Description
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/mantis
Version submitted: 0.3.0
Submission type: Standard
Editor: @beatrizmilz
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
- Paste the full DESCRIPTION file inside a code block below:
Package: mantis
Type: Package
Title: Multiple Time Series Scanner
Version: 0.3.0
Authors@R: c(
person(c("T.", "Phuong"), "Quan", email = "[email protected]",
role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8566-1817")),
person(family = "University of Oxford", role = "cph"),
person(family = "National Institute for Health Research (NIHR)", role = "fnd")
)
Description: Generate interactive html reports that enable quick visual review of multiple
related time series stored in a data frame. For static datasets, this can help to identify any temporal
artefacts that may affect the validity of subsequent analyses. For live data feeds,
regularly scheduled reports can help to pro-actively identify data feed problems
or unexpected trends that may require action. The reports are self-contained and shareable
without a web server.
URL: https://github.com/phuongquan/mantis,
https://phuongquan.github.io/mantis/
BugReports: https://github.com/phuongquan/mantis/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends:
R (>= 4.1.0)
Imports:
rmarkdown,
knitr,
reactable,
dplyr (>= 1.1.1),
tidyr,
dygraphs,
xts,
ggplot2,
scales,
purrr,
htmltools
Suggests:
covr,
testthat (>= 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
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
-
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
It helps to identify any temporal artefacts or unexpected trends in collections of related time series. It does this by generating html reports that enable quick visual review of the data. This is important for ensuring the validity of any subsequent analyses based on the data.
- Who is the target audience and what are scientific applications of this package?
There are two main target audiences:
-
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, increasing the quality of their studies as well as trust in the scientific process.
-
managers of live data feeds that are used as a data source for downstream analyses. Regular inspection of data that is frequently updated will help to identify any issues early and enable a timely response to rectify issues such as missing data. While a set of validation checks in a data pipeline could also work in this circumstance, the benefit of this package is the ability to check for temporal anomalies that are obvious to the human eye but that are difficult to capture programmatically.
- Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
I previously created a related package daiquiri which was accepted at ROpenSci in 2022, which creates data quality reports for temporal, record-level datasets. This new package was developed as a response to users who wanted to visually review datasets that were essentially a collection of time series in a data frame, as opposed to record-level, non-numeric data that requires aggregation, (which is what daiquiri is designed for).
There are other packages that do similar things, such as visually inspecting data frames (visdat) or running validation checks on them (assertr, pointblank), but to my knowledge there are none which assist in identifying anomalous temporal changes.
- (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
@tagthe editor you contacted.
- Explain reasons for any
pkgcheckitems which your package is unable to pass.
All passed
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.