r\n tibble::tibble(b = x, b_info = x + 1)\r\n}\r\n\r\n# dont need 1 and 2 here\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n \\(.x) metric_a(.x),\r\n \\(.x) metric_b(.x)\r\n ),\r\n .unpack = TRUE\r\n ))\r\n#> # A tibble: 100 × 5\r\n#> var1 var1_1_a var1_1_a_info var1_2_b var1_2_b_info\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 1 1 2 1 2\r\n#> 2 2 2 3 2 3\r\n#> 3 3 3 4 3 4\r\n#> 4 4 4 5 4 5\r\n#> 5 5 5 6 5 6\r\n#> 6 6 6 7 6 7\r\n#> 7 7 7 8 7 8\r\n#> 8 8 8 9 8 9\r\n#> 9 9 9 10 9 10\r\n#> 10 10 10 11 10 11\r\n#> # ℹ 90 more rows\r\n\r\n# this is also repetitive\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n a = \\(.x) metric_a(.x),\r\n b = \\(.x) metric_b(.x)\r\n ),\r\n .unpack = TRUE\r\n ))\r\n#> # A tibble: 100 × 5\r\n#> var1 var1_a_a var1_a_a_info var1_b_b var1_b_b_info\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 1 1 2 1 2\r\n#> 2 2 2 3 2 3\r\n#> 3 3 3 4 3 4\r\n#> 4 4 4 5 4 5\r\n#> 5 5 5 6 5 6\r\n#> 6 6 6 7 6 7\r\n#> 7 7 7 8 7 8\r\n#> 8 8 8 9 8 9\r\n#> 9 9 9 10 9 10\r\n#> 10 10 10 11 10 11\r\n#> # ℹ 90 more rows\r\n\r\n# ideally:\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n a = \\(.x) metric_a(.x),\r\n b = \\(.x) metric_b(.x)\r\n ),\r\n .names = \"{.col}_\",\r\n .unpack = TRUE\r\n ))\r\n#> Error in `mutate()`:\r\n#> ℹ In argument: `across(...)`.\r\n#> Caused by error in `across()`:\r\n#> ! Names must be unique.\r\n#> ✖ These names are duplicated:\r\n#> * \"var1_\" at locations 1 and 2.\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. ├─dplyr::mutate(...)\r\n#> 2. ├─dplyr:::mutate.data.frame(...)\r\n#> 3. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)\r\n#> 4. │ ├─base::withCallingHandlers(...)\r\n#> 5. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)\r\n#> 6. │ └─mask$eval_all_mutate(quo)\r\n#> 7. │ └─dplyr (local) eval()\r\n#> 8. └─dplyr::across(...)\r\n#> 9. └─dplyr:::across_setup(...)\r\n#> 10. └─vctrs::vec_as_names(...)\r\n#> 11. └─vctrs (local) `<fn>`()\r\n#> 12. └─vctrs:::validate_unique(names = names, arg = arg, call = call)\r\n#> 13. └─vctrs:::stop_names_must_be_unique(names, arg, call = call)\r\n#> 14. └─vctrs:::stop_names(...)\r\n#> 15. └─vctrs:::stop_vctrs(...)\r\n#> 16. └─rlang::abort(message, class = c(class, \"vctrs_error\"), ..., call = call)\r\n```\r\n\r\n<sup>Created on 2023-10-02 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>",
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#> "body": "Is the following example a bug in `near`?\r\n```r\r\n> near(1.1 * 100 * 10^200, 110 * 10^200)\r\n[1] FALSE\r\n```\r\n_Originally posted by @bundfussr in https://github.com/pharmaverse/admiral/issues/2060#issuecomment-1691876301_\r\n\r\nThe same example works as expected with `base::all.equal()`\r\n```r\r\n> all.equal(1.1 * 100 * 10^200, 110 * 10^200)\r\n[1] TRUE\r\n``` ",
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#> "body": "The result of using `between()` in filtering is not equivalent to evaluating a conditional expression when working with ordered factors.\r\n\r\n```{r}\r\n> (x <- data.frame(Visit = c(\"Discharge\", \"Month 3\", \"Month 6\", \"Month 12\", \"Month 20\")))\r\n Visit\r\n1 Discharge\r\n2 Month 3\r\n3 Month 6\r\n4 Month 12\r\n5 Month 20\r\n\r\n> class(x$Visit)\r\n[1] \"character\"\r\n```\r\n\r\nNow let's make it an ordered factor with assumed levels (used later for other operations):\r\n```{r}\r\n> (x$Visit <- factor(x$Visit, levels = c(\"Baseline\", \"Discharge\", \"Month 3\", \"Month 6\", \"Month 12\", \"Month 20\", \"Premature\"), ordered = TRUE))\r\n[1] Discharge Month 3 Month 6 Month 12 Month 20 \r\nLevels: Baseline < Discharge < Month 3 < Month 6 < Month 12 < Month 20 < Premature\r\n```\r\n\r\nWhen filtering using the conjunction of conditions it works\r\n```{r}\r\n> x %>% filter(Visit >= \"Discharge\" & Visit <= \"Month 20\")\r\n Visit\r\n1 Discharge\r\n2 Month 3\r\n3 Month 6\r\n4 Month 12\r\n5 Month 20\r\n```\r\nWhen using the convenient between(), it doesn't\r\n```{r}\r\n> x %>% filter(between(Visit, \"Discharge\", \"Month 20\"))\r\n Visit\r\n1 Discharge\r\n2 Month 12\r\n3 Month 20\r\n```\r\n2 items are missing in the output.\r\n\r\n-----\r\nR version 4.2.0 (2022-04-22 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19045)\r\n\r\n> packageVersion(\"dplyr\")\r\n[1] ‘1.1.1’",
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#> "body": "It would be nice if `bind_rows()` would work for database tables.\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\nlibrary(purrr)\r\ncon <- DBI::dbConnect(RSQLite::SQLite(), \":memory:\")\r\n\r\n# Create random slices of mtcars\r\ntables <-\r\n 1:5 |>\r\n map(function(n) {\r\n tbl_name <- paste0(\"mtcars_\", n)\r\n copy_to(con, slice_sample(mtcars, n = 3), tbl_name)\r\n\r\n tbl(con, tbl_name)\r\n })\r\nbind_rows(!!!tables)\r\n#> Error in `bind_rows()`:\r\n#> ! Argument 1 must be a data frame or a named atomic vector.\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. └─dplyr::bind_rows(!!!tables)\r\n#> 2. └─rlang::abort(glue(\"Argument {i} must be a data frame or a named atomic vector.\"))\r\n```\r\n\r\n<sup>Created on 2023-08-07 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI know there were issues with the double dispatch and what happens if local tables and database tables are mixed.\r\nBut I think it would be sufficient if either all inputs are local tables or database tables.\r\nThis was also requested again in dbplyr: https://github.com/tidyverse/dbplyr/issues/1342.",
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#> "body": "Hi, \r\n\r\nWhen using `rename_with()` and ending with duplicated column names, the error message tells us about the transformed, duplicated name and the location of the original columns.\r\n\r\nHowever, it would be much clearer to have the original column names in the error message.\r\n\r\nThe locations are not of much help if `rename_with()` is used in a long pipeline, nor is the transformed name if the function is not well described.\r\n\r\nHere is an example case:\r\n\r\n``` r\r\nlibrary(tidyverse)\r\nletter_sum = function(x){\r\n x %>% map_chr(~{str_split_1(.x, \"\") %>% map_int(~which(letters==tolower(.x))) %>% sum() %>% as.character()})\r\n}\r\nmtcars %>% rename_with(letter_sum)\r\n#> Error in `rename_with()`:\r\n#> ! Names must be unique.\r\n#> x These names are duplicated:\r\n#> * \"24\" at locations 4 and 11.\r\n#> * \"43\" at locations 5 and 6.\r\n```\r\n\r\n<sup>Created on 2024-03-28 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\nWhile I admit the transformation is exaggeratedly nonsensical, it shows that the error is not very useful in such cases.\r\n",
