Submission: rio

[chainsawriot]

Submitting Author Name: Chung-hong Chan
Submitting Author Github Handle: @chainsawriot
Other Package Authors Github handles: (comma separated, delete if none) @leeper
Repository: https://github.com/chainsawriot/rio
Version submitted: 0.5.30
Submission type: Standard
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: rio
Type: Package
Title: A Swiss-Army Knife for Data I/O
Version: 0.5.30
Authors@R: c(person("Jason", "Becker", role = "ctb", email = "jason@jbecker.co"),
             person("Chung-hong", "Chan", role = c("aut", "cre"), email = "chainsawtiney@gmail.com",
	     	     comment = c(ORCID = "0000-0002-6232-7530")),
             person("Geoffrey CH", "Chan", role = "ctb", email = "gefchchan@gmail.com"),
             person("Thomas J.", "Leeper",
                    role = "aut", 
                    email = "thosjleeper@gmail.com",
                    comment = c(ORCID = "0000-0003-4097-6326")),
             person("Christopher", "Gandrud", role = "ctb"),
             person("Andrew", "MacDonald", role = "ctb"),
             person("Ista", "Zahn", role = "ctb"),
             person("Stanislaus", "Stadlmann", role = "ctb"),
             person("Ruaridh", "Williamson", role = "ctb", email = "ruaridh.williamson@gmail.com"),
             person("Patrick", "Kennedy", role = "ctb"),
             person("Ryan", "Price", email = "ryapric@gmail.com", role = "ctb"),
             person("Trevor L", "Davis", email = "trevor.l.davis@gmail.com", role = "ctb"),
             person("Nathan", "Day", email = "nathancday@gmail.com", role = "ctb"),
             person("Bill", "Denney",
                    email="wdenney@humanpredictions.com",
                    role="ctb",
                    comment=c(ORCID="0000-0002-5759-428X")),
             person("Alex", "Bokov", email = "alex.bokov@gmail.com", role = "ctb",
                    comment=c(ORCID="0000-0002-0511-9815"))
             )
Description: Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data structure from the file extension, reasonable defaults are used for
    data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is
    natively supported (including from SSL/HTTPS), compressed files can be read
    directly without explicit decompression, and fast import packages are used where
    appropriate. An additional convenience function, 'convert()', provides a simple
    method for converting between file types.
URL: https://github.com/chainsawriot/rio
BugReports: https://github.com/chainsawriot/rio/issues
Depends:
    R (>= 3.6)
Imports:
    tools,
    stats,
    utils,
    foreign,
    haven (>= 1.1.2),
    curl (>= 0.6),
    data.table (>= 1.9.8),
    readxl (>= 0.1.1),
    openxlsx,
    tibble
Suggests:
    datasets,
    bit64,
    testthat,
    knitr,
    magrittr,
    arrow,
    clipr,
    feather,
    fst,
    hexView,
    jsonlite,
    pzfx,
    readODS (>= 1.6.4),
    readr,
    rmarkdown,
    rmatio,
    xml2 (>= 1.2.0),
    yaml
License: GPL-2
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.3

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):

This package is for loading and saving data from either files or urls.

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

Probably all scientific disciplines that involve dealing with data files.

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

As far as I know there are four: reader (not readr), io, ImportExport, and SchemaOnRead. The current package is probably the most used.

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

Yes

• If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

No. I am sorry.

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

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.

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]

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Editor check started

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[ropensci-review-bot]

Checks for rio (v0.5.30)

git hash: daf6cd15

• ✔️ Package is already on CRAN.
• ✖️ does not have a 'codemeta.json' file.
• ✔️ has a 'contributing' file.
• ✖️ The following function has no documented return value: [characterize]
• ✔️ 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: [arg_reconcile, .import, get_ext, install_formats].
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 87.4%.
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.
• 👀 Function names are duplicated in other packages

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

(Checks marked with 👀 may be optionally addressed.)

