qualR: An R package to download Sao Paulo and Rio de Janeiro air pollution data

[quishqa]

Date accepted: 2022-03-08
Submitting Author Name: Mario Gavidia-Calderón
Submitting Author Github Handle: @quishqa
Other Package Authors Github handles: @Schuch666, @mftandra
Repository: https://github.com/quishqa/qualR
Version submitted:0.9.5
Submission type: Standard
Editor: @ldecicco-USGS
Reviewers: @beatrizmilz, @kauedesousa

Due date for @beatrizmilz: 2021-12-01

Due date for @kauedesousa: 2021-12-02
Archive: TBD
Version accepted: TBD

---

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

Package: qualR
Title: An R package to download Sao Paulo and Rio de Janeiro air pollution data
Version: 0.9.5
Authors@R: c(
    person(given = "Mario",
           family = "Gavidia-Calderón",
           role = c("aut", "cre"),
           email = "mario.calderon@iag.usp.br",
           comment = c(ORCID = "0000-0002-7371-1116")),
    person(given = "Maria de Fatima",
           family = "Andrade",
           role = c("ctb", "ths"),
           email = "maria.andrade@iag.usp.br",
           comment = c(ORCID = "0000-0001-5351-8311")),
    person(given = "Daniel",
           family = "Schuch",
           role = c("aut","ctb"),
           email = "d.schuch@northeastern.edu",
           comment = c(ORCID = "0000-0001-5977-4519")))
Description: A package to download information from CETESB QUALAR and
    MonitorAr systems. It contains function to download different parameters, a set of
    criteria pollutants and the most frequent meteorological parameters used in
    air quality data analysis and air quality model evaluation.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Imports: 
    XML,
    httr,
    jsonlite
URL: https://github.com/quishqa/qualR
BugReports: https://github.com/quishqa/qualR/issues
Suggests:
    covr,
    testthat (>= 3.0.0)
Depends: 
    R (>= 3.5.0)
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
  • 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):
  Sao Paulo data is available through CETESB QUALAR System which limits the download to one parameter for one air quality station for one year, Rio de Janeiro is available through data.rio.API which is not so user-friendly. qualR downloads multiple parameters for different air quality stations and produces completed ready-to-use data frames (missing hours are padded out with NA) with a date column in POSIXct type that allows temporal aggregation and compatibility with openair package.

• Who is the target audience and what are scientific applications of this package?
  Any researchers that work with air quality and weather data of Sao Paulo and Rio de Janeiro: Meteorologists, Epidemiologists, Environmental engineers, postgraduates students that work in air quality modeling and field measurements campaigns, also stakeholders and the community. This packages facilitates the retrival of Sao Paulo and Rio de Janeiro air quality data, facilitates exploratory data analysis, and enhance code reproducibility.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
  koffing retrieves Sao Paulo State air quality data. qualR offers more functionalities (i.e. allows retrieve many parameters from one air quality station, accepts pollutants abbreviations and air quality station names in functions parameters, etc), and returns complete datasets ready to use (no missing hours, concentration in numeric format). qualR also include Rio de Janeiro city and the location of each air quality stations. qualR is more user friendly and it is actively maintainded.

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

• 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.

Technical checks

Confirm each of the following by checking the box.

• I have read the guide for authors and rOpenSci packaging guide.

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 qualR (v0.9.5)

git hash: 6ca2ad12

• ✔️ Package name is available
• ✔️ has a 'CITATION' file.
• ✔️ has a 'codemeta.json' file.
• ✖️ does not have a 'contributing' file.
• ✔️ uses 'roxygen2'.
• ✔️ 'DESCRIPTION' has a URL field.
• ✔️ 'DESCRIPTION' has a BugReports field.
• ✖️ Package has at no HTML vignettes
• ✖️ These functions do not have examples: [cetesb_aqs.Rd].
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 100%.
• ✔️ 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: MIT + file LICENSE

---

1. 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 12 files) and
• 2 authors
• no vignette
• 4 internal data files
• 3 imported packages
• 9 exported functions (median 35 lines of code)
• 13 non-exported functions in R (median 33 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

