software-review dataset: Create Data Frames that are Easier to Exchange and Reuse

Submitting Author Name: Daniel Antal Submitting Author Github Handle: @antaldaniel Repository: https://github.com/dataobservatory-eu/dataset/ Version submitted: 0.1.7 Submission type: Standard Editor: @annakrystalli Reviewers: @msperlin, @romanflury

Due date for @msperlin: 2022-09-19

Due date for @romanflury: 2022-09-21

Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: dataset
Title: Create Data Frames that are Easier to Exchange and Reuse
Date: 2022-08-19
Version: 0.1.7.3
Authors@R: 
    person(given = "Daniel", family = "Antal", 
           email = "[email protected]", 
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-7513-6760")
           )
Description: The aim of the 'dataset' package is to make tidy datasets easier to release, 
    exchange and reuse. It organizes and formats data frame 'R' objects into well-referenced, 
    well-described, interoperable datasets into release and reuse ready form. A subjective 
    interpretation of the  W3C  DataSet recommendation and the datacube model  <https://www.w3.org/TR/vocab-data-cube/>, 
    which is also used in the global Statistical Data and Metadata eXchange standards, 
    the application of the connected Dublin Core <https://www.dublincore.org/specifications/dublin-core/dcmi-terms/> 
    and DataCite <https://support.datacite.org/docs/datacite-metadata-schema-44/> standards 
    preferred by European open science repositories to improve the findability, accessibility,
    interoperability and reusability of the datasets.
License: GPL (>= 3)
URL: https://github.com/dataobservatory-eu/dataset
BugReports: https://github.com/dataobservatory-eu/dataset/issues
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
Depends: 
    R (>= 2.10)
LazyData: true
Imports: 
    assertthat,
    ISOcodes,
    utils
Suggests: 
    covr,
    declared,
    dplyr,
    eurostat,
    here,
    kableExtra,
    knitr,
    rdflib,
    readxl,
    rmarkdown,
    spelling,
    statcodelists,
    testthat (>= 3.0.0),
    tidyr
VignetteBuilder: knitr
Config/testthat/edition: 3
Language: en-US

You can find the package website on dataset.dataobservatory.eu. The article Motivation: Make Tidy Datasets Easier to Release Exchange and Reuse will eventually be condensed into a JOSS paper. It has a major development dilemma.

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
- [ ] data munging
- [x ] data deposition
- [ ] data validation and testing
- [x] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences): Open science repositories and analyst comupters are full with datasets that have no provenance, structural or referential data. We believe that whenever possible, metadata should be machine-recorded when possible, and should not be detached from an R object.
There are several R packages that have overalapping goals or functionality to dataset, but they use a different philosophy. When exporting to different files, they should be written as exported, but no sooner, and preferably into the file that contains the data.
Who is the target audience and what are scientific applications of this package?

This package is intended to give a common foundation to the rOpenGov reproducible research packages. It mainly serves communities that want to reuse statistical data (using the SDMX statistical (meta)data exchange sources, like Eurostat, IMF, World Bank, OECD...) or release new datasets from primary social sciences data that can be integrated into an SDMX compatible API or placed on a knowledge graph. Our main aim is to provide a clear publication workflow to the European open science repository Zenodo, and clear serialization strategies to RDF application.

Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? The dataspice package aims to create well-defined and referenced datasets, but follows a different schema and a different publication strategy. The dataset package follows the more restrictive W3C/SDMX "DataSet" definition within the datacube model, which is better suited to synchronize with statistical data sources. Unlike dataset, it uses a manual metadata entry from CSV files. (See the documentation of the dataspice package.)

The dataset package aims for a higher level of reproducibality, and does not detach the metadata from the R object's attributes (it is aimed to be used in other reproducible research pacakges that will directly record provenance and other transactional metadata into the attributes.) We aim to bind together dataspice and dataset by creating export functions to csv files that contain the same metadata that dataspice records. Generally, dataspice seems to be better suited to raw, observational data, while dataset for statistically processed data.

The intended use of dataset is to start correctly record referential, structural and provenance metadata retrieved by various reproducible science packages that interact with statistical data (such as the rOpenGov packages eurostat and iotables, or the oecd package.

