mantis

https://github.com/phuongquan/mantis

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Host: GitHub
Owner: phuongquan
License: gpl-3.0
Language: R
Default Branch: main
Homepage: https://phuongquan.github.io/mantis/
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Created almost 3 years ago · Last pushed 10 months ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output: github_document
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# mantis


[![R-CMD-check](https://github.com/phuongquan/mantis/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/phuongquan/mantis/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/phuongquan/mantis/graph/badge.svg)](https://app.codecov.io/gh/phuongquan/mantis)
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/710_status.svg)](https://github.com/ropensci/software-review/issues/710)



The `mantis` package generates interactive reports that enable quick visual
review of multiple related time series. This can help with identification of
temporal artefacts and unexpected trends. The choice of visualisations include
interactive plots with adjustable axes and tooltips showing the individual dates
and values, as well as static heatmap and scatter plots for more lightweight
applications. E.g.



The resulting html reports are self-contained and shareable without a web server.

`mantis` is designed for use on data that is essentially a collection of time
series in a data frame, where you have already decided which values you are
interested in reviewing. If you want to check more generally for temporal
changes in record-level, non-numeric data, then check out our sister package
['daiquiri'](https://ropensci.github.io/daiquiri/index.html).

## Why should I use it?

We anticipate two main target users:

1. Researchers who analyse data from large, temporal datasets, particularly
routinely-collected data such as electronic health records. Included as part of
the initial analysis stage, it will help you to check for any temporal biases in
your datasets before embarking on your main analyses, therefore improving the
reliability of your study.

2. 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.

## Installation

```{r, eval = FALSE}
# install from CRAN
install.packages("mantis")

# or install the latest github release
# install dependencies first
install.packages(c(
  "remotes",
  "rmarkdown",
  "knitr",
  "reactable",
  "dplyr",
  "tidyr",
  "dygraphs",
  "xts",
  "ggplot2",
  "scales",
  "purrr",
  "htmltools"
))
remotes::install_github("phuongquan/mantis@v0.4.3", dependencies = FALSE)

# or install the current development version
remotes::install_github("phuongquan/mantis", dependencies = FALSE)
```

## Usage

```{r}
library(mantis)

# this example data frame contains numbers of antibiotic prescriptions 
# in long format
data("example_prescription_numbers")

head(example_prescription_numbers)
```

```{r, eval = FALSE}
# create a report in the working directory, with one tab per Location
mantis_report(
  df = example_prescription_numbers,
  file = "example_prescription_numbers_report.html",
  inputspec = inputspec(
    timepoint_col = "PrescriptionDate",
    item_cols = c("Location", "Antibiotic", "Spectrum"),
    value_col = "NumberOfPrescriptions",
    tab_col = "Location",
    timepoint_unit = "day"
  )
)
```

Example reports and more detailed guidance can be found in the [package website](https://phuongquan.github.io/mantis/index.html).


## Acknowledgements

This work was supported by the National Institute for Health Research Health
Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and
Antimicrobial Resistance at the University of Oxford in partnership with the UK
Health Security Agency (UKHSA) (NIHR200915), and by the NIHR Oxford Biomedical
Research Centre.


## Contributing to this package

Please report any bugs or suggestions by opening a [github
issue](https://github.com/phuongquan/mantis/issues).

Owner

Name: Phuong Quan
Login: phuongquan
Kind: user
Location: Oxford, UK
Company: University of Oxford

Repositories: 1
Profile: https://github.com/phuongquan

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cran.r-project.org: mantis

Multiple Time Series Scanner

Homepage: https://github.com/phuongquan/mantis
Documentation: http://cran.r-project.org/web/packages/mantis/mantis.pdf
License: GPL (≥ 3)
Latest release: 0.4.3
published 11 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 361 Last month

Rankings

Dependent packages count: 25.8%

Dependent repos count: 31.7%

Average: 47.7%

Downloads: 85.6%

Maintainers (1)

phuong.quan@ndm.ox.ac.uk