Submitting Author Name: Moritz Hennicke Submitting Author Github Handle: @AAoritz Other Package Authors Github handles: @krausewe Repository: https://github.com/AAoritz/nuts/ Version submitted: 0.0.0.9000 Submission type: Standard Editor: @maelle Reviewers: @nolwenn, @jospueyo

Due date for @nolwenn: 2024-03-01

Due date for @jospueyo: 2024-03-07 Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: nuts
Title: 'nuts': Convert European Regional Data
Version: 0.0.0.9000
Authors@R: c(person("Moritz", "Hennicke", email = "[email protected]", role = c("aut", "cre"),
		   comment = c(ORCID = "0000-0001-6811-1821")),
	     person("Werner", "Krause", email = "[email protected]", role = c("aut"),
		   comment = c(ORCID = "0000-0002-5069-7964")))
Description: Motivated by changing administrative boundaries over time, the 'nuts' package can convert European regional data with NUTS codes between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1, NUTS 2 and NUTS 3). The package uses spatial interpolation based on granular (100mx100m) population and land use data provided by the European Commission's Joint Research Center.  
URL: https://AAoritz.github.io/nuts/
BugReports: https://github.com/AAoritz/nuts/
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Suggests: 
    distill,
    eurostat,
    formatR,
    ggalluvial,
    ggfittext,
    ggplot2,
    ggpubr,
    ggrepel,
    gridExtra,
    kableExtra,
    knitr,
    raster,
    RColorBrewer,
    readr,
    rmarkdown,
    sf,
    terra,
    testthat,
    tidyr
Config/testthat/edition: 3
VignetteBuilder: knitr
Imports:
	crayon,
	dplyr,
	stringr
Depends: 
    R (>= 3.5.0)
LazyData: true

Scope

Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
- [ ] data retrieval
- [ ] data extraction
- [X] data munging
- [ ] data deposition
- [X] data validation and testing
- [ ] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [X] geospatial data
- [ ] text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

Data munging: Linking regional data from different sources at the level of NUTS codes is often complicated by the usage of different versions or varying levels of geographical granularity across sources. The package can be used to harmonize NUTS versions and levels across different data sources using spatial interpolation.

Data validation and testing: The package includes routine tasks to test for the validity and completeness of NUTS codes.

Geospatial data: NUTS codes are the dominant format for European regional data.

Who is the target audience and what are scientific applications of this package?

The target audience are academics, journalists and data scientists interested in European regional data. Users who want to exploit changes within NUTS regions over time face the challenge that administrative boundaries are redrawn over time. The package enables the construction of consistent panel data across NUTS regions and over time through the harmonization of NUTS regions to one common version or level of granularity.

Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

To our knowledge there is currently no package that is targeted at the conversion of NUTS versions using spatial interpolation.

The regions package allows for re-coding of NUTS codes from version to version without spatial interpolation. The package offers some code validation routines, but not an automated detection of the NUTS version used.

(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Yes

If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Explain reasons for any pkgcheck items which your package is unable to pass.

Technical checks

Confirm each of the following by checking the box.

[X] I have read the rOpenSci packaging guide.
[X] I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

[X] does not violate the Terms of Service of any service it interacts with.
[X] has a CRAN and OSI accepted license.
[X] contains a README with instructions for installing the development version.
[X] includes documentation with examples for all functions, created with roxygen2.
[X] contains a vignette with examples of its essential functions and uses.
[X] has a test suite.
[X] has continuous integration, including reporting of test coverage.

Publication options

[X] Do you intend for this package to go on CRAN?

Code of conduct

[X] I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

Jan 26 '24 17:01 AAoritz

Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type @ropensci-review-bot help for help.