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#> "body": "Previous versions of dplyr would allow you to manipulate empty dataframes that had been constructed from empty matrices, e.g.\r\n\r\n```r\r\npackageurl <- \"https://cran.r-project.org/src/contrib/Archive/dplyr/dplyr_1.1.3.tar.gz\"\r\ninstall.packages(packageurl, repos=NULL, type=\"source\")\r\n#> Installing package into 'C:/Users/edwar/Documents/R/win-library/4.1'\r\n#> (as 'lib' is unspecified)\r\npackageVersion('dplyr')\r\n#> [1] '1.1.3'\r\nas.data.frame(matrix(nrow = 0, ncol = 0)) |> dplyr::slice(1)\r\n#> data frame with 0 columns and 0 rows\r\n```\r\n\r\nIn dplyr 1.1.4, this code causes an internal error:\r\n\r\n```r\r\npackageurl <- \"https://cran.r-project.org/src/contrib/dplyr_1.1.4.tar.gz\"\r\ninstall.packages(packageurl, repos=NULL, type=\"source\")\r\n#> Installing package into 'C:/Users/edwar/Documents/R/win-library/4.1'\r\n#> (as 'lib' is unspecified)\r\npackageVersion('dplyr')\r\n#> [1] '1.1.4'\r\nas.data.frame(matrix(nrow = 0, ncol = 0)) |> dplyr::slice(1)\r\n#> Error: Internal error: `template` must have a `names` attribute.\r\n```\r\n\r\nBoth versions are able to reliably manipulate empty dataframes that were not constructed from matrices (e.g. `data.frame() |> dplyr::slice(1)` works just fine).",
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#> "body": "``` r\r\nwaldo::compare(dplyr:::as_join_by(\"a\"), dplyr::join_by(a == a))\r\n#> `old$exprs[[1]]`: `\"a\" == \"a\"`\r\n#> `new$exprs[[1]]`: `a == a`\r\n```\r\n\r\n<sup>Created on 2024-03-08 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI can work around, but I wonder if this may cause some downstream gotchas.",
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#> "body": "**Brief description of the problem:**\r\n\r\nConducting exploratory (_i.e._ not production-environment) data analysis often requires multiple many-to-many joins. `dplyr`'s current behavior emits warnings for each join when not explicitly specifying `relationship = \"many-to-many\"` and significantly clutter the console output. \r\n\r\nThis behavior introduces verbosity into the exploratory analysis process, where \"many-to-many\" relationships are expected, anticipated, and managed. The repeated need to specify the relationship parameter for each join operation to avoid these warnings is cumbersome and detracts from the efficiency to use `dplyr` as an exploration tool. \r\n\r\nAn option to globally silence these warnings would streamline exploratory data analyses, allowing for a focus on substantive inquiry and result interpretation.\r\n\r\n**Desired output:**\r\n\r\nA global option in `dplyr` to silence warnings for many-to-many joins, enhancing experience by reducing repetitive boilerplate and focusing on relevant outputs.\r\n\r\n**Reprex:**\r\n\r\n```r\r\nlibrary(dplyr)\r\n \r\n # Example datasets demonstrating the necessity of chained inner_joins\r\n df1 <- data.frame(key = c(1, 2, 2, 3), value = c(\"A\", \"B\", \"C\", \"D\"))\r\n df2 <- data.frame(key = c(2, 2, 3, 3, 4), value2 = c(\"W\", \"X\", \"Y\", \"Z\", \"P\"))\r\n df3 <- data.frame(key = c(2, 3, 3, 4, 5), value3 = c(\"K\", \"L\", \"M\", \"N\", \"O\"))\r\n \r\n # Chained inner_join operations common in exploratory analysis\r\n df1 %>%\r\n inner_join(df2, by = \"key\") %>%\r\n inner_join(df3, by = \"key\")\r\n\r\n key value value2 value3\r\n1 2 B W K\r\n2 2 B X K\r\n3 2 C W K\r\n4 2 C X K\r\n5 3 D Y L\r\n6 3 D Y M\r\n7 3 D Z L\r\n8 3 D Z M\r\nWarning messages:\r\n1: In inner_join(., df2, by = \"key\") :\r\n Detected an unexpected many-to-many relationship between `x` and `y`.\r\nℹ Row 2 of `x` matches multiple rows in `y`.\r\nℹ Row 1 of `y` matches multiple rows in `x`.\r\nℹ If a many-to-many relationship is expected, set `relationship = \"many-to-many\"` to silence this warning.\r\n2: In inner_join(., df3, by = \"key\") :\r\n Detected an unexpected many-to-many relationship between `x` and `y`.\r\nℹ Row 5 of `x` matches multiple rows in `y`.\r\nℹ Row 1 of `y` matches multiple rows in `x`.\r\nℹ If a many-to-many relationship is expected, set `relationship = \"many-to-many\"` to silence this warning.\r\n```\r\n\r\nIn the above reprex, joining the objects have duplicate keys results in a many-to-many join, triggering a warning, which is sometimes longer than the output. The proposed feature would allow for operations without the need to explicitly suppress warnings for each operation, assuming a global setting had been enabled.\r\n\r\n## Hypothetical implementation 1\r\n\r\nIntroduce a much faster to type alias, i.e. \"rel\" for \"relationship\" and \"mm\" for \"many-to-many\", with other options for other relationships.\r\n\r\n```r\r\n df1 %>%\r\n inner_join(df2, by = \"key\", rel = 'mm') %>%\r\n inner_join(df3, by = \"key\", rel = 'mm') \r\n```\r\n\r\n## Hypothetical implementation 2\r\n\r\nIntroduce an option to silence these warnings for a session\r\n\r\n```r\r\noptions(dplyr.silence_many_to_many_warnings = TRUE)\r\n df1 %>%\r\n inner_join(df2, by = \"key\") %>%\r\n inner_join(df3, by = \"key\") \r\n\r\n```\r\n",
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#> "body": "For example, I now have the following Table 1 and Table 2. I want to find a `value2` from both sides that is closest to the `value` based on matching the IDs. However, closest() only supports an inequality involving one of: >, >=, <, or <=.\r\n\r\n```r\r\nlibrary(purrr)\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\ndf1 = tibble(\r\n ID = c(\"NO1\", \"NO1\", \"NO2\", \"NO3\", \"NO3\", \"NO3\", \"NO4\", \"NO5\", \r\n \"NO5\", \"NO5\", \"NO5\", \"NO6\", \"NO7\", \"NO7\", \"NO7\", \"NO7\", \r\n \"NO7\", \"NO8\", \"NO9\", \"NO9\"), \r\n value = c(11, 25, 22, 24, 29, 1.75, 26, 27, 55, 44, 2.1, 31, \r\n 35, 33, 410, 21, 1.44, 80, 75, 52))\r\ndf1\r\n#> # A tibble: 20 × 2\r\n#> ID value2\r\n#> <chr> <dbl>\r\n#> 1 NO1 11 \r\n#> 2 NO1 25 \r\n#> 3 NO2 22 \r\n#> 4 NO3 24 \r\n#> 5 NO3 29 \r\n#> 6 NO3 1.75\r\n#> 7 NO4 26 \r\n#> 8 NO5 27 \r\n#> 9 NO5 55 \r\n#> 10 NO5 44 \r\n#> 11 NO5 2.1 \r\n#> 12 NO6 31 \r\n#> 13 NO7 35 \r\n#> 14 NO7 33 \r\n#> 15 NO7 410 \r\n#> 16 NO7 21 \r\n#> 17 NO7 1.44\r\n#> 18 NO8 80 \r\n#> 19 NO9 75 \r\n#> 20 NO9 52\r\ndf2 = tibble(\r\n 编号 = c(\"NO1\", \"NO1\", \"NO2\", \"NO3\", \"NO3\", \"NO4\", \"NO5\", \"NO5\", \r\n \"NO5\", \"NO5\", \"NO6\", \"NO7\", \"NO7\", \"NO7\", \"NO8\", \"NO9\", \"NO9\"), \r\n 数值2 = c(11.2, 24.6, 21.6, 22, 29.5, 26.1, 26.9, 26.4, 54.3,\r\n 42.1, 30.2, 34.7, 31.2, 20.59, 79.8, 74.2, 50.9))\r\ndf2\r\n#> # A tibble: 17 × 2\r\n#> 编号 数值2\r\n#> <chr> <dbl>\r\n#> 1 NO1 11.2\r\n#> 2 NO1 24.6\r\n#> 3 NO2 21.6\r\n#> 4 NO3 22 \r\n#> 5 NO3 29.5\r\n#> 6 NO4 26.1\r\n#> 7 NO5 26.9\r\n#> 8 NO5 26.4\r\n#> 9 NO5 54.3\r\n#> 10 NO5 42.1\r\n#> 11 NO6 30.2\r\n#> 12 NO7 34.7\r\n#> 13 NO7 31.2\r\n#> 14 NO7 20.6\r\n#> 15 NO8 79.8\r\n#> 16 NO9 74.2\r\n#> 17 NO9 50.9\r\n```\r\n\r\nThe manual method I can think of right now is:\r\n\r\n```r\r\ndf1 %>%\r\n left_join(df2, join_by(编号), relationship = \"many-to-many\") %>%\r\n group_nest(编号, 数值) %>%\r\n mutate(数值2 = map2_dbl(数值, data, \\(x, y) y$数值2[which.min(abs(y$数值2 - x))]),\r\n 误差 = abs(数值 - 数值2)) %>%\r\n select(-data)\r\n#> # A tibble: 20 × 4\r\n#> 编号 数值 数值2 误差\r\n#> <chr> <dbl> <dbl> <dbl>\r\n#> 1 NO1 11 11.2 0.200\r\n#> 2 NO1 25 24.6 0.400\r\n#> 3 NO2 22 21.6 0.400\r\n#> 4 NO3 1.75 22 20.2 \r\n#> 5 NO3 24 22 2 \r\n#> 6 NO3 29 29.5 0.5 \r\n#> 7 NO4 26 26.1 0.100\r\n#> 8 NO5 2.1 26.4 24.3 \r\n#> 9 NO5 27 26.9 0.100\r\n#> 10 NO5 44 42.1 1.90 \r\n#> 11 NO5 55 54.3 0.700\r\n#> 12 NO6 31 30.2 0.800\r\n#> 13 NO7 1.44 20.6 19.2 \r\n#> 14 NO7 21 20.6 0.41 \r\n#> 15 NO7 33 34.7 1.70 \r\n#> 16 NO7 35 34.7 0.300\r\n#> 17 NO7 410 34.7 375. \r\n#> 18 NO8 80 79.8 0.200\r\n#> 19 NO9 52 50.9 1.10 \r\n#> 20 NO9 75 74.2 0.800\r\n```",
