software-review daiquiri: Data quality reporting for temporal datasets

Submitting Author Name: T. Phuong Quan Submitting Author Github Handle: @phuongquan Other Package Authors Github handles: (comma separated, delete if none) Repository: https://github.com/phuongquan/daiquiri Version submitted: 0.7.1 Submission type: Standard Editor: @maurolepore Reviewers: @brad-cannell, @elinw

Due date for @brad-cannell: 2022-07-27

Due date for @elinw: 2022-08-17 Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: daiquiri
Type: Package
Title: Data Quality Reporting for Temporal Datasets
Version: 0.7.1
Authors@R: c(
    person(c("T.", "Phuong"), "Quan", email = "[email protected]",
        role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8566-1817")),
    person("Jack", "Cregan", role = "ctb"),
    person(family = "University of Oxford", role = "cph"),
    person(family = "National Institute for Health Research (NIHR)", role = "fnd")
    )
Description: Generate reports that enable quick visual review of 
    temporal shifts in record-level data. Time series plots showing aggregated 
    values are automatically created for each data field (column) depending on its 
    contents (e.g. min/max/mean values for numeric data, no. of distinct 
    values for categorical data), as well as overviews for missing values, 
    non-conformant values, and duplicated rows. The resulting reports are sharable 
    and can contribute to forming a transparent record of the entire analysis process. 
    It is designed with Electronic Health Records in mind, but can be used for 
    any type of record-level temporal data (i.e. tabular data where each row represents 
    a single “event”, one column contains the "event date", and other columns 
    contain any associated values for the event).
URL: https://github.com/phuongquan/daiquiri
BugReports: https://github.com/phuongquan/daiquiri/issues
License: GPL (>=3)
Encoding: UTF-8
Imports:
    data.table (>= 1.12.8),
    readr (>= 1.3.1),
    ggplot2 (>= 3.1.0),
    scales (>= 1.1.0),
    cowplot (>= 0.9.3),
    rmarkdown,
    reactable (>= 0.2.3),
    utils,
    stats
RoxygenNote: 7.1.2
Suggests:
    covr,
    knitr,
    testthat (>= 3.0.0),
    codemetar
VignetteBuilder: knitr
Config/testthat/edition: 3

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
- [ ] 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
- [ ] geospatial data
- [ ] text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

It takes a generic data frame containing raw, record-level, temporal data, and generates a data quality report that enables quick visual review of any unexpected temporal shifts in measures such as missingness, min/max/mean/distinct values, and non-conformance.

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

The target audience is all researchers who analyse data from large, temporal datasets, particularly routinely-collected data such as electronic health records. The package helps them to quickly check for temporal biases in their data before embarking on their main analyses. It also helps them to do this in a thorough, consistent and transparent way (since the reports are shareable), hence increasing the quality of their studies as well as trust in the scientific process.

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 my knowledge, there are a small number of R packages that generate summary statistics and/or data quality reports, (with the two most similar being dataquieR and DQAstats), but none which assist in identifying temporal changes in the data, nor which are as lightweight to use and consume.

(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.

https://github.com/ropensci/software-review/issues/527

Explain reasons for any pkgcheck items which your package is unable to pass.

Here is the list of items from version 0.0.3.11. The latest development version (0.0.3.13) is not currently working on Windows.

These functions do not have examples: [daiquiri] - This is not actually a function, daiquiri is the package help page and includes links to the "most useful" function and vignette.
Package uses global assignment operator (‘<<-’). - This is necessary for a withCallingHandlers() call
Package has no continuous integration checks. - The package uses GitHub Actions for R-CMD-check and for covr so I don't know why this is showing as a fail.
Namespace in Imports field not imported from: ‘reactable’ All declared Imports should be used - The 'reactable' package is in fact used but is probably being missed because it is called from an Rmd file in the inst/rmd folder
There are two functions with cyclocomplexity above 15: a) validate_params_type() and b) aggregatefield(). These functions are essentially large switch statements covering a) all arguments to all exported functions (for validating what a user passes to the function, and where some arguments are used across multiple functions), and b) all the different ways that the package can aggregate a data field (e.g. mean, min, max). The only obvious way I can see to reduce the cyclocomplexity score would be to separate out the items into subfunctions, but I judge that would actually make the code harder to follow rather than easier. I am open to other suggestions.

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 using services such as Travis CI, Coveralls and/or CodeCov.

Publication options

[x] Do you intend for this package to go on CRAN?
[ ] Do you intend for this package to go on Bioconductor?
[ ] 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.

May 16 '22 15:05 phuongquan

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

May 16 '22 15:05 ropensci-review-bot

:rocket:

Editor check started

:wave:

May 16 '22 15:05 ropensci-review-bot

Checks for daiquiri (v0.7.1)

git hash: c316aeb1

: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_multiplication_x: These functions do not have examples: [daiquiri].
:heavy_check_mark: Package has continuous integration checks.
:heavy_multiplication_x: Package coverage failed
: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	465
internal	daiquiri	120
internal	graphics	6
imports	ggplot2	63
imports	stats	26
imports	data.table	17
imports	utils	9
imports	scales	8
imports	readr	5
imports	cowplot	5
imports	rmarkdown	1
imports	reactable	NA
suggests	covr	NA
suggests	knitr	NA
suggests	testthat	NA
suggests	codemetar	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