Jan 26 '24 17:01 ropensci-review-bot

:rocket:

Editor check started

:wave:

Jan 26 '24 17:01 ropensci-review-bot

Checks for nuts (v0.0.0.9000)

git hash: 815f2a79

:heavy_check_mark: Package name is available
:heavy_check_mark: has a 'codemeta.json' file.
:heavy_multiplication_x: does not have a 'contributing' file.
:heavy_check_mark: uses 'roxygen2'.
:heavy_check_mark: 'DESCRIPTION' has a URL field.
:heavy_check_mark: 'DESCRIPTION' has a BugReports field.
:heavy_multiplication_x: Package has no HTML vignettes
:heavy_check_mark: All functions have examples.
:heavy_check_mark: Package has continuous integration checks.
:heavy_check_mark: Package coverage is 95%.
:heavy_check_mark: R CMD check found no errors.
:heavy_check_mark: R CMD check found no warnings.

Important: All failing checks above must be addressed prior to proceeding

Package License: MIT + file LICENSE

1. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	utils	26
internal	base	25
imports	crayon	NA
imports	dplyr	NA
imports	stringr	NA
suggests	distill	NA
suggests	eurostat	NA
suggests	formatR	NA
suggests	ggalluvial	NA
suggests	ggfittext	NA
suggests	ggplot2	NA
suggests	ggpubr	NA
suggests	ggrepel	NA
suggests	gridExtra	NA
suggests	kableExtra	NA
suggests	knitr	NA
suggests	raster	NA
suggests	RColorBrewer	NA
suggests	readr	NA
suggests	rmarkdown	NA
suggests	sf	NA
suggests	terra	NA
suggests	testthat	NA
suggests	tidyr	NA
linking_to	NA	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

utils

data (26)

base

get (5), paste0 (5), c (4), names (4), attributes (2), by (2), missing (2), list (1)

NOTE: No imported packages appear to have associated function calls; please ensure with author that these 'Imports' are listed appropriately.

2. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

code in R (100% in 7 files) and
2 authors
1 vignette
4 internal data files
3 imported packages
3 exported functions (median 132 lines of code)
3 non-exported functions in R (median 260 lines of code)

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages The following terminology is used:

loc = "Lines of Code"
fn = "function"
exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	7	45.7
files_vignettes	2	85.7
files_tests	4	79.0
loc_R	602	53.3
loc_vignettes	874	88.9
loc_tests	702	81.2
num_vignettes	1	64.8
data_size_total	625034	92.9
data_size_median	156882	93.1
n_fns_r	6	6.6
n_fns_r_exported	3	12.9
n_fns_r_not_exported	3	5.3
n_fns_per_file_r	1	0.2	TRUE
num_params_per_fn	6	79.0
loc_per_fn_r	193	99.1	TRUE
loc_per_fn_r_exp	132	94.8
loc_per_fn_r_not_exp	260	99.5	TRUE
rel_whitespace_R	18	55.4
rel_whitespace_vignettes	29	89.2
rel_whitespace_tests	12	68.9
doclines_per_fn_exp	60	72.8
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	0	0.0	TRUE

2a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

3. `goodpractice` and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
7671209982	pages build and deployment with artifacts-next	success	4aab2b	11	2024-01-26
7671179570	pkgdown	success	815f2a	14	2024-01-26
7671179564	R-CMD-check	success	815f2a	8	2024-01-26
7671179577	test-coverage	success	815f2a	12	2024-01-26

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fail:

no_import_package_as_a_whole

Test coverage with covr

Package coverage: 94.97

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
convert_nuts_level	24
convert_nuts_version	21
classify_nuts	19

Static code analyses with lintr

lintr found the following 184 potential issues:

message	number of times
Avoid library() and require() calls in packages	22
Lines should not be more than 80 characters.	116
Use <-, not =, for assignment.	46

Package Versions

package	version
pkgstats	0.1.3.9
pkgcheck	0.1.2.11

Editor-in-Chief Instructions:

Processing may not proceed until the items marked with :heavy_multiplication_x: have been resolved.

Jan 26 '24 17:01 ropensci-review-bot

Hi rOpenSci Team and @ropensci-review-bot!

We added a contributing file with this commit
The package uses an HTML vignette based on distill. The bot prefers in check-vignette.R rmarkdown HTML files. Should we switch to rmarkdown?

Thank your for your time and consideration!

Jan 26 '24 19:01 AAoritz

@AAoritz Sorry about the inconvenience there. We've updated our system to detect distill vignettes, so your package will now pass those tests. (The server might take a few days to incorporate those updates; please be paitent.)