Neither dataset or dataspice are very suitable of or documenting social sciences survey data, which are usually held in datasets. Our aim is to connect dataset, declared and DDIwR to create such datasets with DDI codebook metadata. They will create a stable new foundation of the retroharmonize package to create new, well-documented and harmonized statistical datasets from the observational datasets of social sciences surveys.

The zen4R package provides reproducible export functionality to the zenodo open science repository. Interacting with zen4R may be intimidating for the casual R user as it uses R6 classes. Our aim to provide an export function that completely wraps the workings of zen4R when releasing the dataset.

In our experience, while the tidy data standards make reuse more efficient by eliminating unnecessary data processing steps before analysis or placement in a relational database, the application of DataSet definition and the datacube model with the information science metadata standards make reuse more efficient with exchanging and combining the data with other data in different datasets.

(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.
[ ] has continuous integration, including reporting of test coverage.

Publication options

[x ] Do you intend for this package to go on CRAN? -> Yes, I started the CRAN publication process, but opted to stop and get feedback from rOpenSic first
[ ] Do you intend for this package to go on Bioconductor? -> Don't know.
[ ] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

[ ] The package is novel and will be of interest to the broad readership of the journal.
[ ] The manuscript describing the package is no longer than 3000 words.
[ ] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see MEE's Policy on Publishing Code)
(Scope: Do consider MEE's Aims and Scope for your manuscript. We make no guarantee that your manuscript will be within MEE scope.)
(Although not required, we strongly recommend having a full manuscript prepared when you submit here.)
(Please do not submit your package separately to Methods in Ecology and Evolution)

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.

Aug 15 '22 11:08 antaldaniel

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

Aug 15 '22 11:08 ropensci-review-bot

:rocket:

The following problem was found in your submission template:

URL = [https://repourl] is not valid The package could not be checked because of problems with the URL. Editors: Please ensure these problems are rectified, and then call @ropensci-review-bot check package.

:wave:

Aug 15 '22 11:08 ropensci-review-bot

Hi, @antaldaniel, could you please fix the repo URL by providing a link to the package’s repository, please? 🙏

Aug 15 '22 11:08 adamhsparks

@adamhsparks Apologies for the original issue problem, I hope all is fine now. I added both the github repo and the package website url

Aug 15 '22 14:08 antaldaniel

@antaldaniel Then you can start the checks yourself by calling @ropensci-review-bot check package

Aug 15 '22 15:08 mpadge

@ropensci-review-bot check package

Aug 15 '22 19:08 antaldaniel

Thanks, about to send the query.

Aug 15 '22 19:08 ropensci-review-bot

:rocket:

Editor check started

:wave:

Aug 15 '22 19:08 ropensci-review-bot

Checks for dataset (v0.1.7)

git hash: 2eb439b5

:heavy_check_mark: Package name is available
:heavy_multiplication_x: does not have 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_check_mark: Package has at least one HTML vignette
:heavy_multiplication_x: These functions do not have examples: [attributes_measures].
:heavy_multiplication_x: Function names are duplicated in other packages
:heavy_multiplication_x: Package has no continuous integration checks.
:heavy_multiplication_x: Package coverage is 67.8% (should be at least 75%).
: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: GPL (>= 3)

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	base	159
internal	dataset	79
internal	stats	4
imports	utils	4
imports	rlang	1
imports	assertthat	NA
imports	ISOcodes	NA
suggests	declared	NA
suggests	dplyr	NA
suggests	eurostat	NA
suggests	here	NA
suggests	kableExtra	NA
suggests	knitr	NA
suggests	rdflib	NA
suggests	readxl	NA
suggests	rmarkdown	NA
suggests	spelling	NA
suggests	statcodelists	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.