list (83), c (29), format (23), sum (22), length (20), names (20), vapply (17), structure (16), is.na (15), for (12), inherits (11), with (11), character (10), is.nan (10), max (9), suppressWarnings (8), seq_along (7), class (6), labels (6), min (6), paste0 (6), which (6), as.character (5), call (5), logical (5), message (5), ncol (5), options (5), anyNA (4), comment (4), lapply (4), mean (4), quote (4), unique (4), as.integer (3), file (3), nrow (3), vector (3), by (2), ceiling (2), data.frame (2), file.path (2), formals (2), grep (2), nchar (2), strsplit (2), which.max (2), as.Date (1), as.list (1), as.numeric (1), col (1), do.call (1), dQuote (1), emptyenv (1), floor (1), gsub (1), is.symbol (1), log10 (1), missing (1), new.env (1), open (1), paste (1), q (1), rle (1), row.names (1), save (1), seq (1), sort (1), substring (1), sys.calls (1), system.file (1), typeof (1), unlist (1), warning (1), withCallingHandlers (1)

daiquiri

fieldtypes (15), datafield (14), fieldtype (11), get_datafield_max (4), get_datafield_min (4), is.fieldtype_timepoint (3), timepoint_as_aggregationunit (3), aggregate_data (2), aggtype_friendlyname (2), fieldtypes_to_cols (2), ft_allfields (2), ft_ignore (2), ft_timepoint (2), get_datafield_missing (2), identify_duplicaterows (2), is.fieldtype (2), log_initialise (2), plot_overview_heatmap_static (2), plot_overview_totals_static (2), aggregateallfields (1), aggregatefield (1), create_report (1), export_aggregated_data (1), fieldtypes_template (1), fieldtypes_to_string (1), ft_categorical (1), ft_datetime (1), ft_duplicates (1), ft_freetext (1), ft_numeric (1), ft_simple (1), ft_uniqueidentifier (1), get_aggfunctions (1), get_collector (1), get_dataclass (1), get_datafield_basetype (1), get_datafield_count (1), get_datafield_fieldtype_name (1), get_datafield_validation_warnings_n (1), get_datafield_vector (1), get_fieldtype_name (1), is.aggregatedata (1), is.aggregatefield (1), is.datafield (1), is.fieldtype_calculated (1), is.fieldtype_datetime (1), is.fieldtype_ignore (1), is.fieldtype_numeric (1), is.fieldtypes (1), is.sourcedata (1), log_close (1), log_function_end (1), log_function_start (1), log_message (1), plot_overview_combo_static (1), plot_timeseries_static (1), prepare_data (1), report_data (1), summarise_aggregated_data (1), summarise_source_data (1), yscale_breaks (1)

ggplot2

element_text (17), theme (7), ggplot (6), element_blank (5), labs (5), aes_string (4), a (3), element_rect (3), scale_fill_gradient (3), scale_x_date (3), facet_grid (2), geom_point (2), geom_line (1), scale_y_continuous (1), unit (1)

stats

df (10), dt (7), heatmap (6), median (3)

data.table

data.table (10), fifelse (4), as.data.table (2), copy (1)

utils

data (7), object.size (1), packageNa (1)

scales

label_date_short (5), breaks_pretty (3)

graphics

title (6)

cowplot

plot_grid (5)

readr

read_delim (2), col_character (1), cols (1), type_convert (1)

rmarkdown

render (1)

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 8 files) and
1 authors
1 vignette
no internal data file
9 imported packages
18 exported functions (median 10 lines of code)
130 non-exported functions in R (median 8 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	8	50.7
files_vignettes	1	68.4
files_tests	7	86.4
loc_R	1314	75.1
loc_vignettes	143	37.3
loc_tests	690	80.9
num_vignettes	1	64.8
n_fns_r	148	84.9
n_fns_r_exported	18	64.2
n_fns_r_not_exported	130	88.2
n_fns_per_file_r	11	88.4
num_params_per_fn	2	10.4
loc_per_fn_r	8	20.0
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	8	22.6
rel_whitespace_R	18	74.6
rel_whitespace_vignettes	45	47.5
rel_whitespace_tests	21	80.2
doclines_per_fn_exp	68	78.4
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	141	84.4

2a. Network visualisation

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

3. `goodpractice` and other checks

Details of goodpractice and other checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
2332879957	R-CMD-check	success	c316ae	17	2022-05-16
2332879944	test-coverage	success	c316ae	16	2022-05-16

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following note:

checking dependencies in R code ... NOTE Namespace in Imports field not imported from: ‘reactable’ All declared Imports should be used.

R CMD check generated the following check_fails:

cyclocomp
rcmdcheck_imports_not_imported_from

Test coverage with covr

ERROR: Test Coverage Failed

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
validate_params_type	84
aggregatefield	56

Static code analyses with lintr

lintr found the following 550 potential issues:

message	number of times
Lines should not be more than 80 characters.	550

Package Versions

package	version
pkgstats	0.0.4.30
pkgcheck	0.0.3.19

Editor-in-Chief Instructions:

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

May 16 '22 15:05 ropensci-review-bot

Hi I think there is a problem with pkgcheck.

As you can see from the badge on https://github.com/phuongquan/daiquiri, code coverage is 96%, so I don't know why the Test Coverage check has failed your side.

Also, as I mentioned in the submission, the item "These functions do not have examples: [daiquiri]" does not appear to be appropriate since daiquiri is not a function but is the package itself (following https://r-pkgs.org/man.html#man-packages). If you want me to add examples for a selection of functions in the package I can do so, but it is not clear to me if that is appropriate.

Thanks.