Jan 29 '24 09:01 mpadge

Wow, thanks @mpadge! Looking forward to the review process!

Jan 29 '24 09:01 AAoritz

Yep, thanks @mpadge. Will wait a bit of sending another check request to let those changes work through.

@AAoritz I think this looks good and is a fit for rOpenSci. I will start working on finding a handling editor for your submission!

Jan 29 '24 18:01 jhollist

Great news @jhollist! Thank you!

Jan 30 '24 12:01 AAoritz

@ropensci-review-bot check package

Jan 30 '24 17:01 jhollist

Thanks, about to send the query.

Jan 30 '24 17:01 ropensci-review-bot

:rocket:

Editor check started

:wave:

Jan 30 '24 17:01 ropensci-review-bot

Checks for nuts (v0.0.0.9000)

git hash: cf771ced

:heavy_check_mark: Package name is available
:heavy_check_mark: has a 'codemeta.json' file.
:heavy_check_mark: has a 'contributing' file.
:heavy_check_mark: uses 'roxygen2'.
:heavy_check_mark: 'DESCRIPTION' has a URL field.
:heavy_check_mark: 'DESCRIPTION' has a BugReports field.
:heavy_check_mark: Package has at least one HTML vignette
:heavy_check_mark: All functions have examples.
:heavy_check_mark: Package has continuous integration checks.
:heavy_check_mark: Package coverage is 95%.
:heavy_check_mark: R CMD check found no errors.
:heavy_check_mark: R CMD check found no warnings.

Package License: MIT + file LICENSE

1. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	utils	26
internal	base	25
imports	crayon	NA
imports	dplyr	NA
imports	stringr	NA
suggests	distill	NA
suggests	eurostat	NA
suggests	formatR	NA
suggests	ggalluvial	NA
suggests	ggfittext	NA
suggests	ggplot2	NA
suggests	ggpubr	NA
suggests	ggrepel	NA
suggests	gridExtra	NA
suggests	kableExtra	NA
suggests	knitr	NA
suggests	raster	NA
suggests	RColorBrewer	NA
suggests	readr	NA
suggests	rmarkdown	NA
suggests	sf	NA
suggests	terra	NA
suggests	testthat	NA
suggests	tidyr	NA
linking_to	NA	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

utils

data (26)

base

get (5), paste0 (5), c (4), names (4), attributes (2), by (2), missing (2), list (1)

NOTE: No imported packages appear to have associated function calls; please ensure with author that these 'Imports' are listed appropriately.

2. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

code in R (100% in 7 files) and
2 authors
1 vignette
4 internal data files
3 imported packages
3 exported functions (median 132 lines of code)
3 non-exported functions in R (median 260 lines of code)

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages The following terminology is used:

loc = "Lines of Code"
fn = "function"
exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	7	45.7
files_vignettes	2	85.7
files_tests	4	79.0
loc_R	602	53.3
loc_vignettes	874	88.9
loc_tests	702	81.2
num_vignettes	1	64.8
data_size_total	625034	92.9
data_size_median	156882	93.1
n_fns_r	6	6.6
n_fns_r_exported	3	12.9
n_fns_r_not_exported	3	5.3
n_fns_per_file_r	1	0.2	TRUE
num_params_per_fn	6	79.0
loc_per_fn_r	193	99.1	TRUE
loc_per_fn_r_exp	132	94.8
loc_per_fn_r_not_exp	260	99.5	TRUE
rel_whitespace_R	18	55.4
rel_whitespace_vignettes	29	89.2
rel_whitespace_tests	12	68.9
doclines_per_fn_exp	60	72.8
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	0	0.0	TRUE

2a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

3. `goodpractice` and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
7694130869	pages build and deployment with artifacts-next	success	38d719	13	2024-01-29
7694097562	pkgdown	success	cf771c	16	2024-01-29
7694097559	R-CMD-check	success	cf771c	10	2024-01-29
7694097589	test-coverage	success	cf771c	14	2024-01-29

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fail:

no_import_package_as_a_whole

Test coverage with covr

Package coverage: 94.97

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
convert_nuts_level	24
convert_nuts_version	21
classify_nuts	19

Static code analyses with lintr

lintr found the following 184 potential issues:

message	number of times
Avoid library() and require() calls in packages	22
Lines should not be more than 80 characters.	116
Use <-, not =, for assignment.	46

Package Versions

package	version
pkgstats	0.1.3.9
pkgcheck	0.1.2.13

Editor-in-Chief Instructions:

This package is in top shape and may be passed on to a handling editor

Jan 30 '24 19:01 ropensci-review-bot

@AAoritz looking good! Assigning editor in just a few.

