daiquiri: Data quality reporting for temporal datasets

[phuongquan]

Date accepted: 2022-10-25

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 = "phuong.quan@ndm.ox.ac.uk",
        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
  • 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.

#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.

1. 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.
2. Package uses global assignment operator (‘<<-’). - This is necessary for a withCallingHandlers() call
3. 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.
4. 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
5. 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.

• I have read the rOpenSci packaging guide.
• 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:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions, created with roxygen2.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.

Publication options

• 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

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

[ropensci-review-bot]

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

[ropensci-review-bot]

🚀

Editor check started

👋

[ropensci-review-bot]

Checks for daiquiri (v0.7.1)

git hash: c316aeb1

• ✔️ Package name is available
• ✔️ has a 'codemeta.json' file.
• ✔️ has a 'contributing' file.
• ✔️ uses 'roxygen2'.
• ✔️ 'DESCRIPTION' has a URL field.
• ✔️ 'DESCRIPTION' has a BugReports field.
• ✔️ Package has at least one HTML vignette
• ✖️ These functions do not have examples: [daiquiri].
• ✔️ Package has continuous integration checks.
• ✖️ Package coverage failed
• ✔️ R CMD check found no errors.
• ✔️ 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:

1. 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:

1. cyclocomp
2. 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 ✖️ have been resolved.

[phuongquan]

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.

[mpadge]

@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.

[phuongquan]

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

[emilyriederer]

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.

[emilyriederer]

@ropensci-review-bot assign @maurolepore as editor

[emilyriederer]

@ropensci-review-bot assign @maurolepore as editor

[ropensci-review-bot]

Assigned! @maurolepore is now the editor

[maurolepore]

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.