May 16 '22 17:05 phuongquan

@phuongquan You're right about the functions not having examples. That was a bug which has now been fixed, so thanks! You can ignore that result. The coverage issue is, however, more problematic. I've tried to run covr::package_coverage() (as well as codecov() in a couple of different docker containers and always encouter this error:

simpleWarning in utils::install.packages(repos = NULL, lib = tmp_lib, pkg$path, : installation of package ‘/daiquiri’ had non-zero exit status

simpleWarning in file(con, "r"): cannot open file '/tmp/RtmpRuQwPt/R_LIBS14f26d8825f2/daiquiri/R/daiquiri': No such file or directory

Error in file(con, "r"): cannot open the connection

This is clearly what causes the fails on our system. You should be able to reproduce by cloning your repo in a clean docker container and trying for yourself. I nevertheless see that the GitHub action works, and that your coverage is indeed as you claim, so you may also choose to ignore that if you like. My guess is that it's related to {renv}. covr installs the package in a temporary library location, but our check system loads the package prior to that and so populates your renv directory. That then does not get copied across to the temporary libary location used by covr, and so installation fails. It works on GitHub actions because that uses a fresh clone of the repo which populates renv in the temporary library location.

May 16 '22 19:05 mpadge

Hi I just wanted to check if there is anything you are waiting on me for?

May 30 '22 08:05 phuongquan

Hi @phuongquan - I apologize for the delay on my end. Thank you again for your submission. I will work on finding an editor and follow up shortly.

May 30 '22 13:05 emilyriederer

@ropensci-review-bot assign @maurolepore as editor

May 30 '22 17:05 emilyriederer

@ropensci-review-bot assign @maurolepore as editor

May 30 '22 17:05 emilyriederer

Assigned! @maurolepore is now the editor

May 30 '22 17:05 ropensci-review-bot

Hi @phuongquan, I'm pleased to be the handling editor of this submission.

I'll re-run checks now. I see the comments above suggest some checks may fail for reasons beyond your control. I'll explore those issues and walk through the editor's template by the end of this week.

May 30 '22 21:05 maurolepore

@ropensci-review-bot check package

May 30 '22 21:05 maurolepore

Thanks, about to send the query.

May 30 '22 21:05 ropensci-review-bot

:rocket:

Editor check started

:wave:

May 30 '22 21:05 ropensci-review-bot

Checks for daiquiri (v0.7.1)

git hash: c316aeb1

: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_multiplication_x: Package coverage failed
: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	465
internal	daiquiri	120
internal	graphics	6
imports	ggplot2	63
imports	stats	26
imports	data.table	17
imports	utils	9
imports	scales	8
imports	readr	5
imports	cowplot	5
imports	rmarkdown	1
imports	reactable	NA
suggests	covr	NA
suggests	knitr	NA
suggests	testthat	NA
suggests	codemetar	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

list (83), c (29), format (23), sum (22), length (20), names (20), vapply (17), structure (16), is.na (15), for (12), inherits (11), with (11), character (10), is.nan (10), max (9), suppressWarnings (8), seq_along (7), class (6), labels (6), min (6), paste0 (6), which (6), as.character (5), call (5), logical (5), message (5), ncol (5), options (5), anyNA (4), comment (4), lapply (4), mean (4), quote (4), unique (4), as.integer (3), file (3), nrow (3), vector (3), by (2), ceiling (2), data.frame (2), file.path (2), formals (2), grep (2), nchar (2), strsplit (2), which.max (2), as.Date (1), as.list (1), as.numeric (1), col (1), do.call (1), dQuote (1), emptyenv (1), floor (1), gsub (1), is.symbol (1), log10 (1), missing (1), new.env (1), open (1), paste (1), q (1), rle (1), row.names (1), save (1), seq (1), sort (1), substring (1), sys.calls (1), system.file (1), typeof (1), unlist (1), warning (1), withCallingHandlers (1)

daiquiri

fieldtypes (15), datafield (14), fieldtype (11), get_datafield_max (4), get_datafield_min (4), is.fieldtype_timepoint (3), timepoint_as_aggregationunit (3), aggregate_data (2), aggtype_friendlyname (2), fieldtypes_to_cols (2), ft_allfields (2), ft_ignore (2), ft_timepoint (2), get_datafield_missing (2), identify_duplicaterows (2), is.fieldtype (2), log_initialise (2), plot_overview_heatmap_static (2), plot_overview_totals_static (2), aggregateallfields (1), aggregatefield (1), create_report (1), export_aggregated_data (1), fieldtypes_template (1), fieldtypes_to_string (1), ft_categorical (1), ft_datetime (1), ft_duplicates (1), ft_freetext (1), ft_numeric (1), ft_simple (1), ft_uniqueidentifier (1), get_aggfunctions (1), get_collector (1), get_dataclass (1), get_datafield_basetype (1), get_datafield_count (1), get_datafield_fieldtype_name (1), get_datafield_validation_warnings_n (1), get_datafield_vector (1), get_fieldtype_name (1), is.aggregatedata (1), is.aggregatefield (1), is.datafield (1), is.fieldtype_calculated (1), is.fieldtype_datetime (1), is.fieldtype_ignore (1), is.fieldtype_numeric (1), is.fieldtypes (1), is.sourcedata (1), log_close (1), log_function_end (1), log_function_start (1), log_message (1), plot_overview_combo_static (1), plot_timeseries_static (1), prepare_data (1), report_data (1), summarise_aggregated_data (1), summarise_source_data (1), yscale_breaks (1)

ggplot2

element_text (17), theme (7), ggplot (6), element_blank (5), labs (5), aes_string (4), a (3), element_rect (3), scale_fill_gradient (3), scale_x_date (3), facet_grid (2), geom_point (2), geom_line (1), scale_y_continuous (1), unit (1)

stats

df (10), dt (7), heatmap (6), median (3)

data.table

data.table (10), fifelse (4), as.data.table (2), copy (1)

utils

data (7), object.size (1), packageNa (1)

scales

label_date_short (5), breaks_pretty (3)

graphics

title (6)

cowplot

plot_grid (5)

readr

read_delim (2), col_character (1), cols (1), type_convert (1)

rmarkdown

render (1)

