*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island's Atlantic Veterinary College](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
* Function `as.sir()` now has extensive support for animal breakpoints from CLSI. Use `breakpoint_type = "animal"` and set the `host` argument to a variable that contains animal species names.
* The `clinical_breakpoints` data set contains all these breakpoints, and can be downloaded on our [download page](https://msberends.github.io/AMR/articles/datasets.html).
* The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well.
*`ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q")
*`ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
* EUCAST 2024 and CLSI 2024 are now supported, by adding all of their over 4,000 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 is now the new default guideline for all MIC and disks diffusion interpretations.
*`as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Currently, the `clinical_breakpoints` data set contains 24 breakpoints that can return the value "SDD" instead of "I".
* New function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
* New function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
* Microbiological taxonomy (`microorganisms` data set) updated to June 2024, with some exciting new features:
* Added MycoBank as the primary taxonomic source for fungi
* The `microorganisms` data set now contains additional columns `mycobank`, `mycobank_parent`, and `mycobank_renamed_to`
* New function `mo_mycobank()` to get the MycoBank record number, analogous to existing functions `mo_lpsn()` and `mo_gbif()`
* We've welcomed over 2,000 records from 2023, over 900 from 2024, and many thousands of new fungi
* The `as.mo()` function now includes a new argument, `only_fungi` (TRUE/FALSE), which limits the results to fungi only. Normally, bacteria are often prioritised by the algorithm, but setting `only_fungi = TRUE` ensures only fungi are returned.
* You can also set this globally using the new R option `AMR_only_fungi`, e.g., `options(AMR_only_fungi = TRUE)`.
* New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.
* It is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users.
* Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
* To get quantitative values, `as.double()` on a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
* New argument `formatting_type` to set any of the 12 options for the formatting of all 'cells'. This defaults to `10`, changing the output of antibiograms to cells with `5% (15/300)` instead of the previous standard of just `5`.
* For this reason, `add_total_n` is now `FALSE` at default since the denominators are added to the cells
* The `ab_transform` argument now defaults to `"name"`, displaying antibiotic column names instead of codes
* Added "clindamycin inducible screening" as `CLI1`. Since clindamycin is a lincosamide, the antibiotic selector `lincosamides()` now contains the argument `only_treatable = TRUE` (similar to other antibiotic selectors that contain non-treatable drugs)
* Added Amorolfine (`AMO`, D01AE16), which is now also part of the `antifungals()` selector
* Added selectors `nitrofurans()` and `rifamycins()`
* When using antibiotic selectors such as `aminoglycosides()` that exclude non-treatable drugs like gentamicin-high, the function now always returns a warning that these can be included using `only_treatable = FALSE`
* Added new argument `keep_operators` to `as.mic()`. This can be `"all"` (default), `"none"`, or `"edges"`. This argument is also available in the new `rescale_mic()` and `scale_*_mic()` functions.
* Comparisons of MIC values are now more strict. For example, `>32` is higher than (and never equal to) `32`. Thus, `as.mic(">32") == as.mic(32)` now returns `FALSE`, and `as.mic(">32") > as.mic(32)` now returns `TRUE`.
* Sorting of MIC values (using `sort()`) was fixed in the same manner; `<0.001` now gets sorted before `0.001`, and `>0.001` gets sorted after `0.001`.
* Greatly improved `vctrs` integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as `dplyr`'s `bind_rows()`, `rowwise()` and `c_across()` are now supported for e.g. columns of class `mic`. Despite this, this `AMR` package is still zero-dependent on any other package, including `dplyr` and `vctrs`.
* Improved overall algorithm of `as.mo()` for better performance and accuracy. Specifically, more weight is given to genus and species combinations in cases where the subspecies is miswritten, so that the result will be the correct genus and species.
