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(v1.6.0.9044) betalactams() selector

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dr. M.S. (Matthijs) Berends
2021-05-18 00:53:04 +02:00
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NEWS.md
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# `AMR` 1.6.0.9043
## <small>Last updated: 17 May 2021</small>
# `AMR` 1.6.0.9044
## <small>Last updated: 18 May 2021</small>
### New
* Function `custom_eucast_rules()` that brings support for custom AMR rules in `eucast_rules()`
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* The `first_isolate()` function can now take a vector of values for `col_keyantibiotics` and can have an episode length of `Inf`
* Since the phenotype-based method is the new default, `filter_first_isolate()` renders the `filter_first_weighted_isolate()` function redundant. For this reason, `filter_first_weighted_isolate()` is now deprecated.
* The documentation of the `first_isolate()` and `key_antimicrobials()` functions has been completely rewritten.
* Added `ggplot()` method for `resistance_predict()`
* Function `betalactams()` as additional antbiotic column selector and function `filter_betalactams()` as additional antbiotic column filter. The group of betalactams consists of all carbapenems, cephalosporins and penicillins.
* A `ggplot()` method for `resistance_predict()`
### Changed
* Custom MDRO guidelines (`mdro()`, `custom_mdro_guideline()`):
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* Updated `skimr::skim()` usage for MIC values to also include 25th and 75th percentiles
* Fix for plotting missing MIC/disk diffusion values
* Updated join functions to always use `dplyr` join functions if the `dplyr` package is installed - now also preserving grouped variables
* Fix for filtering on antibiotic classes (such as `filter_cephalosporins()`)
* Fix for filtering on antibiotic classes (such as `filter_cephalosporins()`), which now also supports dplyr groups
* Antibiotic class selectors (such as `cephalosporins()`) now maintain the column order from the original data
### Other
* All unit tests are now processed by the `tinytest` package, instead of the `testthat` package. The `testthat` package unfortunately requires tons of dependencies that are also heavy and only usable for recent R versions, defeating the purpose to test our package under less recent R versions. On the contrary, the `tinytest` package is very lightweight and dependency-free.