Package License: GPL-2

---

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	494
internal	rio	33
internal	grDevices	4
internal	graphics	3
internal	methods	1
imports	utils	23
imports	haven	11
imports	tools	8
imports	openxlsx	5
imports	stats	3
imports	foreign	3
imports	data.table	3
imports	curl	2
imports	readxl	1
imports	tibble	1
suggests	xml2	17
suggests	clipr	3
suggests	pzfx	3
suggests	rmatio	3
suggests	feather	2
suggests	fst	2
suggests	readODS	2
suggests	readr	2
suggests	arrow	1
suggests	jsonlite	1
suggests	datasets	NA
suggests	bit64	NA
suggests	testthat	NA
suggests	knitr	NA
suggests	magrittr	NA
suggests	hexView	NA
suggests	rmarkdown	NA
suggests	yaml	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

file (148), which (46), list (30), c (29), lapply (16), names (14), for (11), attributes (10), do.call (9), paste0 (9), seq_along (9), args (7), row.names (7), invisible (6), length (6), unlist (6), drop (5), sapply (5), try (5), url (5), as.character (4), format (4), nchar (4), tempfile (4), basename (3), col (3), new.env (3), raw (3), regexpr (3), regmatches (3), rep (3), return (3), seq_len (3), strsplit (3), unclass (3), class (2), cumsum (2), formals (2), gettext (2), grep (2), gsub (2), levels (2), max (2), nrow (2), readLines (2), setdiff (2), sort (2), switch (2), table (2), tolower (2), unique (2), alist (1), as.environment (1), as.numeric (1), attr (1), by (1), cbind.data.frame (1), comment (1), dump (1), duplicated (1), getOption (1), getwd (1), if (1), is.na (1), labels (1), library (1), match.arg (1), match.call (1), ncol (1), paste (1), quote (1), read.dcf (1), readBin (1), rownames (1), sink (1), sprintf (1), structure (1), sub (1), substitute (1), system.file (1), T (1)

rio

import (4), twrap (3), arg_reconcile (2), doone (2), export (2), extract_html_row (2), find_compress (2), get_ext (2), uninstalled_formats (2), characterize (1), characterize.data.frame (1), characterize.default (1), compress_out (1), convert (1), convert_google_url (1), export_delim (1), factorize (1), factorize.data.frame (1), factorize.default (1), gather_attrs (1), standardize_attributes (1)

utils

data (5), unzip (5), untar (4), type.convert (2), zip (2), head (1), packageName (1), read.fortran (1), tar (1), write.table (1)

xml2

read_xml (4), xml_add_child (4), read_html (3), as_list (2), xml_find_all (2), xml_attrs (1), xml_children (1)

haven

write_sav (4), write_dta (2), write_sas (2), write_xpt (2), read_sas (1)

tools

file_ext (5), file_path_sans_ext (3)

openxlsx

addWorksheet (1), getSheetNames (1), loadWorkbook (1), saveWorkbook (1), write.xlsx (1)

grDevices

bmp (1), jpeg (1), png (1), tiff (1)

clipr

read_clip (1), read_clip_tbl (1), write_clip (1)

data.table

rbindlist (2), fwrite (1)

foreign

read.dta (1), read.systat (1), write.dbf (1)

graphics

title (2), text (1)

pzfx

read_pzfx (2), write_pzfx (1)

rmatio

write.mat (2), read.mat (1)

stats

setNames (3)

curl

curl_fetch_memory (1), parse_headers (1)

feather

read_feather (1), write_feather (1)

fst

read.fst (1), write.fst (1)

readODS

read_ods (1), write_ods (1)

readr

fwf_empty (1), read_fwf (1)

arrow

write_parquet (1)

jsonlite

fromJSON (1)

methods

is (1)

readxl

excel_sheets (1)

tibble

as_tibble (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 22 files) and
• 2 authors
• 1 vignette
• no internal data file
• 10 imported packages
• 21 exported functions (median 15 lines of code)
• 202 non-exported functions in R (median 4 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	22	83.6
files_vignettes	1	68.4
files_tests	54	99.3
loc_R	1513	78.2
loc_vignettes	182	46.0
loc_tests	1196	89.2
num_vignettes	1	64.8
n_fns_r	223	91.2
n_fns_r_exported	21	68.8
n_fns_r_not_exported	202	93.3
n_fns_per_file_r	5	71.4
num_params_per_fn	2	11.9
loc_per_fn_r	5	8.1
loc_per_fn_r_exp	15	35.6
loc_per_fn_r_not_exp	4	9.3
rel_whitespace_R	8	57.4
rel_whitespace_vignettes	38	50.9
rel_whitespace_tests	19	86.4
doclines_per_fn_exp	50	63.0
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	88	77.1