base

names (26), data.frame (14), class (12), paste (9), rep (7), sapply (7), unlist (6), which (6), attr (5), lapply (5), length (5), ncol (5), subset (4), as.character (3), attributes (3), c (3), logical (3), seq_along (3), vapply (3), as.data.frame (2), as.numeric (2), cbind (2), file (2), inherits (2), matrix (2), nrow (2), round (2), args (1), date (1), deparse (1), for (1), gsub (1), ifelse (1), is.null (1), paste0 (1), rbind (1), tolower (1), union (1), unique (1), url (1), UseMethod (1)

dataset

dimensions (6), attributes_measures (5), measures (5), all_unique (3), dataset_title (3), related_item (3), creator (2), datacite (2), dataset (2), dataset_source (2), description (2), geolocation (2), identifier (2), language (2), metadata_header (2), publication_year (2), publisher (2), related_item_identifier (2), resource_type (2), add_date (1), add_relitem (1), arg.names (1), attributes_names (1), bibentry_dataset (1), datacite_add (1), dataset_download (1), dataset_download_csv (1), dataset_export (1), dataset_export_csv (1), dataset_local_id (1), dataset_title_create (1), dataset_uri (1), dimensions_names (1), document_package_used (1), dot.names (1), dublincore (1), dublincore_add (1), extract_year (1), is.dataset (1), measures_names (1), print (1), print.dataset (1), resource_type_general (1), rights (1), subject (1), time_var_guess (1), version (1)

stats

df (2), time (2)

utils

citation (1), object.size (1), read.csv (1), sessionInfo (1)

rlang

get_expr (1)

NOTE: Some imported packages appear to have no 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 26 files) and
1 authors
7 vignettes
no internal data file
4 imported packages
56 exported functions (median 10 lines of code)
82 non-exported functions in R (median 15 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	26	87.0
files_vignettes	7	98.5
files_tests	27	97.6
loc_R	1000	68.2
loc_vignettes	676	84.7
loc_tests	371	68.8
num_vignettes	7	99.2	TRUE
n_fns_r	138	83.6
n_fns_r_exported	56	89.5
n_fns_r_not_exported	82	79.7
n_fns_per_file_r	3	55.0
num_params_per_fn	2	11.9
loc_per_fn_r	15	46.1
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	15	49.5
rel_whitespace_R	27	78.3
rel_whitespace_vignettes	36	88.3
rel_whitespace_tests	25	70.7
doclines_per_fn_exp	39	48.6
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	103	79.7

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)

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fail:

no_description_date

Test coverage with covr

Package coverage: 67.81

The following files are not completely covered by tests:

file	coverage
R/creator.R	64.29%
R/datacite_attributes.R	0%
R/datacite.R	46.88%
R/dataset_uri.R	0%
R/dataset.R	48.36%
R/document_package_used.R	0%
R/dublincore.R	67.74%
R/publication_year.R	55.56%
R/related_item.R	66.67%

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
datacite_add	24
dublincore_add	23

Static code analyses with lintr

lintr found the following 383 potential issues:

message	number of times
Avoid 1:ncol(...) expressions, use seq_len.	4
Avoid library() and require() calls in packages	20
Avoid using sapply, consider vapply instead, that's type safe	4
Lines should not be more than 80 characters.	352
Use <-, not =, for assignment.	3

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 10 function names are duplicated in other packages:

- dataset from assemblerr, febr, robis
- description from dataMaid, dataPreparation, dataReporter, dcmodify, memisc, metaboData, PerseusR, ritis, rmutil, rsyncrosim, stream, synchronicity, timeSeries, tis, validate
- dimensions from gdalcubes, openeo, sp, tiledb
- identifier from Ramble
- is.dataset from crunch
- language from sylly, wakefield
- measures from greybox, mlr3measures, tsibble
- size from acrt, BaseSet, container, crmPack, CVXR, datastructures, deal, disto, easyVerification, EFA.MRFA, flifo, gdalcubes, gWidgets2, hrt, iemisc, InDisc, kernlab, matlab2r, multiverse, optimbase, PopED, pracma, ramify, rEMM, rmonad, simplegraph, siren, tcltk2, UComp, unival, vampyr
- subject from DGM, emayili, gmailr, sendgridr
- version from BiocManager, garma, geoknife, mice, R6DS, rerddap, rsyncrosim, shiny.info, SMFilter

Package Versions

package	version
pkgstats	0.1.1.20
pkgcheck	0.1.0.3

Editor-in-Chief Instructions:

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

Aug 16 '22 08:08 ropensci-review-bot

Hi again, @antaldaniel. If you could please address the issues that the bot flagged with the ✖️, then I can proceed with your submission.