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#> "body": "Hello, I've been working on [`tidypolars`](https://tidypolars.etiennebacher.com/) for a few months. It provides the `tidyverse` syntax while using [`polars`](https://rpolars.github.io/) as backend. `polars` provides functions to manipulate dataframes, but also strings, dates, and many more things. Although `tidypolars` doesn't support all functions from the `tidyverse` for now (`lubridate` support in particular is lagging behind), most of `dplyr`, `tidyr`, and `stringr` functions are covered (some arguments might not be supported yet).\r\n\r\nWould you accept adding `tidypolars` to the [list of backends](https://github.com/tidyverse/dplyr?tab=readme-ov-file#backends) in the README? That would give more visibility to `tidypolars` and would hopefully encourage people to contribute, which would be greatly appreciated given the important number of R / `tidyverse` functions to translate to `polars` syntax.\r\n\r\nHappy to make a PR if so.",
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#> "body": "The `tidylog` package is an amazing supplement to the `dplyr` and `tidyr` packages. It allows immediate tracking/debugging of a pipeline by giving high-level information on the results of each step in the pipeline (see below). It's _especially_ useful in tracking joins, which I know was an important part of the error and warning handling made to the join functions in dplyr 1.1.0+.\r\n\r\nThe silly example below shows what messages `tidylog` provides about the process. \r\n\r\nI **_always_** use `tidylog` by default and I would love to also teach it to my students. But I don't because it works by masking regular tidyverse verbs with a logging version, which eliminates use of auto-complete. In order to implement auto-complete, the maintainer of the tidylog would have to constantly keep up with tidyverse updates even though they are completely separate (elbersb/tidylog#56)\r\n\r\nIt would be great if this kind of functionality were implemented **natively** and maintained by the relevant teams so that updates to APIs (e.g. new arguments) would also involve updating the log structure.\r\n\r\n``` r\r\nsuppressMessages({\r\n library(tidyverse)\r\n library(tidylog)\r\n})\r\n\r\ntbl1 <- dplyr::starwars |> \r\n filter(height > 90) |> \r\n select(name, height, mass, hair_color) |> \r\n summarize(\r\n across(c(height, mass), mean),\r\n name_list = list(name),\r\n .by = hair_color\r\n )\r\n#> filter: removed 9 rows (10%), 78 rows remaining\r\n#> select: dropped 10 variables (skin_color, eye_color, birth_year, sex, gender, …)\r\n#> summarize: now 12 rows and 4 columns, ungrouped\r\n \r\ntbl2 <- dplyr::starwars |>\r\n select(hair_color, films) |> \r\n unnest_longer(films) |> \r\n summarize(\r\n films_list = list(films),\r\n .by = hair_color\r\n )\r\n#> select: dropped 12 variables (name, height, mass, skin_color, eye_color, …)\r\n#> summarize: now 12 rows and 2 columns, ungrouped\r\n\r\njoined <- tbl1 |> \r\n full_join(\r\n tbl2,\r\n by = join_by(hair_color)\r\n )\r\n#> full_join: added one column (films_list)\r\n#> > rows only in tbl1 0\r\n#> > rows only in tbl2 0\r\n#> > matched rows 12\r\n#> > ====\r\n#> > rows total 12\r\n```\r\n\r\n<sup>Created on 2024-02-13 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n",
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#> "body": "Currently this is the message dplyr emits for `summarize()` after `group_by()` with multiple variables.\r\n\r\n``` r\r\nlibrary(dplyr)\r\n\r\nmtcars |>\r\n group_by(vs, am) |>\r\n summarize(mean_mpg = mean(mpg))\r\n#> `summarise()` has grouped output by 'vs'. You can override using the `.groups`\r\n#> argument.\r\n#> # A tibble: 4 × 3\r\n#> # Groups: vs [2]\r\n#> vs am mean_mpg\r\n#> <dbl> <dbl> <dbl>\r\n#> 1 0 0 15.0\r\n#> 2 0 1 19.8\r\n#> 3 1 0 20.7\r\n#> 4 1 1 28.4\r\n```\r\n\r\n<sup>Created on 2024-01-29 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI think this message is still confusing and would be more clear if the grouping message was about the output and it explicitly stated `.groups` is an argument in `summarize()`, e.g.,\r\n\r\n```\r\nThe output is grouped by `vs`. You can specify grouping structure of the output using the `.groups` argument in `summarize()`.\r\n```\r\n\r\nIf going this route some things to keep in mind:\r\n\r\n- Maybe \"result\" instead of \"output\" in two places in the message, or change the description of the `.groups` argument to say \"Grouping structure of the output.\" Basically, we should match what we're calling the \"thing\" that the function spits out.\r\n- It would be a nice-to-have if US/UK spelling of the function in the message matched what the spelling in the code that generates the message.\r\n\r\nAn alternative suggestion by @DavisVaughan was\r\n\r\n```\r\nsummarize() has computed your expressions grouped by (foo, bar), and has regrouped the output by (foo).\r\n```\r\n\r\nI think this is an improvement over the current message too, but I'd suggest going with something simpler like the one above.\r\n",
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#> "body": "I believe this is an unexplored performance issue, seemingly relating to dplyr::expand_across\r\n\r\nBenchmarked over a 1000 repetitions of processing ames data; There is a marked difference between direct mutation, and indirect mutation faciliated by across , seemingly both when using `where()` selection, and explicit all_of(c(..)) style selection. The latter speed degredation (of direct listing through all_of(c(...)) I think shows that the issue wont be related to checking properties a la the where() instant. \r\n\r\nI think the performance issue is significant, with direct mutation approx 3x faster than that mediated by across\r\n```\r\n# A tibble: 4 × 9\r\n expression min median *`itr/sec`* mem_alloc `gc/sec` n_itr n_gc total_time\r\n <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm>\r\n1 acrosswhere_func(ames_narrow) 3.69ms 4.26ms *219.* 1.75MB 7.70 966 34 4.42s\r\n2 across_all_of_func(ames_narrow) 3.3ms 3.83ms *256.* 64.73KB 8.20 969 31 3.78s\r\n3 direct_mutate_func(ames_narrow) 1.1ms 1.26ms *766.* 48.59KB 8.52 989 11 1.29s\r\n4 direct_mutate_with_class_detect_func(ames_narrow) 1.22ms 1.36ms *722.* 71.12KB 8.77 988 12 1.37s\r\n```\r\nI came across and considered whether this was related to #6897; however I believe it is something else.\r\nHere when using across I use the anonymous function syntax as advised.