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	12	62.6
files_vignettes	0	0.0	TRUE
files_tests	9	87.3
loc_R	495	44.0
loc_tests	61	26.1
num_vignettes	0	0.0	TRUE
data_size_total	4165	65.9
data_size_median	667	61.4
n_fns_r	22	22.5
n_fns_r_exported	9	38.9
n_fns_r_not_exported	13	19.3
n_fns_per_file_r	1	8.3
num_params_per_fn	7	85.6
loc_per_fn_r	34	88.5
loc_per_fn_r_exp	35	70.3
loc_per_fn_r_not_exp	33	89.7
rel_whitespace_R	24	54.8
rel_whitespace_tests	41	75.1
doclines_per_fn_exp	41	51.1
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	34	53.0

---

1a. Network visualisation

Interactive network visualisation of calls between objects in package can be viewed by clicking here

---

2. goodpractice and other checks

Details of goodpractice and other checks (click to open)

3a. Continuous Integration Badges

[]( )

---

3b. goodpractice results

R CMD check with rcmdcheck

R CMD check generated the following note:

1. checking data for non-ASCII characters ... NOTE
   Note: found 40 marked UTF-8 strings

R CMD check generated the following check_fail:

1. rcmdcheck_non_ascii_characters_in_data

Test coverage with covr

Package coverage: 100

Cyclocomplexity with cyclocomp

No functions have cyclocomplexity >= 15

Static code analyses with lintr

lintr found the following 54 potential issues:

message	number of times
Avoid changing the working directory, or restore it in on.exit	2
Lines should not be more than 80 characters.	52

---

Package Versions

package	version
pkgstats	0.0.2.16
pkgcheck	0.0.2.86

---

Editor-in-Chief Instructions:

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

[quishqa]

@ropensci-review-bot help

[ropensci-review-bot]

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

# List all available commands
@ropensci-review-bot help

# Show our Code of Conduct
@ropensci-review-bot code of conduct

[quishqa]

Hi!
All items marked as ✖️ have been resolved. We are working on fixing goodpractice checks.

[ldecicco-USGS]

@ropensci-review-bot check package

[ropensci-review-bot]

Thanks, about to send the query.

[ropensci-review-bot]

🚀

Editor check started

👋

[ropensci-review-bot]

Checks for qualR (v0.9.5)

git hash: ca28ae7b

• ✔️ Package name is available
• ✔️ has a 'CITATION' file.
• ✔️ 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
• ✔️ All functions have examples.
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 100%.
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.

Package License: MIT + file LICENSE

---

1. 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 12 files) and
• 2 authors
• 1 vignette
• 4 internal data files
• 3 imported packages
• 9 exported functions (median 35 lines of code)
• 13 non-exported functions in R (median 33 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

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	12	62.6
files_vignettes	0	0.0	TRUE
files_tests	9	87.3
loc_R	509	45.0
loc_tests	65	27.3
num_vignettes	1	60.7
data_size_total	4165	65.9
data_size_median	667	61.4
n_fns_r	22	22.5
n_fns_r_exported	9	38.9
n_fns_r_not_exported	13	19.3
n_fns_per_file_r	1	8.3
num_params_per_fn	7	85.6
loc_per_fn_r	34	88.5
loc_per_fn_r_exp	35	70.3
loc_per_fn_r_not_exp	33	89.7
rel_whitespace_R	23	54.8
rel_whitespace_tests	38	75.1
doclines_per_fn_exp	42	52.5
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	34	53.0

---

1a. Network visualisation

Interactive network visualisation of calls between objects in package can be viewed by clicking here

---

2. goodpractice and other checks

Details of goodpractice and other checks (click to open)

3a. Continuous Integration Badges

[]( )

---

3b. goodpractice results

R CMD check with rcmdcheck

R CMD check generated the following note:

1. checking data for non-ASCII characters ... NOTE
   Note: found 40 marked UTF-8 strings

R CMD check generated the following check_fail:

1. rcmdcheck_non_ascii_characters_in_data

Test coverage with covr

Package coverage: 100

Cyclocomplexity with cyclocomp

No functions have cyclocomplexity >= 15

Static code analyses with lintr

lintr found the following 4 potential issues:

message	number of times
Avoid changing the working directory, or restore it in on.exit	4

---

Package Versions

package	version
pkgstats	0.0.2.16
pkgcheck	0.0.2.86

---

Editor-in-Chief Instructions:

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

[ldecicco-USGS]

Thanks @quishqa ! The package is looking great. I'll be taking the editor role.

@ropensci-review-bot assign @ldecicco-USGS as editor

[ldecicco-USGS]

@ropensci-review-bot assign @ldecicco-USGS as editor