Aug 16 '22 23:08 adamhsparks

Hi @adamhsparks I hope I managed to add these things, with the following exception.

✔️does not have a 'codemeta.json' file -> added with codematar. ✔️does not have a 'contributing' file -> added CONTRIBUTING.md ✔️ These functions do not have examples: [attributes_measures]. -> added ✖️ Function names are duplicated in other packages

I tried to avoid duplications while keeping in mind rOpenSci duplication guildelines, and at this point, I do not see which are the dupblications and if there is any sensible way to resolve them.

Your guidelines state "Avoid function name conflicts with base packages or other popular ones (e.g. ggplot2, dplyr, magrittr, data.table)" The package currently has no name conflict with any packages that I was thinking of to be used together, and I do not know how to test for this. (Apolgoies if this is somewhere in the 1.3 Package API)

✔️ Package has no continuous integration checks -> added ✖️ Package coverage is 67.8% (should be at least 75%)

I do not see a sensible way to achieve 75%+ codecov coverage with a metadata package that is in an early development page, still has development questions open (see Motivation: Make Tidy Datasets Easier to Release Exchange and Reuse, hence the submission here before the first CRAN release). For example, in the target category, other metadata management pacakges like codemetar has a 42% coverage, EML has 65%, both below the current coverage before the first release of dataset.

Aug 17 '22 10:08 antaldaniel

@antaldaniel You may indeed ignore the "Function names are duplicated in other packages." That will soon be changed from a failing check (:heavy_multiplication_x:) to an advisory note only. Sorry for any confusion there. @adamhsparks will comment further on the code coverage.

Aug 17 '22 10:08 mpadge

@mpadge I do not seem to find the output where this informaiton is coming from, but I think that it is nevertheless a very useful reminder, and it would be good to see what conflicts your bot has found. Again, apologies if I ask the obvious, but where can I check what duplicates were flagged by your bot?

Aug 17 '22 15:08 antaldaniel

It's in the check results. Under "4. Other Checks", you'll see a "Details of other checks (click to open)". You can also generate those yourself by running:

library(pkgcheck)
checks <- pkgcheck("/<path>/<to>/<dataset-pkg>")
checks_md <- checks_to_markdown(checks, render = TRUE)

That will automatically open a HTML-rendered version of the checks, just like the above. You can use that repeatedly as you work through the issues highlighted above.

Aug 17 '22 15:08 mpadge

@mpadge Oh, really, sorry for asking the obvious.

I would like to comment here on the issue then in substance. The main development question of the package, which aims to make R objects standard datasets (as defined by W3C and SDMX), is to add structural and referential metadata, is if the best way to do this is to create an s3 object or not (see the dilemma here.)

In the current stage, it is a pseudo object inherited from data.frame, but it can be seen also as a utility to any data.frame, tibble, and data.table (or similar tabular format) R objects. The functions, which have duplicates in other packages, are following a very simple naming convention. I think that these is the cleanest API interface that I can think of, for example, the

subject() gets the metadata attribute Subject and the subject<-() sets it. As DataCite, Dublin Core and schema.org has dozens of potential attributes, to me the easiest is to use in a slightly modified form the name of the attribute to set/get its value.

All these functions are lowercase to manipulate a camelCase standard attribute. Except for the SDMX attribute 'attribute', which would create a conflict with the base R 'attributes()' function.

Aug 17 '22 16:08 antaldaniel

Hi @antaldaniel, I can understand the difficulty in writing tests for such a non-standard package. But I've had a look at covr::report() for "dataobservatory-eu/dataset". I think that there is still low-hanging fruit here that can be covered to get your code-coverage up to 75% that we ask for.

For instance, Lines 40-43 are covered but Lines 44-45 aren't. These are seemingly the same except for checking on 2 or 3 letter ISO codes, unless I'm mistaken.

Or the message response within the stop() functions in the same file aren't checked.