Jan 30 '24 19:01 jhollist

@ropensci-review-bot assign @maelle as editor

Jan 30 '24 19:01 jhollist

Assigned! @maelle is now the editor

Jan 30 '24 19:01 ropensci-review-bot

Editor checks:

[x] Documentation: The package has sufficient documentation available online (README, pkgdown docs) to allow for an assessment of functionality and scope without installing the package. In particular,
- [x] Is the case for the package well made?
- [ ] Is the reference index page clear (grouped by topic if necessary)?
- [x] Are vignettes readable, sufficiently detailed and not just perfunctory?
[x] Fit: The package meets criteria for fit and overlap.
[ ] Installation instructions: Are installation instructions clear enough for human users?
[x] Tests: If the package has some interactivity / HTTP / plot production etc. are the tests using state-of-the-art tooling?
[ ] Contributing information: Is the documentation for contribution clear enough e.g. tokens for tests, playgrounds?
[x] License: The package has a CRAN or OSI accepted license.
[x] Project management: Are the issue and PR trackers in a good shape, e.g. are there outstanding bugs, is it clear when feature requests are meant to be tackled?

Editor comments

Many thanks for your submission @AAoritz & @krausewe! Useful package! I know of the French equivalent to nuts, COGjugaison, so was vaguely aware of the idea https://antuki.github.io/COGugaison/ :smile_cat:

I have a few comments that I'd like to resolve or discuss before I proceed to the reviewer search.

Contributing guide

In the CONTRIBUTING.md file could you please remove the tidyverse specific bits such as " For more detailed info about contributing to this, and other tidyverse packages,"?
I was surprised to see optional dependencies before Imports in DESCRIPTION. Would you mind running desc::desc_normalize() and see whether you like the result?

Docs

Instead of remotes in the README you could recommend pak, pak::pak("AAoritz/nuts") but in any case I don't think the force = TRUE argument is warranted, is it?
Since there is only one vignette I'd recommend naming it "nuts.Rmd" so that it is automatically added in the pkgdown website navbar as "Get started". See https://pkgdown.r-lib.org/reference/build_articles.html#get-started
Your images have alternative text but that text is not complete, it does not describe the image so that a screenreader user would miss a lot. https://www.w3.org/WAI/tutorials/images/informative/
I was surprised by the short pipeline in https://github.com/AAoritz/nuts/blob/cf771ced02a20b226b56e6908959044ca62804e4/vignettes/nuts-vignette.Rmd#L247, could it avoid using the pipe, or if it does, could filter( go on a separate line? I feel it's an unusual pattern (but I might be wrong). I see this pattern of one-line pipelines in the tests too so it might be a personal preference! It's more noticeable/important in the user facing code but I won't impose my personal style.
The reference index is short but it might make sense to group the functions then the datasets. https://pkgdown.r-lib.org/reference/build_reference.html#reference-index

User interface

The function output of https://aaoritz.github.io/nuts/reference/classify_nuts.html#value is a list where you identify parts via their index. Could you add names to the output? Something like

output <- list(
  data = data, 
  versions_data = data_all_versions,  
  missing_data = data_missing_nuts
)

so that then the user could use output [["missing_data"]] instead of output[[3]] which is less readable.

Regarding the messages,
- I see you use crayon for information but stop() for error messages. The crayon package has been superseeded by cli (see https://cli.r-lib.org/ and https://blog.r-hub.io/2023/11/30/cliff-notes-about-cli/), according to https://cran.r-project.org/web/packages/crayon/index.html. You could keep using crayon (it's still supported) but it might make sense switching, as cli provides really nice features. Code like

stop("Input 'nuts_code' must be provided as a string.")

could become

cli::cli_abort("Input {.arg nuts_code} must be provided as a string, not {.obj_type_friendly {nuts_code}}.")