\r\n\r\nfirst a reprex and then my session info...\r\n\r\n```r\r\nlibrary(bench)\r\nlibrary(tidyverse)\r\nlibrary(modeldata)\r\noptions(\"lifecycle_verbosity\"=\"error\")\r\n\r\n\r\n(ames_narrow <- ames |> select(1:5))\r\n\r\nnum_op <- mean\r\nchar_op <- identity\r\n\r\nacrosswhere_func <- function(a){\r\n mutate(a,\r\n across(where(is.numeric),\\(x){num_op(x)}),\r\n across(where(is.character)|where(is.factor),\\(x){char_op(x)}))\r\n}\r\n\r\nacross_all_of_func <- function(a){\r\n mutate(a,\r\n across(all_of(c(\"Lot_Frontage\",\"Lot_Area\")),\\(x){num_op(x)}),\r\n across(all_of(c(\"MS_SubClass\",\"MS_Zoning\",\"Street\")),\\(x){char_op(x)}))\r\n}\r\n\r\n\r\ndirect_mutate_func <- function(a){\r\n mutate(a,\r\n Lot_Frontage = num_op(Lot_Frontage),\r\n Lot_Area = num_op(Lot_Area),\r\n MS_SubClass = char_op(MS_SubClass),\r\n MS_Zoning = char_op(MS_Zoning),\r\n Street = char_op(Street))\r\n}\r\n\r\ndirect_mutate_with_class_detect_func <- function(a){\r\n \r\n l <- map_lgl(a,\\(x)is.numeric(x))\r\n numnames <- names(l[l])\r\n l <- map_lgl(a,\\(x){is.character(x)|is.factor(x)})\r\n catnames <- names(l[l])\r\n \r\n mutate(a,\r\n Lot_Frontage = num_op(Lot_Frontage),\r\n Lot_Area = num_op(Lot_Area),\r\n MS_SubClass = char_op(MS_SubClass),\r\n MS_Zoning = char_op(MS_Zoning),\r\n Street = char_op(Street))\r\n}\r\n\r\nb1 <- mark(acrosswhere_func(ames_narrow),\r\n across_all_of_func(ames_narrow),\r\n direct_mutate_func(ames_narrow),\r\n direct_mutate_with_class_detect_func(ames_narrow),iterations = 1000L)\r\n\r\nselect(b1,1:9)\r\n\r\n```\r\nsession info \r\n```\r\nR version 4.2.2 (2022-10-31 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19045)\r\n\r\nMatrix products: default\r\n\r\nlocale:\r\n[1] LC_COLLATE=English_United Kingdom.utf8 LC_CTYPE=English_United Kingdom.utf8 \r\n[3] LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C \r\n[5] LC_TIME=English_United Kingdom.utf8 \r\n\r\nattached base packages:\r\n[1] stats graphics grDevices utils datasets methods base \r\n\r\nother attached packages:\r\n [1] modeldata_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 \r\n [8] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0 bench_1.1.3 \r\n\r\nloaded via a namespace (and not attached):\r\n [1] rstudioapi_0.15.0 magrittr_2.0.3 hms_1.1.3 tidyselect_1.2.0 munsell_0.5.0 timechange_0.2.0 \r\n [7] colorspace_2.1-0 R6_2.5.1 rlang_1.1.3 fansi_1.0.4 tools_4.2.2 grid_4.2.2 \r\n[13] gtable_0.3.4 utf8_1.2.3 cli_3.6.2 withr_2.5.0 lifecycle_1.0.3 tzdb_0.4.0 \r\n[19] vctrs_0.6.5 glue_1.6.2 stringi_1.7.8 compiler_4.2.2 pillar_1.9.0 generics_0.1.3 \r\n[25] scales_1.2.1 profmem_0.6.0 pkgconfig_2.0.3 \r\n```",
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#> "body": "I want to recode certain columns based on values of columns that have a similar naming. I'm using 1. `case_when()` to recode, 2. `cur_column()` to programmatically select the \"related\" columns, and 3. `across()` to automatically do this across the relevant columns.\r\n\r\nAs shown below, I can do this when specifying one column, but not with `cur_column()`. It complains that it must be used within `across()`, even though it is being used within `across()`\r\n\r\n``` r\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\nlibrary(stringr)\r\n\r\nnames_list <-\r\n c(\"x\", \"y\", \"color\")\r\n\r\n## works\r\n\r\nstarwars |>\r\n rename(color = hair_color) |>\r\n mutate(across(\r\n skin_color,\r\n \\(x)\r\n case_when(\r\n x == \"fair\" ~ x,\r\n is.na(!!sym(na.omit(str_extract(\"skin_color\", names_list)))) ~ NA,\r\n .default = \"other\"\r\n )\r\n )) |>\r\n select(color, skin_color)\r\n#> # A tibble: 87 × 2\r\n#> color skin_color\r\n#> <chr> <chr> \r\n#> 1 blond fair \r\n#> 2 <NA> <NA> \r\n#> 3 <NA> <NA> \r\n#> 4 none other \r\n#> 5 brown other \r\n#> 6 brown, grey other \r\n#> 7 brown other \r\n#> 8 <NA> <NA> \r\n#> 9 black other \r\n#> 10 auburn, white fair \r\n#> # ℹ 77 more rows\r\n\r\n## doesn't work\r\n\r\nstarwars |>\r\n rename(color = hair_color) |>\r\n mutate(across(\r\n skin_color,\r\n \\(x)\r\n case_when(\r\n x == \"fair\" ~ x,\r\n is.na(!!sym(na.omit(str_extract(cur_column(), names_list)))) ~ NA,\r\n .default = \"other\"\r\n )\r\n )) |>\r\n select(color, skin_color)\r\n#> Error in `cur_column()`:\r\n#> ! Must only be used inside `across()`.\r\n```\r\n\r\n<sup>Created on 2024-01-15 with [reprex v2.1.0](https://reprex.tidyverse.org)<\/sup>\r\n\r\n<details style=\"margin-bottom:10px;\">\r\n<summary>\r\nSession info\r\n<\/summary>\r\n\r\n``` r\r\nsessioninfo::session_info()\r\n#> ─ Session info ───────────────────────────────────────────────────────────────\r\n#> setting value\r\n#> version R version 4.3.2 (2023-10-31)\r\n#> os macOS Sonoma 14.2.1\r\n#> system aarch64, darwin23.0.0\r\n#> ui unknown\r\n#> language (EN)\r\n#> collate en_AU.UTF-8\r\n#> ctype en_AU.UTF-8\r\n#> tz Australia/Sydney\r\n#> date 2024-01-15\r\n#> pandoc 2.19.2 @ /opt/homebrew/bin/ (via rmarkdown)\r\n#> \r\n#> ─ Packages ───────────────────────────────────────────────────────────────────\r\n#> package * version date (UTC) lib source\r\n#> cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> digest 0.6.33 2023-07-07 [1] CRAN (R 4.3.2)\r\n#> dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.2)\r\n#> evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.2)\r\n#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.2)\r\n#> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.2)\r\n#> fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.2)\r\n#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.2)\r\n#> glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.2)\r\n#> htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.2)\r\n#> knitr 1.45 2023-10-30 [1] CRAN (R 4.3.2)\r\n#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.2)\r\n#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.2)\r\n#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.2)\r\n#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.2)\r\n#> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.2)\r\n#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.3.2)\r\n#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.2)\r\n#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.3.2)\r\n#> R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.3.2)\r\n#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.2)\r\n#> reprex 2.1.0 2024-01-11 [1] CRAN (R 4.3.2)\r\n#> rlang 1.1.2 2023-11-04 [1] CRAN (R 4.3.2)\r\n#> rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.2)\r\n#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.2)\r\n#> stringi 1.8.3 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.2)\r\n#> styler 1.10.2 2023-08-29 [1] CRAN (R 4.3.2)\r\n#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.2)\r\n#> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.2)\r\n#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.2)\r\n#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.2)\r\n#> withr 2.5.2 2023-10-30 [1] CRAN (R 4.3.2)\r\n#> xfun 0.41 2023-11-01 [1] CRAN (R 4.3.2)\r\n#> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.2)\r\n#> \r\n#> [1] /opt/homebrew/Cellar/r/4.3.2/lib/R/library\r\n#> \r\n#> ──────────────────────────────────────────────────────────────────────────────\r\n```\r\n\r\n<\/details>",