Could I ask that you have another look and see if you can't further improve the coverage a bit more?

Aug 18 '22 07:08 adamhsparks

Hi @adamhsparks I went up to 71.27%, but further changes are not very productive. I did not extensively cover two areas, one is the constructor for the dataset() itself, where I expect potentially breaking changes, and in the file I/O areas, where I think I would like to come up with a more general solution, and also avoid test being run on CRAN later. As the overwrite function and its messages make the most branches, this is a bit of a play with %, as the very same copied test is tested again and again.

Do you have a good solution to include download and file I/O tests that run fast enough or cause no disruption when later run on CRAN?

Aug 19 '22 12:08 antaldaniel

@adamhsparks I am much above your treshold, and apologies for the trivial error. I wanted to omit some issues in the dataset() construtor, but I did not realize that it had some old code that had been rewritten - the test were omitting them, of course, but they sat at the bottom of the file. It is now 81.2% covered, I know that it has to improve, but I'd prefer to do it when some issues are resolved in a clear direction (see my comment above.)

Aug 19 '22 17:08 antaldaniel

Hi @antaldaniel, that's great to see. Thank you for rechecking everything and updating.

If you have tests that you feel are unconducive for CRAN, I'd just use (and do liberally use) skip_on_cran(). Reviewers should hopefully be able to help guide you on this more.

Aug 20 '22 03:08 adamhsparks

@ropensci-review-bot check package

Aug 20 '22 03:08 adamhsparks

Thanks, about to send the query.

Aug 20 '22 03:08 ropensci-review-bot

:rocket:

Editor check started

:wave:

Aug 20 '22 03:08 ropensci-review-bot

Checks for dataset (v0.1.7.0002)

git hash: 93c03c54

: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_multiplication_x: Function names are duplicated in other packages
:heavy_check_mark: Package has continuous integration checks.
:heavy_check_mark: Package coverage is 82.1%.
: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: GPL (>= 3)

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	base	147
internal	dataset	66
internal	stats	2
imports	utils	2
imports	assertthat	NA
imports	ISOcodes	NA
suggests	covr	NA
suggests	declared	NA
suggests	dplyr	NA
suggests	eurostat	NA
suggests	here	NA
suggests	kableExtra	NA
suggests	knitr	NA
suggests	rdflib	NA
suggests	readxl	NA
suggests	rmarkdown	NA
suggests	spelling	NA
suggests	statcodelists	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.

base

names (21), class (12), data.frame (10), paste (9), vapply (9), rep (7), character (6), unlist (6), attr (5), lapply (5), length (5), ncol (5), subset (4), as.character (3), c (3), seq_along (3), as.data.frame (2), as.numeric (2), attributes (2), cbind (2), file (2), inherits (2), logical (2), matrix (2), nrow (2), round (2), which (2), date (1), for (1), ifelse (1), is.null (1), paste0 (1), rbind (1), seq_len (1), tolower (1), union (1), unique (1), url (1), UseMethod (1)

dataset

attributes_measures (5), dimensions (4), all_unique (3), dataset_title (3), measures (3), creator (2), datacite (2), dataset (2), dataset_source (2), description (2), geolocation (2), identifier (2), language (2), metadata_header (2), publication_year (2), publisher (2), related_item_identifier (2), resource_type (2), bibentry_dataset (1), datacite_add (1), dataset_download (1), dataset_download_csv (1), dataset_export (1), dataset_export_csv (1), dataset_local_id (1), dataset_title_create (1), dataset_uri (1), dublincore (1), dublincore_add (1), extract_year (1), is.dataset (1), print (1), print.dataset (1), related_item (1), resource_type_general (1), resource_type_general_allowed (1), rights (1), subject (1), time_var_guess (1), version (1)

stats

df (2)

utils

object.size (1), read.csv (1)