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 8 files) and
1 authors
1 vignette
no internal data file
9 imported packages
18 exported functions (median 10 lines of code)
130 non-exported functions in R (median 8 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	8	50.7
files_vignettes	1	68.4
files_tests	7	86.4
loc_R	1314	75.1
loc_vignettes	143	37.3
loc_tests	690	80.9
num_vignettes	1	64.8
n_fns_r	148	84.9
n_fns_r_exported	18	64.2
n_fns_r_not_exported	130	88.2
n_fns_per_file_r	11	88.4
num_params_per_fn	2	10.4
loc_per_fn_r	8	20.0
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	8	22.6
rel_whitespace_R	18	74.6
rel_whitespace_vignettes	45	47.5
rel_whitespace_tests	21	80.2
doclines_per_fn_exp	68	78.4
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	141	84.4

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
2332879957	R-CMD-check	success	c316ae	17	2022-05-16
2332879944	test-coverage	success	c316ae	16	2022-05-16

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following note:

checking dependencies in R code ... NOTE Namespace in Imports field not imported from: ‘reactable’ All declared Imports should be used.

R CMD check generated the following check_fails:

cyclocomp
rcmdcheck_imports_not_imported_from

Test coverage with covr

ERROR: Test Coverage Failed

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
validate_params_type	84
aggregatefield	56

Static code analyses with lintr

lintr found the following 550 potential issues:

message	number of times
Lines should not be more than 80 characters.	550

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 5 function names are duplicated in other packages:

- aggregate_data from simITS
- create_report from DataExplorer, prodigenr, reporter
- log_close from logr
- prepare_data from bbsBayes, bigstep, childsds, corporaexplorer, disaggregation, fHMM, ggasym, multigroup, multimorbidity, mutualinf, nmm, parsnip, PLNmodels, sglOptim, shapr, ssMousetrack
- read_data from creditmodel, deaR, deforestable, diverse, ecocomDP, GeodesiCL, logib, metrix, prepdat, qtlpoly, RTextTools, sjlabelled, whippr

Package Versions

package	version
pkgstats	0.0.4.55
pkgcheck	0.0.3.40

Editor-in-Chief Instructions:

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

May 30 '22 21:05 ropensci-review-bot

Note: We're still working on a fix for the package coverage failure there - see package GitHub workflow for actual coverage in the meantime: 96%. Sorry for an inconvenience @phuongquan.

May 31 '22 08:05 mpadge

Dear @phuongquan,

Thanks again for this awesome submisison! I'm here to help you pass review smoothly. I looked for opportunities to make the package follow rOpenSci's guidelines as closely as possible, and to make the reviewer's job as easy as possible. After the section "Editor checks" you'll see my comments:

I listed some issues I need you to please address before I start searching for reviewers. You may refer to them using the labels ml01, ml02, and so on.
Also I listed some other issues for you to consider. Those ones have no label and won't block the process but if you address them now you're sure they won't be picked up by the reviewers.

Please let me know what questions you have.

Editor checks:

[ ] 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?
[ ] Tests: If the package has some interactivity / HTTP / plot production etc. are the tests using state-of-the-art tooling?
[x] Contributing information: Is the documentation for contribution clear enough e.g. tokens for tests, playgrounds?
[ ] 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

TODO

Documentation:

[x] ml01. In README please add a basic example of the package's usage (e.g. dplyr's usage).

Installation instructions:

[ ] ml02. Please simplify the installation instructions. This code should install daiquiri v0.7.0 and all its dependencies (details about the repo argument):

# install.packages("devtools")
devtools::install_github("phuongquan/[email protected]")

Or

# install.packages("pak")
pak::pkg_install("phuongquan/[email protected]")

[ ] ml03. Testing: Please rename for every R/file.R please rename the corresponding file in tests/testhat/ from test_files.R to test-files.R. This change is small and enables the development workflow with use_r() and use_test().

CONSIDER

Documentation:

We recommend creating a documentation website for your package using pkgdown.
If you do use pkgdown and rename your vignette from "walkthrough" to "daiquiri" then it will be automatically exposed on the package website under the navigation-bar link as "Get started" (e.g. dplyr, readr).

Fit:

I see a comment about some ovelap with the pakcages dataquieR and DQAstats, and I also see some similarities with the packages skimr and naniar. Consider comparing the alternatives in README or an artile (exapmle for piggyback).

Installation instructions:

It's common to consider the code on GitHub as "in development" and the code on CRAN as "released". If embrace that release model then users installing from GitHub only need the default branch.
If you stick to using GitHub releases, then consider upgrading them from "pre-release" to "release" (https://github.com/phuongquan/daiquiri/releases).
The installation instructions are under the heading "Get started". Instead many popular packages use the heading "Installation" (e.g. dplyr, ggplot2), mainy because it's the template you get with usethis::use_readme_rmd().

Tests:

In my local environment devtools::test() results in 24 warnings with this message:

semi-transparency is not supported on this device: reported only once per page

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.0 (2022-04-22)
#>  os       Ubuntu 20.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Guatemala
#>  date     2022-06-03
#>  pandoc   2.17.1.1 @ /usr/lib/rstudio/bin/quarto/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  brio          1.1.3   2021-11-30 [1] RSPM
#>  cachem        1.0.6   2021-08-19 [1] RSPM
#>  callr         3.7.0   2021-04-20 [1] RSPM
#>  cli           3.3.0   2022-04-25 [1] RSPM
#>  crayon        1.5.1   2022-03-26 [1] RSPM
#>  desc          1.4.1   2022-03-06 [1] RSPM
#>  devtools      2.4.3   2021-11-30 [1] RSPM
#>  digest        0.6.29  2021-12-01 [1] RSPM
#>  ellipsis      0.3.2   2021-04-29 [1] RSPM
#>  evaluate      0.15    2022-02-18 [1] RSPM
#>  fansi         1.0.3   2022-03-24 [1] RSPM
#>  fastmap       1.1.0   2021-01-25 [1] RSPM
#>  fs            1.5.2   2021-12-08 [1] RSPM
#>  glue          1.6.2   2022-02-24 [1] RSPM
#>  highr         0.9     2021-04-16 [1] RSPM
#>  htmltools     0.5.2   2021-08-25 [1] RSPM
#>  knitr         1.39    2022-04-26 [1] RSPM
#>  lifecycle     1.0.1   2021-09-24 [1] RSPM
#>  magrittr      2.0.3   2022-03-30 [1] RSPM
#>  memoise       2.0.1   2021-11-26 [1] RSPM
#>  pillar        1.7.0   2022-02-01 [1] RSPM
#>  pkgbuild      1.3.1   2021-12-20 [1] RSPM
#>  pkgconfig     2.0.3   2019-09-22 [1] RSPM
#>  pkgload       1.2.4   2021-11-30 [1] RSPM
#>  prettyunits   1.1.1   2020-01-24 [1] RSPM
#>  processx      3.5.3   2022-03-25 [1] RSPM
#>  ps            1.7.0   2022-04-23 [1] RSPM
#>  purrr         0.3.4   2020-04-17 [1] RSPM
#>  R.cache       0.15.0  2021-04-30 [1] RSPM
#>  R.methodsS3   1.8.1   2020-08-26 [1] RSPM
#>  R.oo          1.24.0  2020-08-26 [1] RSPM
#>  R.utils       2.11.0  2021-09-26 [1] RSPM
#>  R6            2.5.1   2021-08-19 [1] RSPM
#>  remotes       2.4.2   2021-11-30 [1] RSPM
#>  reprex        2.0.1   2021-08-05 [1] RSPM
#>  rlang         1.0.2   2022-03-04 [1] RSPM
#>  rmarkdown     2.14    2022-04-25 [1] RSPM
#>  rprojroot     2.0.3   2022-04-02 [1] RSPM
#>  rstudioapi    0.13    2020-11-12 [1] RSPM
#>  sessioninfo   1.2.2   2021-12-06 [1] RSPM
#>  stringi       1.7.6   2021-11-29 [1] RSPM
#>  stringr       1.4.0   2019-02-10 [1] RSPM
#>  styler        1.7.0   2022-03-13 [1] RSPM
#>  testthat      3.1.4   2022-04-26 [1] RSPM
#>  tibble        3.1.7   2022-05-03 [1] RSPM (R 4.2.0)
#>  usethis       2.1.6   2022-05-25 [1] RSPM (R 4.2.0)
#>  utf8          1.2.2   2021-07-24 [1] RSPM
#>  vctrs         0.4.1   2022-04-13 [1] RSPM
#>  withr         2.5.0   2022-03-03 [1] RSPM
#>  xfun          0.31    2022-05-10 [1] RSPM (R 4.2.0)
#>  yaml          2.3.5   2022-02-21 [1] RSPM
#> 
#>  [1] /home/mauro/R/x86_64-pc-linux-gnu-library/4.2
#>  [2] /opt/R/4.2.0/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

^{Created on 2022-06-03 by the reprex package (v2.0.1)}

License:

Consider running usethis::use_gpl3_license(). This is a good way to ensure the license and related infrastructure are setup correctly.

Other:

Consider deactivating renv with renv::deactivate(). I see that renv can help the develpoer in some ways -- such as to explore issues with backwards and forwards compatibility, {and} reverse dependency checks -- but it seems to add some friction for reviewers. For example, immediatlely after cloning the repo in a fresh project on https://rstudio.cloud I get this message:

* Project '/cloud/project' loaded. [renv 0.12.2]
Warning message:
Project requested R version '4.1.2' but '4.2.0' is currently being used 
* The project may be out of sync -- use `renv::status()` for more details.

Consider running usethis::use_tidy_style(). This will style the code in a way that is more likely to be familiar to the reviewers.
Consider running spelling::spell_check_package().
Consider running goodpractice(). The most frequent suggestion I see is this:

"avoid long code lines, it's bad for readability. Also, many people prefer editor windows that are about 80 characters wide. Try make your lines shorter than 80 characters"

# devtools::install_github("mangothecat/goodpractice") 
goodpractice::goodpractice()

Consider removing the package reactable from DESCRIPTION or importing one of its functions (maybe via usethis::use_import_from()). This should avoid this R CMD check note:

❯ checking dependencies in R code ... NOTE
  Namespace in Imports field not imported from: ‘reactable’
    All declared Imports should be used.

Consider replacing the current package-documentation with the one generated automatically with usethis::use_package_doc(). This might help avoid the issue with the bot.
Consider automating package-development tasks with usethis (https://usethis.r-lib.org/). For a quick tour see https://r-pkgs.org/whole-game.html.

Jun 03 '22 19:06 maurolepore

Hi @maurolepore,

Many thanks for your comments. I have pushed the following changes:

ml01: I have added a Usage section to the README
ml02: I encountered a problem with your suggestion of devtools::install_github("phuongquan/[email protected]") in that it failed for me when I added the option build_vignettes = TRUE. I think it might be an issue with renv and I don't know how to fix it, or if other people would get the same error. Since I feel the vignette is very important, I have updated the installation instructions to continue to use the github release but also included a line to explicitly install the dependencies first. Please let me know if this is sufficient. Also, this should no longer be an issue once it's on CRAN
ml03: I have renamed all the test files to use dashes instead of underscores

Other items:

I have published a pkgdown site to github pages (https://phuongquan.github.io/daiquiri/index.html)
I have recreated the Licence using usethis::use_gpl3_license()
In the README, I have renamed the Get Started section to Installation, and moved the reference to the vignette to the Usage section
I have reformatted much of the code and reduced line lengths where possible

Lastly, The R CMD check note

Namespace in Imports field not imported from: ‘reactable’ All declared Imports should be used

is erroneous. The 'reactable' package is in fact used (and so needs to be in the DESCRIPTION file) but is probably being missed because it is called from an Rmd file in the inst/rmd folder.