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#> "body": "Hello,\r\n\r\nWhen using `arrange` on grouped data (and wanting to respect the grouping), we need to use the `.by_group` argument. However, if you make a syntax error - e.g. `by_group` or `.by_groups` - then the arranging fails to respect the groups silently. This has the potential to lead to serious errors in analysis.\r\n\r\n``` r\r\nlibrary(dplyr)\r\n#> \r\n#> Attaching package: 'dplyr'\r\n#> The following objects are masked from 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> The following objects are masked from 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\n\r\n# correct behaviour\r\nmtcars |>\r\n group_by(cyl) |>\r\n arrange(mpg, .by_group = TRUE)\r\n#> # A tibble: 32 × 11\r\n#> # Groups: cyl [3]\r\n#> mpg cyl disp hp drat wt qsec vs am gear carb\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2\r\n#> 2 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1\r\n#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1\r\n#> 4 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2\r\n#> 5 24.4 4 147. 62 3.69 3.19 20 1 0 4 2\r\n#> 6 26 4 120. 91 4.43 2.14 16.7 0 1 5 2\r\n#> 7 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1\r\n#> 8 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2\r\n#> 9 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2\r\n#> 10 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1\r\n#> # ℹ 22 more rows\r\n\r\n# syntax error leads to no grouping, but happens silently\r\nmtcars |>\r\n group_by(cyl) |>\r\n arrange(mpg, .by_groups = TRUE)\r\n#> # A tibble: 32 × 11\r\n#> # Groups: cyl [3]\r\n#> mpg cyl disp hp drat wt qsec vs am gear carb\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4\r\n#> 2 10.4 8 460 215 3 5.42 17.8 0 0 3 4\r\n#> 3 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4\r\n#> 4 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4\r\n#> 5 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4\r\n#> 6 15 8 301 335 3.54 3.57 14.6 0 1 5 8\r\n#> 7 15.2 8 276. 180 3.07 3.78 18 0 0 3 3\r\n#> 8 15.2 8 304 150 3.15 3.44 17.3 0 0 3 2\r\n#> 9 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2\r\n#> 10 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4\r\n#> # ℹ 22 more rows\r\n```\r\n\r\n<sup>Created on 2024-01-08 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI assume this is happening because of the `...` argument combined with passing an object set to `TRUE`. I think there needs to be something here that either checks for arguments similar to `.by_group` but miss-spelt, or some other method to warn / throw an error in such circumstances. Failing to group silently seems a bit dangerous.",
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#> "body": "This code does not work:\r\n```r\r\nf <- function(df, var) filter(df, {{ var }})\r\nf(mtcars, am)\r\n# Error in `filter()`:\r\n# ℹ In argument: `vs`.\r\n# Caused by error:\r\n# ! `..1` must be a logical vector, not a double vector.\r\n```\r\n\r\nThis code works:\r\n```r\r\nf <- function(df, var) filter(df, {{ var }} == 0)\r\nf(mtcars, vs)\r\n```\r\n",
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#> "body": "We are building some packages on top of all the dplyr + dbplyr infrastructure (very grateful for that) and we build some classes like 'generated_cohort_set', 'cdm_reference', 'cdm_table', 'codelist' and so.\r\n\r\nOne problem that we are facing is that there are some functions (group_by, summarise, ...) that drop the classes (see reprex). \r\nI guess that this is on purpose, but wondering why and if it is something that could be considered to be implemented in the future?\r\n\r\nhere are the packages if you are curious: https://cran.r-project.org/web/packages/CDMConnector/index.html, https://cran.r-project.org/web/packages/DrugUtilisation/index.html, https://cran.r-project.org/web/packages/PatientProfiles/index.html, https://cran.r-project.org/web/packages/IncidencePrevalence/index.html ...)\r\n\r\n``` r\r\nx <- dplyr::tibble(a = 1)\r\nclass(x) <- c(\"my_class\", class(x))\r\nclass(x)\r\n#> [1] \"my_class\" \"tbl_df\" \"tbl\" \"data.frame\"\r\nx |> dplyr::mutate(b = 1) |> class()\r\n#> [1] \"my_class\" \"tbl_df\" \"tbl\" \"data.frame\"\r\nx |> dplyr::group_by(a) |> class()\r\n#> [1] \"grouped_df\" \"tbl_df\" \"tbl\" \"data.frame\"\r\n```\r\n\r\n<sup>Created on 2023-12-05 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nFYI @edward-burn @ablack3",
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#> "body": "One common thing that I want to do in the `dplyr` workflow is take a set of variables, apply some function to them, and create a new set of variables. `across` makes this really easy, but it almost always requires me updating the names in a second step with `rename_with`. I'm wondering if a function would be passed to `.names` that accepts the vector of column names and returns the resulting columns names.\r\n\r\nFor example, I would want:\r\n```r\r\ndata |> \r\n mutate(across(\r\n matches(\"emp_[0-9]+\"),\r\n ~ .x / emp_all, \r\n .names = \"share_{.col}\"\r\n )) |>\r\n rename_with(\r\n \\(str) gsub(\"^share_emp\", \"share_\", str)\r\n ) \r\n```\r\nto become:\r\n```r\r\ndata |> \r\n mutate(across(\r\n matches(\"emp_[0-9]+\"),\r\n ~ .x / emp_all, \r\n .names = \\(str) gsub(\"^share_emp\", \"share_\", str)\r\n ))\r\n```\r\n\r\nAdmittedly, I haven't thought of how that would play with other features of the function (e.g. multiple functions). Thanks for the consideration!",
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#> "title": "Feature request: allow additional predicates in join_by",
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#> "body": "`join_by` refuses to handle any predicates it doesn't recognize:\r\n\r\n```\r\nExpressions must use one of: `==`, `>=`, `>`, `<=`, `<`, `closest()`, `between()`, `overlaps()`, or\r\n `within()`.