---

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

(There do not appear to be any)

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
6010120891	pages build and deployment	failure	57d3a2	5	2023-08-29
6012061439	R-CMD-check	success	fd7053	6	2023-08-29
6012061436	test-coverage	success	fd7053	18	2023-08-29

---

3b. goodpractice results

R CMD check with rcmdcheck

rcmdcheck found no errors, warnings, or notes

Test coverage with covr

Package coverage: 87.35

Cyclocomplexity with cyclocomp

The following functions have cyclocomplexity >= 15:

function	cyclocomplexity
import_list	31
import	24
arg_reconcile	20
import_delim	18
set_class	17

Static code analyses with lintr

lintr found the following 314 potential issues:

message	number of times
Avoid 1:nrow(...) expressions, use seq_len.	2
Avoid changing the working directory, or restore it in on.exit	3
Avoid library() and require() calls in packages	4
Avoid using sapply, consider vapply instead, that's type safe	8
Lines should not be more than 80 characters.	297

---

4. Other Checks

Details of other checks (click to open)

✖️ The following 4 function names are duplicated in other packages:

• • convert from AquaEnv, ascii, breakaway, cabootcrs, CHNOSZ, convertr, coreCT, DDIwR, equateIRT, hablar, khroma, nCov2019, phenopix, qtl, quanteda, rMIDAS, scan, StratigrapheR, tidygraph, tis, wavethresh
• • export from admisc, aLFQ, box, box, bruceR, campsismod, crestr, EviewsR, flux, fsbrain, gm, grainscape, inTextSummaryTable, job, kimisc, Momocs, Morpho, mpm, pitchRx, scan, seewave, soc.ca, strvalidator, tipsae, wpa
• • factorize from admisc, conf.design, elliptic, Epi, gmp, labdsv, lme4, mosaic, QCApro, RcmdrPlugin.KMggplot2, rminer, sfsmisc
• • import from act, aLFQ, ambiorix, backports, bruceR, EviewsR, fSRM, importar, isqg, MALDIquantForeign, NMproject, openair, reticulate, reticulate, rTorch, strvalidator, tensorflow

---

Package Versions

package	version
pkgstats	0.1.3.7
pkgcheck	0.1.2.1

---

Editor-in-Chief Instructions:

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

[chainsawriot]

@ropensci-review-bot help

[ropensci-review-bot]

Hello @chainsawriot, here are the things you can ask me to do:

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@ropensci-review-bot submit response <AUTHOR_RESPONSE_URL>

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[chainsawriot]

@ropensci-review-bot check package

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Thanks, about to send the query.

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[chainsawriot]

@ropensci My local check with pkgcheck showed that there should be no more items marked with x, except the optional point on duplicated function names. However, as a decade old package it is probably harmful in terms of computational reproducibility to change those generic function names now: import, export, convert and factorize.

[chainsawriot]

httr::HEAD("https://badges.ropensci.org/605_status.svg")
#> Response [https://badges.ropensci.org/605_status.svg]
#>   Date: 2023-08-30 15:32
#>   Status: 404
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#> <EMPTY BODY>

^{Created on 2023-08-30 with reprex v2.0.2}

[noamross]

Thank you for this submission @chainsawriot! I realize the last response from the bot is an error, as a badge should not be generated or checked for until after an editor has approved moving forward with the process.

I believe rio is out of scope for us. Per the package descriptions in our Aims and Scope, retrieval, extraction, or munging categories should be specific to "data sources / topics", "aid in retrieving data from unstructured sources such as text, images and PDFs, as well as parsing scientific data types and outputs from scientific equipment", or "focus on tools for handling data in specific scientific formats generated from scientific workflows or exported from scientific instruments." The reason for this is that it is hard to have objective reviews for where we draw on relevant field expertise with highly general/swiss army tools. The latter are more likely to have a lot of users that provide feedback so need the review process less. I would recommend JOSS as a venue for reviewing and publishing rio.