Please let me know if you need anything more from me.

Kind regards,

Phuong

Jun 08 '22 16:06 phuongquan

Thanks! We are almost there :-)

ml01. It would be nice to see the output so users can understand without having to install the package and run the code. This idea comes from the philosophy of good reproducible examples -- https://reprex.tidyverse.org/articles/reprex-dos-and-donts.html#package-philosophy

[x] ml02: I thought about this a bit more and I now think we can't ask the reviewers to consider both the HEAD of the repo and the latest release. If you want them to review the latest release, then please make it the HEAD. Then the simpler installation instructions should be sufficient. After the review you are welcome to again point users to a GitHub release if you still think that's the best idea. In general I think it's good to see what other popular packages do. That will give you a good sense of what most users will expect. Users tend to like packages that are consistent with other packages because they can reuse what they already know, lowering the overall effort they need to use the new package.
[x] ml04: Please deactivate renv from the version you submit for review. I thought a bit more about this issue and I now think it's imprtant to address it before review. Reviewers will have to deactivate renv to access packages they need for the review (e.g. devtools) and renv blocks that. Please be kind with the voluntary reviewers and do it yourself. After the review you are welcome to activate renv again.
[x] ml05: Please ensure the examples for create_report() and report_data() run in 5s or less. This is required for CRAN and kind to the reviewers who may run R CMD check multiple times. Also it may distract them from other more interesting issues. R CMD check shows this:

✔  checking examples (36.8s)
   Examples with CPU (user + system) or elapsed time > 5s
                   user system elapsed
   create_report 17.695  0.679  18.859
   report_data   13.871  0.165  14.737

[x] Please address the NOTE about reactable. You can do it by moving "reactable" from Imports: to Suggests of by using any of its functions in any file in the R/ directory. If you use a function only for this purpose please write a comment explaining so -- else the reviewers might wonder what's going on. I want to avoid this relatively simple NOTE to distract reviewers from more interesting issues. Anyway you'll need to address it before you submit to CRAN.

❯ checking dependencies in R code ... NOTE
  Namespace in Imports field not imported from: ‘reactable’
    All declared Imports should be used.

Jun 10 '22 21:06 maurolepore

@maurolepore We've updated the automated checking in the meantime, so it now flags any submissions which have renv in activated mode, and require that to be de-activated for reviews to proceed.

Jun 11 '22 11:06 mpadge

@ropensci-review-bot check package

Jun 14 '22 18:06 maurolepore

Thanks, about to send the query.

Jun 14 '22 18:06 ropensci-review-bot

:rocket:

Editor check started

:wave:

Jun 14 '22 18:06 ropensci-review-bot

Checks for daiquiri (v0.7.2)

git hash: 089bd85e

: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 96.1%.
:heavy_multiplication_x: Package has renv activated
: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	402
internal	daiquiri	102
internal	graphics	6
internal	mgcv	1
imports	ggplot2	37
imports	stats	19
imports	data.table	14
imports	scales	8
imports	utils	4
imports	readr	3
imports	cowplot	NA
imports	rmarkdown	NA
imports	reactable	NA
suggests	covr	NA
suggests	knitr	NA
suggests	testthat	NA
suggests	codemetar	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

list (74), c (28), sum (21), format (20), names (19), vapply (16), for (12), is.na (12), length (12), inherits (11), with (11), message (9), structure (9), character (8), suppressWarnings (8), seq_along (7), class (6), labels (6), max (6), as.character (5), call (5), logical (5), min (5), paste0 (5), which (5), as.double (4), is.nan (4), mean (4), options (4), unique (4), by (3), lapply (3), nrow (3), vector (3), anyNA (2), as.integer (2), ceiling (2), data.frame (2), file.path (2), formals (2), grep (2), nchar (2), which.max (2), as.Date (1), as.list (1), as.numeric (1), col (1), do.call (1), dQuote (1), emptyenv (1), file (1), gsub (1), is.symbol (1), missing (1), ncol (1), new.env (1), open (1), paste (1), q (1), rle (1), row.names (1), seq (1), sort (1), strsplit (1), substring (1), sys.calls (1), system.file (1), typeof (1), unlist (1), warning (1)

daiquiri

fieldtypes (12), datafield (11), fieldtype (11), get_datafield_max (4), get_datafield_min (4), is.fieldtype_timepoint (3), timepoint_as_aggregationunit (3), aggregate_data (2), ft_allfields (2), ft_ignore (2), ft_timepoint (2), get_datafield_missing (2), identify_duplicaterows (2), is.fieldtype (2), aggregateallfields (1), aggregatefield (1), aggtype_friendlyname (1), create_report (1), export_aggregated_data (1), fieldtypes_template (1), fieldtypes_to_string (1), ft_categorical (1), ft_datetime (1), ft_duplicates (1), ft_freetext (1), ft_numeric (1), ft_simple (1), ft_uniqueidentifier (1), get_aggfunctions (1), get_collector (1), get_dataclass (1), get_datafield_basetype (1), get_datafield_count (1), get_datafield_fieldtype_name (1), get_datafield_validation_warnings_n (1), get_datafield_vector (1), get_fieldtype_name (1), is.aggregatedata (1), is.aggregatefield (1), is.datafield (1), is.fieldtype_calculated (1), is.fieldtype_datetime (1), is.fieldtype_ignore (1), is.fieldtype_numeric (1), is.fieldtypes (1), is.sourcedata (1), log_initialise (1), plot_overview_heatmap_static (1), plot_overview_totals_static (1), prepare_data (1), report_data (1), summarise_aggregated_data (1), summarise_source_data (1), yscale_breaks (1)