(note that this function also requires importing rlang in DESCRIPTION)

and info messages could use cli_alert_ functions https://cli.r-lib.org/reference/cli_alert.html

Most importantly, at the moment there is no way for the user to turn off messages. This is guidance we've just added to the development version of the dev guide so pretty new but I would still like this to be implemented as it might improve the quality of life of the users (including you, for instance in tests). See https://devdevguide.netlify.app/pkg_building#console-messages, https://blog.r-hub.io/2023/11/30/cliff-notes-about-cli/#how-to-make-cli-quiet-or-not if you use cli, and, hum, a draft blog post @mpadge and I are working on https://github.com/ropensci/roweb3/pull/717 Based on the interface tooling you end up selecting I am happy to provide tips about silencing if needed. Thanks for your understanding. :sweat_smile:

Code

Why does the package import whole packages rather than individual functions? Furthermore the imports should happen only once so they could happen in the package-level manual page source. You can create it with https://usethis.r-lib.org/reference/use_package_doc.html whose docs say "This .R file is also a good place for roxygen directives that apply to the whole package (vs. a specific function), such as global namespace tags like @importFrom."
In lines such as https://github.com/AAoritz/nuts/blob/cf771ced02a20b226b56e6908959044ca62804e4/R/convert_nuts_version.R#L128 the code might benefit from the use of "explaining variables", for instance (with a better name probably, again an example :sweat_smile: )

not_enough_codes <- (length(data$from_code[check_nuts_codes]) < length(data$from_code) &&
               length(data$from_code[check_nuts_codes]) > 0)
if  (not_enough_codes)

(oh and the use of && instead of & see https://lintr.r-lib.org/reference/vector_logic_linter.html)

Tests

You do not need data() to load package data in tests.
I'd recommend not having top-level code in tests which means no code outside of test_that(. This makes tests more self contained. So in a helper file, tests/testthat/helper-test-data.R for instance you'd have the function

manure_indic_DE_2003 <- function() {
manure %>%
  filter(nchar(geo) == 4) %>%
  filter(indic_ag == "I07A_EQ_Y") %>%
  select(-indic_ag) %>%
  filter(grepl("^DE", geo)) %>%
  filter(time == 2003) %>%
  select(-time)
}

and in tests/testthat/test-classify_nuts.R you'd have

test_that("Needs geo var 1", {
  expect_error(
    manure_indic_DE_2003() %>% classify_nuts(nuts_code = NULL),
    "Input 'nuts_code' cannot be NULL."
  )
})

test_that("Needs geo var 2", {
  expect_error(manure_indic_DE_2003() %>% classify_nuts())
})

test_that("nuts_code not valid", {
  expect_error(
    manure_indic_DE_2003() %>% classify_nuts(nuts_code = 1),
    "Input 'nuts_code' must be provided as a string."
  )
})

See https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ and https://r-pkgs.org/testing-design.html + https://r-pkgs.org/testing-advanced.html

Instead of using set.seed(), in tests please use the withr version: https://withr.r-lib.org/reference/with_seed.html

Happy to answer any question and to discuss! Thanks again for submitting your package!

Jan 31 '24 12:01 maelle

Hi @maelle ! Many thanks for these super useful checks and comments. @krausewe and I start looking into them now and we will get back to you soon.

Jan 31 '24 16:01 AAoritz

Hi @maelle!

We have addressed the very helpful remarks & recommendations.

Please find below the list of commits.

We are looking forward to the next steps!