\r\n``` \r\n\r\nThis is understandable in a narrow sense but interferes with the ability of dplyr to work with external databases like duckdb that are far more expressive. This is also inconsistent with most of the other dplyr functions, that are quite happy passing along such functions. Consider manipulating the following `lazy_tbl` objects using the explicitly spatial functions that duckdb recognizes:\r\n\r\n\r\n```r\r\nlibrary(dplyr)\r\ndevtools::install_github(\"cboettig/duckdbfs@spatial-read\")\r\nlibrary(duckdbfs)\r\n\r\ncountries <- open_dataset(\"/vsicurl/https://github.com/cboettig/duckdbfs/raw/spatial-read/inst/extdata/world.gpkg\",\r\n format = \"sf\", tblname = \"countries\")\r\n\r\ncities <- open_dataset(\"/vsicurl/https://github.com/cboettig/duckdbfs/raw/spatial-read/inst/extdata/metro.fgb\",\r\n format = \"sf\", tblname = \"cities\")\r\n\r\nmelbourne <- st_point(c(144.9633, -37.814)) |> st_as_text()\r\n\r\n# most dplyr functions have no difficulty using these arguments\r\n\r\ncountries |> filter(st_contains(geom, ST_GeomFromText({melbourne})))\r\n```\r\n\r\nnote that `dplyr::filter` (and most other such functions) have no problem passing these `st_*` functions on to duckdb, allowing us to do spatial operations. Now a very natural operation is to do spatial joins, e.g. in SQL / DBI we can do:\r\n\r\n```\r\ncon <- cached_connection() # duckdbfs connection that holds the above tables\r\n\r\nx <- DBI::dbGetQuery(con,\"\r\nSELECT countries.iso_a3, cities.geom, countries.geom AS geometry\r\nFROM countries\r\nLEFT JOIN cities\r\nON st_contains(countries.geom, cities.geom)\r\n\") |> as_tibble()\r\n```\r\n\r\nAll I'd like is for dplyr to construct this syntax with it's usual wonderful magic, e.g. I want to write:\r\n\r\n```r\r\ncountries |> left_join(cities, join_by(st_contains(geom, geom))) \r\n```\r\n \r\nWouldn't that be nice? I think it could do so if only `join_by()` was not so fastidious in asserting that it doesn't understand anything but a hard-coded set of functions. Unfortunately this obviously errors with the message:\r\n\r\n```\r\nError in `join_by()`:\r\n! Expressions must use one of: `==`, `>=`, `>`, `<=`, `<`, `closest()`, `between()`, `overlaps()`, or\r\n `within()`.\r\nℹ Expression 1 is `st_contains(geom, geom)`.\r\n```\r\n\r\nWould it be possible to just relax this assertion, e.g. when the dataset in question is a remote src?\r\n\r\n\r\n\r\n\r\n",
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#> "body": "Have had to re-confirm this for myself a few times:\r\n\r\n```r\r\nfilter(mtcars, cyl < max(cyl), hp < max(hp)) |> dim()\r\n# [1] 18 11\r\n\r\n# vs\r\nfilter(filter(mtcars, cyl < max(cyl)), hp < max(hp)) |> dim()\r\n# [1] 17 11\r\n```\r\n\r\nThis in `?dplyr` hints at what's going on:\r\n\r\n> If multiple expressions are included, they are combined with the `&` operator.\r\n\r\nBut this behavior is a bit more subtle / worth calling out IMO. This came up again recently here:\r\n\r\nhttps://github.com/r-lib/lintr/pull/2305#discussion_r1398186673\r\n\r\nFWIW it's also really not clear from reading the `filter.data.frame` implementation without being well-versed in {dplyr} internals.",
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#> "body": "`case_match()` is great to have as a more pipe-able alternative to `case_when()` following modern tidyverse approaches. However, it lacks an equivalent capabilities to one nice feature of its predecessor `recode()`: namely, where non-recoded elements could be allowed to pass through into the output. While there is a workaround for this missing feature provided in the help examples, I'm realizing it may not generalize well across use cases.\r\n\r\nAn example similar to what I encountered:\r\n\r\n``` r\r\n# Some filenames:\r\nfilenames <- \r\n c(\"juniors_2022_OU.csv\", \"roster_2023_juniors_notre_dame.csv\", \"2022_SOU_juniors_roster.csv\")\r\n\r\n# Begin building tibble to (eventually) import & organize the content from these files:\r\ntibble::tibble(\r\n\r\n # Import filenames (in practice, would be a call to list.files() ):\r\n file_names = filenames,\r\n \r\n # Get key info like year and school out of filenames: \r\n \r\n # Year is straightforward:\r\n id_year = file_names |> stringr::str_extract(\"202[23]\"),\r\n \r\n #School, however, needs initialisms expanded to minimize ambiguity:\r\n \r\n # recode() can do this inside a single set of piped instructions. It\r\n # fills in the original data when items are not recoded (& same \r\n # type as recoded output):\r\n\r\n id_school_recode = \r\n file_names |> \r\n # Remove non-school-name content:\r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\") |>\r\n # Recode initialisms:\r\n dplyr::recode(\r\n \"OU\" = \"oklahoma\",\r\n \"SOU\" = \"southern_oregon\"\r\n ),\r\n \r\n # Using case_match(), though, non-recoded elements become NA.\r\n id_school_casematch = \r\n file_names |> \r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\") |> \r\n dplyr::case_match(\r\n \"OU\" ~ \"oklahoma\",\r\n \"SOU\" ~ \"southern_oregon\"\r\n )\r\n)\r\n#> # A tibble: 3 × 4\r\n#> file_names id_year id_school_recode id_school_casematch\r\n#> <chr> <chr> <chr> <chr> \r\n#> 1 juniors_2022_OU.csv 2022 oklahoma oklahoma \r\n#> 2 roster_2023_juniors_notre_dame.c… 2023 notre_dame <NA> \r\n#> 3 2022_SOU_juniors_roster.csv 2022 southern_oregon southern_oregon\r\n \r\n``` \r\n\r\nHelp docs for case_match() has an example that uses argument `.default = <<varname>>` (e.g. species) to fill back in original data. This would work in `mutate()`, but not here in `tibble()` - using the approach requires multiple arguments specifying the same column/variable, which tibble() forbids:\r\n\r\n``` r\r\ntibble::tibble(\r\n\r\n # Locate and remove non-name content:\r\n id_school_casematch = \r\n filenames |> \r\n stringr::str_remove(\".csv$\") |> \r\n stringr::str_remove(\"_?202[23]_?\") |> \r\n stringr::str_remove(\"_?juniors_?\") |> \r\n stringr::str_remove(\"_?roster_?\"),\r\n\r\n # Recode names:\r\n id_school_casematch = \r\n id_school_casematch |> \r\n dplyr::case_match(\r\n \"OU\" ~ \"oklahoma\",\r\n \"SOU\" ~ \"southern_oregon\",\r\n .default = id_school_casematch\r\n )\r\n)\r\n#> Error in `tibble::tibble()`:\r\n#> ! Column name `id_school_casematch` must not be duplicated.\r\n#> Use `.name_repair` to specify repair.\r\n#> Caused by error in `repaired_names()`:\r\n#> ! Names must be unique.\r\n#> ✖ These names are duplicated:\r\n#> * \"id_school_casematch\" at locations 1 and 2.\r\n```\r\n\r\n(also, even when using mutate or standard variable creation [<-], specifying any *new* column/variable requires 2 separate arguments/calls. Admittedly my personal opinion, but that does seems less concise/readable to me, and/or not fully capitalizing on the pipe-ability that seems to be `case_match()`'s key offering.)\r\n\r\nCould `case_match()` perhaps have an option/default added, e.g. `case_match(.x, ..., .default = .x)`, that would mirror `recode()`'s capabilities? I recognize you'd still need equivalents to `recode()`'s checks ensuring that new & pass-through content have the same type--but wouldn't that be manageable, given the `vctrs` underpinnings of this function? \r\n",