ggplot2

element_text (10), element_blank (5), ggplot (5), labs (4), aes_string (3), facet_grid (2), geom_point (2), theme (2), element_rect (1), geom_line (1), scale_y_continuous (1), unit (1)

stats

df (7), dt (7), median (3), heatmap (2)

data.table

data.table (9), as.data.table (2), fifelse (2), copy (1)

scales

label_date_short (5), breaks_pretty (3)

graphics

title (5), axis (1)

utils

data (3), object.size (1)

readr

cols (3)

mgcv

s (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 8 files) and
1 authors
1 vignette
no internal data file
9 imported packages
18 exported functions (median 10 lines of code)
130 non-exported functions in R (median 9 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	8	50.7
files_vignettes	1	68.4
files_tests	7	86.4
loc_R	1914	83.4
loc_vignettes	143	37.3
loc_tests	878	85.1
num_vignettes	1	64.8
n_fns_r	148	84.9
n_fns_r_exported	18	64.2
n_fns_r_not_exported	130	88.2
n_fns_per_file_r	11	88.4
num_params_per_fn	2	10.4
loc_per_fn_r	10	27.7
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	9	27.1
rel_whitespace_R	12	75.0
rel_whitespace_vignettes	45	47.5
rel_whitespace_tests	17	79.9
doclines_per_fn_exp	68	78.4
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	141	84.4

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
2462421281	pages build and deployment	success	089bd8	3	2022-06-08
2462421408	R-CMD-check	success	089bd8	19	2022-06-08
2462421399	test-coverage	success	089bd8	18	2022-06-08

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following note:

checking dependencies in R code ... NOTE Namespace in Imports field not imported from: ‘reactable’ All declared Imports should be used.

R CMD check generated the following check_fails:

cyclocomp
rcmdcheck_imports_not_imported_from

Test coverage with covr

Package coverage: 96.12

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
validate_params_type	84
aggregatefield	56

Static code analyses with lintr

lintr found the following 277 potential issues:

message	number of times
Lines should not be more than 80 characters.	277

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 5 function names are duplicated in other packages:

- aggregate_data from simITS
- create_report from DataExplorer, prodigenr, reporter
- log_close from logr
- prepare_data from bbsBayes, bigstep, childsds, corporaexplorer, disaggregation, fHMM, ggasym, multigroup, multimorbidity, mutualinf, nmm, parsnip, PLNmodels, sglOptim, shapr, ssMousetrack
- read_data from creditmodel, deaR, deforestable, diverse, ecocomDP, GeodesiCL, logib, metrix, prepdat, qtlpoly, RTextTools, sjlabelled, whippr

Package Versions

package	version
pkgstats	0.0.4.75
pkgcheck	0.0.3.60

Editor-in-Chief Instructions:

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

Jun 14 '22 19:06 ropensci-review-bot

Hi @maurolepore,

I have pushed the following changes:

ml01: I have added an example report to the package website and added a link to it in the Usage section of the README
ml02: I want the reviewers to consider only the HEAD of the repo so have removed the reference to the latest 'release' from the installation instructions
ml04: I have deactivated renv
ml05: I have wrapped the long running examples in /donttest and they no longer appear in R CMD check. (I can't make them run any quicker as it is the rmarkdown rendering that is taking the time)
ml06: I have added a dummy function to stop the NOTE about reactable

Please let me know if you need anything more.

Thanks, Phuong

Jun 15 '22 16:06 phuongquan

Thanks! I'll run checks one last time and start searching for reviewers shortly.

Could you please name three people you think might be good reviewers and explain why?

(I would appoint only one but having more helps understand what kinds of skill you think are useful.)

Here are some more non-blocking comments.

ml01. Maybe you could minimize your example to somethis like this:

library(daiquiri)

path <- daiquiri_example("raw_data.csv")
raw_data <- readr::read_csv(path)
raw_data

if (interactive()) {
  create_report(raw_data)
}

Here is a version comments for you

library(daiquiri)

# Maybe write this helper (inspired by `readr::readr_example`)
path <- daiquiri_example("raw_data.csv")
# Maybe create a basic .csv that reads well directly with read_csv()?
# I think read_data() might be helpful but not the main goal of the package
raw_data <- readr::read_csv(path)

# Do show what the data looks like. Users may want to know if the data they
# have is a good fit for this package. Ensure to show just a bit. If it doesn't print
# as a tibble, then use `head()` or `str()`.
raw_data

# It won't run when you render README. It  will run if copy-pasting this code
if (interactive()) {
  # If you add this default: `fieldtypes = fieldtypes()`
  create_report(raw_data)
}

ml05: Would it run faster if you make the example raw_data smaller, like 1/4 or 1/10 of the size it has now?

Jun 16 '22 12:06 maurolepore

@ropensci-review-bot check package

Jun 16 '22 12:06 maurolepore

Thanks, about to send the query.