Contributing guide

Remove tidyverse specific points in CONTRIBUTING: https://github.com/AAoritz/nuts/commit/f5c4a5a4049c52c9f34dd253d99a0eb2515af164
Normalize DESCRIPTION: https://github.com/AAoritz/nuts/commit/b80c8db0ad253a631b6b06f8fa20a61523c0d218

Docs

Change install to pak in README: https://github.com/AAoritz/nuts/commit/51c2e44addb5b5052421b967cb9ddc11837fb6af
Rename vignette to nuts.Rmd: https://github.com/AAoritz/nuts/commit/3fe96c87ece74a6e28d35f350302d282523238e8
Include alt-text of images in vignette: https://github.com/AAoritz/nuts/commit/51f1cb3c67b1e85acaef84dc3937620c963a3985
Add line breaks for one-line pipelines: https://github.com/AAoritz/nuts/commit/baf01ff9227695fa60e95cc6ee61c7d6c3189202
Group functions and data pkgdown: https://github.com/AAoritz/nuts/commit/f11a08070d0c1356339936c742165cfe6a551c34

User interface

Simplify names of output list of classify: https://github.com/AAoritz/nuts/commit/a5bbea99ace46bd32be7bc0628d239ebe0f319f4
Use cli messages: https://github.com/AAoritz/nuts/commit/8cb9be6e0fa526f1fc43cbaeb8ab5b3bec89c8e8
Silencing messages: https://github.com/AAoritz/nuts/commit/9a93ff174389a922ca7bae39daa039d93eb7454b

Code

Import functions as opposed to entire packages: https://github.com/AAoritz/nuts/commit/d2744d97cd43dd7a45c52416640633cfd604d34f
Use explaining variables and double &&: https://github.com/AAoritz/nuts/commit/4bc0303f8b2be824ac554d31cb772ac27d242140

Tests

Don't load data(): https://github.com/AAoritz/nuts/commit/e2ba58f62169b8c84b33817faf305dfc6aeb6c09
Include helper file that prepares data: https://github.com/AAoritz/nuts/commit/e2ba58f62169b8c84b33817faf305dfc6aeb6c09
Replace set.seed() with withr: https://github.com/AAoritz/nuts/commit/0d9e3ad4084780c69179f52afd21c30b1eedba6c

Feb 08 '24 07:02 krausewe

Awesome, thank you! I'll now look for reviewers.

Note that @mpadge's and my tech note is now published: https://ropensci.org/blog/2024/02/06/verbosity-control-packages/

Feb 08 '24 14:02 maelle

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Feb 09 '24 14:02 ropensci-review-bot

Hi @maelle! We are a bit lost with implementing verbosity control with local_options. This example from your blog does not seem to work with cli:

> pkg_message <- function(...) {
+   is_verbose_mode <- (getOption("mypackage.verbose", "quiet") == "verbose")
+   if (is_verbose_mode) {
+     # Options local to this function only; reset on exit!
+     rlang::local_options(rlib_message_verbosity = "verbose")
+   }
+   rlang::inform(...)
+ }
> pkg_message("normal message")
normal message
> rlang::local_options(rlib_message_verbosity = "quiet")
Setting global deferred event(s).
i These will be run:
  * Automatically, when the R session ends.
  * On demand, if you call `withr::deferred_run()`.
i Use `withr::deferred_clear()` to clear them without executing.
> pkg_message("suppressed message")
> rlang::local_options(mypackage.verbose = "verbose")
> pkg_message("reawakened message")
reawakened message


> withr::deferred_run()
Ran 2/2 deferred expressions
> pkg_message <- function(...) {
+   is_verbose_mode <- (getOption("mypackage.verbose", "quiet") == "verbose")
+   if (is_verbose_mode) {
+     # Options local to this function only; reset on exit!
+     rlang::local_options(rlib_message_verbosity = "verbose")
+   }
+   cli::cli_h1(...)
+ }
> pkg_message("normal message")

── normal message ───────────────────────────────────────────────────────────────────────────────────────────────
> rlang::local_options(rlib_message_verbosity = "quiet")
Setting global deferred event(s).
i These will be run:
  * Automatically, when the R session ends.
  * On demand, if you call `withr::deferred_run()`.
i Use `withr::deferred_clear()` to clear them without executing.
> pkg_message("suppressed message")

── suppressed message ───────────────────────────────────────────────────────────────────────────────────────────
> rlang::local_options(mypackage.verbose = "verbose")
> pkg_message("reawakened message")

── reawakened message ───────────────────────────────────────────────────────────────────────────────────────────
>

Feb 12 '24 10:02 AAoritz

Right, it seems specific to rlang (cc @mpadge).