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#> "body": "Brief description of the problem:\r\n\r\nTransposing a DataFrame in dplyr currently requires a combination of pivot_longer() and pivot_wider() functions. This process is not as straightforward as using the base R t() function and can be confusing for users, especially those new to dplyr or coming from a base R background. An intuitive, one-step transposition function would greatly simplify the data manipulation process in dplyr.\r\n\r\nExpected output:\r\n\r\nA new function, possibly named transpose_df(), that allows users to transpose a DataFrame in a single step, mirroring the simplicity and ease of use of the base R t() function.\r\n\r\n----------------------------------------------------------------------------------------\r\nlibrary(dplyr)\r\nlibrary(tidyr)\r\n\r\n# Sample dataframe\r\ndf <- tibble::tibble(\r\n x = 1:3,\r\n y = 4:6,\r\n z = 7:9\r\n)\r\n\r\n# Current method for transposing in dplyr/tidyr\r\ntransposed_df <- df %>%\r\n pivot_longer(cols = everything(), names_to = \"variable\", values_to = \"value\") %>%\r\n pivot_wider(names_from = \"variable\", values_from = \"value\")\r\n\r\n# Print the transposed dataframe\r\nprint(transposed_df)\r\n",
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#> "body": "i.e. it tells you to run the lifecycle function, but there is no warning there:\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\nrlang::local_interactive()\r\n\r\ndf <- tibble(x = 1)\r\n\r\nmutate(df, across(x, mean, na.rm = TRUE))\r\n#> Warning: There was 1 warning in `mutate()`.\r\n#> ℹ In argument: `across(x, mean, na.rm = TRUE)`.\r\n#> Caused by warning:\r\n#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.\r\n#> Supply arguments directly to `.fns` through an anonymous function instead.\r\n#> \r\n#> # Previously\r\n#> across(a:b, mean, na.rm = TRUE)\r\n#> \r\n#> # Now\r\n#> across(a:b, \\(x) mean(x, na.rm = TRUE))\r\n#> This warning is displayed once every 8 hours.\r\n#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was\r\n#> generated.\r\n#> # A tibble: 1 × 1\r\n#> x\r\n#> <dbl>\r\n#> 1 1\r\n\r\nlifecycle::last_lifecycle_warnings()\r\n#> list()\r\n\r\ndplyr::last_dplyr_warnings()\r\n#> [[1]]\r\n#> <warning/rlang_warning>\r\n#> Warning in `mutate()`:\r\n#> ℹ In argument: `across(x, mean, na.rm = TRUE)`.\r\n#> Caused by warning:\r\n#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.\r\n#> Supply arguments directly to `.fns` through an anonymous function instead.\r\n#> \r\n#> # Previously\r\n#> across(a:b, mean, na.rm = TRUE)\r\n#> \r\n#> # Now\r\n#> across(a:b, \\(x) mean(x, na.rm = TRUE))\r\n#> This warning is displayed once every 8 hours.\r\n#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was\r\n#> generated.\r\n#> ---\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. ├─dplyr::mutate(df, across(x, mean, na.rm = TRUE))\r\n#> 2. ├─dplyr:::mutate.data.frame(df, across(x, mean, na.rm = TRUE)) at dplyr/R/mutate.R:146:3\r\n#> 3. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by) at dplyr/R/mutate.R:181:3\r\n#> 4. │ ├─base::withCallingHandlers(...) at dplyr/R/mutate.R:268:3\r\n#> 5. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns) at dplyr/R/mutate.R:273:7\r\n#> 6. │ └─mask$eval_all_mutate(quo) at dplyr/R/mutate.R:380:9\r\n#> 7. │ └─dplyr (local) eval() at dplyr/R/data-mask.R:94:7\r\n#> 8. └─dplyr::across(x, mean, na.rm = TRUE)\r\n```\r\n\r\n<sup>Created on 2023-11-03 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>",
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#> "body": "Maybe because of a delayed evaluation issue ?\r\n\r\n``` r\r\nbugged <- function() {\r\n withr::local_options(warn = 2)\r\n rlang::warn(\"oops\") # or warning(\"oops\")\r\n 24\r\n}\r\n\r\n# as expected\r\n\r\nbugged()\r\n#> Error: (converted from warning) oops\r\nsubset(cars, speed == bugged())\r\n#> Error: (converted from warning) oops\r\n\r\n# unexpected\r\n\r\ndplyr::filter(cars, speed == bugged())\r\n#> Warning: There was 1 warning in `dplyr::filter()`.\r\n#> ℹ In argument: `speed == bugged()`.\r\n#> Caused by warning:\r\n#> ! oops\r\n#> speed dist\r\n#> 1 24 70\r\n#> 2 24 92\r\n#> 3 24 93\r\n#> 4 24 120\r\n```\r\n\r\n<sup>Created on 2023-10-25 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n",
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#> "body": "Hi,\r\n\r\nit took me a while to get to this (very artificial) reduced example, but I managed to get it: For some reason, using `cli::warn()` inside a nested and grouped mutate function results in the described error.\r\n\r\nRemoving `cli_warn()` also removes the error and results in a sane answer.\r\n\r\nI am using R 4.2.1, with dplyr 1.1.3 and cli 3.6.1.\r\n\r\n---\r\n\r\nBrief description of the problem\r\n\r\n``` r\r\nlibrary(cli)\r\nlibrary(dplyr)\r\n#> \r\n#> Attache Paket: 'dplyr'\r\n#> Die folgenden Objekte sind maskiert von 'package:stats':\r\n#> \r\n#> filter, lag\r\n#> Die folgenden Objekte sind maskiert von 'package:base':\r\n#> \r\n#> intersect, setdiff, setequal, union\r\n\r\nsome_warning <- function() {\r\n cli::cli_warn(\"some warning\")\r\n return(1)\r\n}\r\n\r\ncreate_new_col <- function() {\r\n tibble(col = TRUE) |>\r\n mutate(\r\n new_col = some_warning(),\r\n .by = col\r\n ) |>\r\n pull(new_col)\r\n}\r\n\r\ntibble(group = TRUE) |>\r\n mutate(new_col = create_new_col(), .by = group)\r\n#> Error in vapply(.x, .f, .mold, ..., USE.NAMES = FALSE): Werte müssen die Länge 1 haben,\r\n#> Ergebnis von FUN(X[[1]]) hat aber Länge 0\r\n```\r\n\r\n<sup>Created on 2023-10-10 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\n",
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#> "body": "Extracted from https://github.com/tidyverse/dplyr/issues/6933\r\n\r\nSometimes the function you apply in `across()` returns a tibble who's column names already uniquely identify that function (`TTR::TDI()` or `TTR::MACD()`), so you don't need to name the `list()` as that would be repetitive, and you don't need the `_1` or `_2` you get automatically.\r\n\r\nIt would be nice if you could set `.names = \"{.col}_\"` when `.unpack = TRUE` to handle this special case, but that runs the name uniqueness check too early. It creates two names called `var1_`, which do eventually get resolved _after_ the unpacking is done, and a second name uniqueness check is already done in `df_unpack()`, so I think we can skip the ones in `across_setup()` when `.unpack = TRUE`.\r\n\r\nEasiest way to do this is the probably give `across_setup()` a `name_repair` argument that we can set to `\"minimal\"` when `.unpack = TRUE`.\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\n\r\ndf <- tibble(\r\n var1 = seq(1, 100, 1)\r\n)\r\n\r\nmetric_a <- function(x) {\r\n tibble::tibble(a = x, a_info = x + 1)\r\n}\r\nmetric_b <- function(x) {\r\n tibble::tibble(b = x, b_info = x + 1)\r\n}\r\n\r\n# dont need 1 and 2 here\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n \\(.x) metric_a(.x),\r\n \\(.x) metric_b(.x)\r\n ),\r\n .unpack = TRUE\r\n ))\r\n#> # A tibble: 100 × 5\r\n#> var1 var1_1_a var1_1_a_info var1_2_b var1_2_b_info\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 1 1 2 1 2\r\n#> 2 2 2 3 2 3\r\n#> 3 3 3 4 3 4\r\n#> 4 4 4 5 4 5\r\n#> 5 5 5 6 5 6\r\n#> 6 6 6 7 6 7\r\n#> 7 7 7 8 7 8\r\n#> 8 8 8 9 8 9\r\n#> 9 9 9 10 9 10\r\n#> 10 10 10 11 10 11\r\n#> # ℹ 90 more rows\r\n\r\n# this is also repetitive\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n a = \\(.x) metric_a(.x),\r\n b = \\(.x) metric_b(.x)\r\n ),\r\n .unpack = TRUE\r\n ))\r\n#> # A tibble: 100 × 5\r\n#> var1 var1_a_a var1_a_a_info var1_b_b var1_b_b_info\r\n#> <dbl> <dbl> <dbl> <dbl> <dbl>\r\n#> 1 1 1 2 1 2\r\n#> 2 2 2 3 2 3\r\n#> 3 3 3 4 3 4\r\n#> 4 4 4 5 4 5\r\n#> 5 5 5 6 5 6\r\n#> 6 6 6 7 6 7\r\n#> 7 7 7 8 7 8\r\n#> 8 8 8 9 8 9\r\n#> 9 9 9 10 9 10\r\n#> 10 10 10 11 10 11\r\n#> # ℹ 90 more rows\r\n\r\n# ideally:\r\ndf |>\r\n mutate(across(\r\n var1,\r\n list(\r\n a = \\(.x) metric_a(.x),\r\n b = \\(.x) metric_b(.x)\r\n ),\r\n .names = \"{.col}_\",\r\n .unpack = TRUE\r\n ))\r\n#> Error in `mutate()`:\r\n#> ℹ In argument: `across(...)