Jun 16 '22 12:06 ropensci-review-bot

:rocket:

Editor check started

:wave:

Jun 16 '22 12:06 ropensci-review-bot

Checks for daiquiri (v0.7.3)

git hash: 339a93e8

: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 96.1%.
: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	401
internal	daiquiri	102
internal	graphics	6
internal	mgcv	1
imports	ggplot2	37
imports	stats	19
imports	data.table	14
imports	scales	8
imports	utils	4
imports	readr	3
imports	reactable	1
imports	cowplot	NA
imports	rmarkdown	NA
suggests	covr	NA
suggests	knitr	NA
suggests	testthat	NA
suggests	codemetar	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

list (74), c (28), sum (21), format (20), names (19), vapply (16), for (12), is.na (12), length (12), with (11), inherits (10), message (9), structure (9), character (8), suppressWarnings (8), seq_along (7), class (6), labels (6), max (6), as.character (5), call (5), logical (5), min (5), paste0 (5), which (5), as.double (4), is.nan (4), mean (4), options (4), unique (4), by (3), lapply (3), nrow (3), vector (3), anyNA (2), as.integer (2), ceiling (2), data.frame (2), file.path (2), formals (2), grep (2), nchar (2), which.max (2), as.Date (1), as.list (1), as.numeric (1), col (1), do.call (1), dQuote (1), emptyenv (1), file (1), gsub (1), is.symbol (1), missing (1), ncol (1), new.env (1), open (1), paste (1), q (1), rle (1), row.names (1), seq (1), sort (1), strsplit (1), substring (1), sys.calls (1), system.file (1), typeof (1), unlist (1), warning (1)

daiquiri

fieldtypes (12), datafield (11), fieldtype (11), get_datafield_max (4), get_datafield_min (4), is.fieldtype_timepoint (3), timepoint_as_aggregationunit (3), aggregate_data (2), ft_allfields (2), ft_ignore (2), ft_timepoint (2), get_datafield_missing (2), identify_duplicaterows (2), is.fieldtype (2), aggregateallfields (1), aggregatefield (1), aggtype_friendlyname (1), create_report (1), dummy_reactable_call (1), export_aggregated_data (1), fieldtypes_template (1), fieldtypes_to_string (1), ft_categorical (1), ft_datetime (1), ft_duplicates (1), ft_freetext (1), ft_numeric (1), ft_simple (1), ft_uniqueidentifier (1), get_aggfunctions (1), get_collector (1), get_dataclass (1), get_datafield_basetype (1), get_datafield_count (1), get_datafield_fieldtype_name (1), get_datafield_validation_warnings_n (1), get_datafield_vector (1), get_fieldtype_name (1), is.aggregatedata (1), is.aggregatefield (1), is.datafield (1), is.fieldtype_calculated (1), is.fieldtype_datetime (1), is.fieldtype_ignore (1), is.fieldtype_numeric (1), is.fieldtypes (1), log_initialise (1), plot_overview_heatmap_static (1), plot_overview_totals_static (1), prepare_data (1), report_data (1), summarise_aggregated_data (1), summarise_source_data (1), yscale_breaks (1)

ggplot2

element_text (10), element_blank (5), ggplot (5), labs (4), aes_string (3), facet_grid (2), geom_point (2), theme (2), element_rect (1), geom_line (1), scale_y_continuous (1), unit (1)

stats

df (7), dt (7), median (3), heatmap (2)

data.table

data.table (9), as.data.table (2), fifelse (2), copy (1)

scales

label_date_short (5), breaks_pretty (3)

graphics

title (5), axis (1)

utils

data (3), object.size (1)

readr

cols (3)

mgcv

s (1)

reactable

colDef (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 8 files) and
1 authors
1 vignette
no internal data file
9 imported packages
18 exported functions (median 10 lines of code)
132 non-exported functions in R (median 9 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	8	50.7
files_vignettes	1	68.4
files_tests	7	86.4
loc_R	1917	83.4
loc_vignettes	143	37.3
loc_tests	878	85.1
num_vignettes	1	64.8
n_fns_r	150	85.2
n_fns_r_exported	18	64.2
n_fns_r_not_exported	132	88.4
n_fns_per_file_r	11	88.5
num_params_per_fn	2	10.4
loc_per_fn_r	9	24.3
loc_per_fn_r_exp	10	22.2
loc_per_fn_r_not_exp	9	27.1
rel_whitespace_R	13	75.2
rel_whitespace_vignettes	45	47.5
rel_whitespace_tests	17	79.9
doclines_per_fn_exp	69	79.2
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	141	84.4

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
2503730587	pages build and deployment	success	339a93	7	2022-06-15
2503730618	R-CMD-check	success	339a93	23	2022-06-15
2503730623	test-coverage	success	339a93	22	2022-06-15

3b. `goodpractice` results

`R CMD check` with rcmdcheck

R CMD check generated the following check_fail:

cyclocomp

Test coverage with covr

Package coverage: 96.06

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
validate_params_type	84
aggregatefield	56

Static code analyses with lintr

lintr found the following 279 potential issues:

message	number of times
Lines should not be more than 80 characters.	279

4. Other Checks

Details of other checks (click to open)

:heavy_multiplication_x: The following 5 function names are duplicated in other packages:

- aggregate_data from simITS
- create_report from DataExplorer, prodigenr, reporter
- log_close from logr
- prepare_data from bbsBayes, bigstep, childsds, corporaexplorer, disaggregation, fHMM, ggasym, multigroup, multimorbidity, mutualinf, nmm, parsnip, PLNmodels, sglOptim, shapr, ssMousetrack
- read_data from creditmodel, deaR, deforestable, diverse, ecocomDP, GeodesiCL, logib, metrix, prepdat, qtlpoly, RTextTools, sjlabelled, whippr

Package Versions

package	version
pkgstats	0.0.4.75
pkgcheck	0.0.3.60

Editor-in-Chief Instructions:

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

Jun 16 '22 12:06 ropensci-review-bot

software-review software-review copied to clipboard

daiquiri: Data quality reporting for temporal datasets

Due date for @elinw: 2022-08-17 Archive: TBD Version accepted: TBD Language: en

Scope

Technical checks

Publication options

Code of conduct

Checks for daiquiri (v0.7.1)

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 daiquiri (v0.7.1)

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:

Editor checks:

Editor comments

TODO

CONSIDER

Checks for daiquiri (v0.7.2)

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:

Checks for daiquiri (v0.7.3)

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

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