How about something simpler like

pkg_message <- function(...) {
  is_verbose_mode <- (getOption("mypackage.verbose", "quiet") == "verbose")
  if (is_verbose_mode) {
    cli::cli_alert(...)
  }
  
}

withr::with_options(list("mypackage.verbose" = "quiet"), {
  pkg_message("pof")
})

withr::with_options(list("mypackage.verbose" = "verbose"), {
  pkg_message("pof")
})
#> → pof

^{Created on 2024-02-13 with reprex v2.1.0}

:thinking:

Feb 13 '24 09:02 maelle

@AAoritz The options we wrote about in the blog post only apply to the cli_inform(), cli_warn(), and cli_abort() functions, not to any other cli functions. If you want to use others such as cli_h<N>(), you'd need to further adapt the custom handler to not call that at all if verbose == "quiet".

Feb 14 '24 14:02 mpadge

Thank you @maelle and @mpadge! I wrapped our cli message as suggested in https://github.com/AAoritz/nuts/commit/47ee441ea6ea98b30326157b803f81dba5557b42 by @maelle. Here is the result:

rlang::local_options(nuts.verbose = "quiet")
df <- patents %>%
  filter(unit == "NR", nchar(geo) == 4, time == 2012) %>%
  filter(grepl("^DE", geo)) %>%
  classify_nuts(data = ., nuts_code = "geo")

withr::deferred_run()
#> Ran 1/1 deferred expressions
rlang::local_options(nuts.verbose = "verbose")
df <- patents %>%
  filter(unit == "NR", nchar(geo) == 4, time == 2012) %>%
  filter(grepl("^DE", geo)) %>%
  classify_nuts(data = ., nuts_code = "geo")
#> 
#> ── Classifying version of NUTS codes ───────────────────────────────────────────
#> Within groups defined by country:
#> ! These NUTS codes cannot be identified or classified: DEXX and DEZZ.
#> ✔ Unique NUTS version classified.
#> ✔ No missing NUTS codes.

withr::deferred_run()
#> Ran 2/2 deferred expressions
rlang::local_options(rlib_message_verbosity = "quiet")
df <- patents %>%
  filter(unit == "NR", nchar(geo) == 4, time == 2012) %>%
  filter(grepl("^DE", geo)) %>%
  classify_nuts(data = ., nuts_code = "geo")

^{Created on 2024-02-14 with reprex v2.0.2}

How would you recommend to document this option? Below the examples in the function level help/documentation?

Feb 14 '24 15:02 AAoritz

@AAoritz Would you please be so kind as to ask that question on our discussion forum? The issue of where to document verbosity control is important, and would be good to have in that general context, rather than here.

Feb 14 '24 15:02 mpadge

software-review software-review copied to clipboard

'nuts': Convert European Regional Data

Scope

Technical checks

Publication options

Code of conduct

Checks for nuts (v0.0.0.9000)

1. Package Dependencies

2. Statistical Properties

2a. Network visualisation

3. goodpractice and other checks

3a. Continuous Integration Badges

3b. goodpractice results

R CMD check with rcmdcheck

Test coverage with covr

Cyclocomplexity with cyclocomp

Static code analyses with lintr

Editor-in-Chief Instructions:

Checks for nuts (v0.0.0.9000)

1. Package Dependencies

2. Statistical Properties

2a. Network visualisation

3. goodpractice and other checks

3a. Continuous Integration Badges

3b. goodpractice results

R CMD check with rcmdcheck

Test coverage with covr

Cyclocomplexity with cyclocomp

Static code analyses with lintr

Editor-in-Chief Instructions:

Editor checks:

Editor comments

Contributing guide

Docs

User interface

Code

Tests

Contributing guide

Docs

User interface

Code

Tests

software-review
software-review copied to clipboard

3. `goodpractice` and other checks

3b. `goodpractice` results

`R CMD check` with rcmdcheck

3. `goodpractice` and other checks

3b. `goodpractice` results

`R CMD check` with rcmdcheck