NOTE: Some imported packages appear to have no 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 24 files) and
1 authors
7 vignettes
no internal data file
3 imported packages
56 exported functions (median 10 lines of code)
66 non-exported functions in R (median 15 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	24	85.5
files_vignettes	7	98.5
files_tests	28	97.7
loc_R	889	64.9
loc_vignettes	676	84.7
loc_tests	432	72.0
num_vignettes	7	99.2	TRUE
n_fns_r	122	81.1
n_fns_r_exported	56	89.5
n_fns_r_not_exported	66	74.6
n_fns_per_file_r	3	54.4
num_params_per_fn	2	11.9
loc_per_fn_r	11	32.3
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	15	49.5
rel_whitespace_R	27	75.4
rel_whitespace_vignettes	36	88.3
rel_whitespace_tests	28	76.4
doclines_per_fn_exp	39	48.6
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	103	79.7

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
2891146042	pkgcheck	failure	93c03c	17	2022-08-19
2891146050	test-coverage	success	93c03c	20	2022-08-19

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fail:

no_description_date

Test coverage with covr

Package coverage: 82.12

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
datacite_add	24
dublincore_add	23

Static code analyses with lintr

lintr found the following 370 potential issues:

message	number of times
Avoid library() and require() calls in packages	20
Lines should not be more than 80 characters.	350

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 10 function names are duplicated in other packages:

- dataset from assemblerr, febr, robis
- description from dataMaid, dataPreparation, dataReporter, dcmodify, memisc, metaboData, PerseusR, ritis, rmutil, rsyncrosim, stream, synchronicity, timeSeries, tis, validate
- dimensions from gdalcubes, openeo, sp, tiledb
- identifier from Ramble
- is.dataset from crunch
- language from sylly, wakefield
- measures from greybox, mlr3measures, tsibble
- size from acrt, BaseSet, container, crmPack, CVXR, datastructures, deal, disto, easyVerification, EFA.MRFA, flifo, gdalcubes, gWidgets2, hrt, iemisc, InDisc, kernlab, matlab2r, multiverse, optimbase, PopED, pracma, ramify, rEMM, rmonad, simplegraph, siren, tcltk2, UComp, unival, vampyr
- subject from DGM, emayili, gmailr, sendgridr
- version from BiocManager, garma, geoknife, mice, R6DS, rerddap, rsyncrosim, shiny.info, SMFilter

Package Versions

package	version
pkgstats	0.1.1.20
pkgcheck	0.1.0.3

Editor-in-Chief Instructions:

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

Aug 20 '22 03:08 ropensci-review-bot

@ropensci-review-bot assign @melvidoni as editor

Aug 22 '22 00:08 adamhsparks

Assigned! @melvidoni is now the editor

Aug 22 '22 00:08 ropensci-review-bot

@ropensci-review-bot seeking reviewers

Aug 22 '22 01:08 melvidoni

Please add this badge to the README of your package repository:

[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/553_status.svg)](https://github.com/ropensci/software-review/issues/553)

Furthermore, if your package does not have a NEWS.md file yet, please create one to capture the changes made during the review process. See https://devguide.ropensci.org/releasing.html#news

Aug 22 '22 01:08 ropensci-review-bot

For clarity, apart from adding the README badge, I made a few URL corrections and added a paragraph to the Movtivation vignette.

Aug 22 '22 07:08 antaldaniel

@ropensci-review-bot assign @duttashi as reviewer

Aug 23 '22 21:08 melvidoni

@duttashi added to the reviewers list. Review due date is 2022-09-13. Thanks @duttashi for accepting to review! Please refer to our reviewer guide.

rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers guide and code of conduct for more.

Aug 23 '22 21:08 ropensci-review-bot

software-review software-review copied to clipboard

dataset: Create Data Frames that are Easier to Exchange and Reuse

Archive: TBD Version accepted: TBD Language: en

Scope

Technical checks

Publication options

Code of conduct

Checks for dataset (v0.1.7)

1. Package Dependencies

2. Statistical Properties

2a. Network visualisation

3. goodpractice and other checks

3b. goodpractice results

R CMD check with rcmdcheck

Test coverage with covr

Cyclocomplexity with cyclocomp

Static code analyses with lintr

4. Other Checks

Editor-in-Chief Instructions:

Checks for dataset (v0.1.7.0002)

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

4. Other Checks

Editor-in-Chief Instructions:

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