`.\r\n#> Caused by error in `across()`:\r\n#> ! Names must be unique.\r\n#> ✖ These names are duplicated:\r\n#> * \"var1_\" at locations 1 and 2.\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. ├─dplyr::mutate(...)\r\n#> 2. ├─dplyr:::mutate.data.frame(...)\r\n#> 3. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)\r\n#> 4. │ ├─base::withCallingHandlers(...)\r\n#> 5. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)\r\n#> 6. │ └─mask$eval_all_mutate(quo)\r\n#> 7. │ └─dplyr (local) eval()\r\n#> 8. └─dplyr::across(...)\r\n#> 9. └─dplyr:::across_setup(...)\r\n#> 10. └─vctrs::vec_as_names(...)\r\n#> 11. └─vctrs (local) `<fn>`()\r\n#> 12. └─vctrs:::validate_unique(names = names, arg = arg, call = call)\r\n#> 13. └─vctrs:::stop_names_must_be_unique(names, arg, call = call)\r\n#> 14. └─vctrs:::stop_names(...)\r\n#> 15. └─vctrs:::stop_vctrs(...)\r\n#> 16. └─rlang::abort(message, class = c(class, \"vctrs_error\"), ..., call = call)\r\n```\r\n\r\n<sup>Created on 2023-10-02 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>",
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#> "body": "Is the following example a bug in `near`?\r\n```r\r\n> near(1.1 * 100 * 10^200, 110 * 10^200)\r\n[1] FALSE\r\n```\r\n_Originally posted by @bundfussr in https://github.com/pharmaverse/admiral/issues/2060#issuecomment-1691876301_\r\n\r\nThe same example works as expected with `base::all.equal()`\r\n```r\r\n> all.equal(1.1 * 100 * 10^200, 110 * 10^200)\r\n[1] TRUE\r\n``` ",
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#> "body": "The result of using `between()` in filtering is not equivalent to evaluating a conditional expression when working with ordered factors.\r\n\r\n```{r}\r\n> (x <- data.frame(Visit = c(\"Discharge\", \"Month 3\", \"Month 6\", \"Month 12\", \"Month 20\")))\r\n Visit\r\n1 Discharge\r\n2 Month 3\r\n3 Month 6\r\n4 Month 12\r\n5 Month 20\r\n\r\n> class(x$Visit)\r\n[1] \"character\"\r\n```\r\n\r\nNow let's make it an ordered factor with assumed levels (used later for other operations):\r\n```{r}\r\n> (x$Visit <- factor(x$Visit, levels = c(\"Baseline\", \"Discharge\", \"Month 3\", \"Month 6\", \"Month 12\", \"Month 20\", \"Premature\"), ordered = TRUE))\r\n[1] Discharge Month 3 Month 6 Month 12 Month 20 \r\nLevels: Baseline < Discharge < Month 3 < Month 6 < Month 12 < Month 20 < Premature\r\n```\r\n\r\nWhen filtering using the conjunction of conditions it works\r\n```{r}\r\n> x %>% filter(Visit >= \"Discharge\" & Visit <= \"Month 20\")\r\n Visit\r\n1 Discharge\r\n2 Month 3\r\n3 Month 6\r\n4 Month 12\r\n5 Month 20\r\n```\r\nWhen using the convenient between(), it doesn't\r\n```{r}\r\n> x %>% filter(between(Visit, \"Discharge\", \"Month 20\"))\r\n Visit\r\n1 Discharge\r\n2 Month 12\r\n3 Month 20\r\n```\r\n2 items are missing in the output.\r\n\r\n-----\r\nR version 4.2.0 (2022-04-22 ucrt)\r\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\r\nRunning under: Windows 10 x64 (build 19045)\r\n\r\n> packageVersion(\"dplyr\")\r\n[1] ‘1.1.1’",
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#> "body": "It would be nice if `bind_rows()` would work for database tables.\r\n\r\n``` r\r\nlibrary(dplyr, warn.conflicts = FALSE)\r\nlibrary(purrr)\r\ncon <- DBI::dbConnect(RSQLite::SQLite(), \":memory:\")\r\n\r\n# Create random slices of mtcars\r\ntables <-\r\n 1:5 |>\r\n map(function(n) {\r\n tbl_name <- paste0(\"mtcars_\", n)\r\n copy_to(con, slice_sample(mtcars, n = 3), tbl_name)\r\n\r\n tbl(con, tbl_name)\r\n })\r\nbind_rows(!!!tables)\r\n#> Error in `bind_rows()`:\r\n#> ! Argument 1 must be a data frame or a named atomic vector.\r\n#> Backtrace:\r\n#> ▆\r\n#> 1. └─dplyr::bind_rows(!!!tables)\r\n#> 2. └─rlang::abort(glue(\"Argument {i} must be a data frame or a named atomic vector.\"))\r\n```\r\n\r\n<sup>Created on 2023-08-07 with [reprex v2.0.2](https://reprex.tidyverse.org)<\/sup>\r\n\r\nI know there were issues with the double dispatch and what happens if local tables and database tables are mixed.\r\nBut I think it would be sufficient if either all inputs are local tables or database tables.\r\nThis was also requested again in dbplyr: https://github.com/tidyverse/dbplyr/issues/1342.",
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## Automatic pagination
users <- gh("/users", .limit = 50)
length(users)
#> [1] 50
if (FALSE) {
## Access developer preview of Licenses API (in preview as of 2015-09-24)
gh("/licenses") # used to error code 415
gh("/licenses", .accept = "application/vnd.github.drax-preview+json")
}
if (FALSE) {
## Access Github Enterprise API
## Use GITHUB_API_URL environment variable to change the default.
gh("/user/repos", type = "public", .api_url = "https://github.foobar.edu/api/v3")
}
if (FALSE) {
## Use I() to force body part to be sent as an array, even if length 1
## This works whether assignees has length 1 or > 1
assignees <- "gh_user"
assignees <- c("gh_user1", "gh_user2")
gh("PATCH /repos/OWNER/REPO/issues/1", assignees = I(assignees))
}
if (FALSE) {
## There are two ways to send JSON data. One is that you supply one or
## more objects that will be converted to JSON automatically via
## jsonlite::toJSON(). In this case sometimes you need to use
## jsonlite::unbox() because fromJSON() creates lists from scalar vectors
## by default. The Content-Type header is automatically added in this
## case. For example this request turns on GitHub Pages, using this
## API: https://docs.github.com/v3/repos/pages/#enable-a-pages-site
gh::gh(
"POST /repos/{owner}/{repo}/pages",
owner = "r-lib",
repo = "gh",
source = list(
branch = jsonlite::unbox("gh-pages"),
path = jsonlite::unbox("/")
),
.send_headers = c(Accept = "application/vnd.github.switcheroo-preview+json")
)
## The second way is to handle the JSON encoding manually, and supply it
## as a raw vector in an unnamed argument, and also a Content-Type header:
body <- '{ "source": { "branch": "gh-pages", "path": "/" } }'
gh::gh(
"POST /repos/{owner}/{repo}/pages",
owner = "r-lib",
repo = "gh",
charToRaw(body),
.send_headers = c(
Accept = "application/vnd.github.switcheroo-preview+json",
"Content-Type" = "application/json"
)
)
}
if (FALSE) {
## Pass along a query to the search/code endpoint via the ... argument
x <- gh::gh(
"/search/code",
q = "installation repo:r-lib/gh",
.send_headers = c("X-GitHub-Api-Version" = "2022-11-28")
)
str(x, list.len = 3, give.attr = FALSE)
}