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algorithm improvement

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dr. M.S. (Matthijs) Berends
2018-09-14 10:31:21 +02:00
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NEWS.md
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@ -4,7 +4,23 @@
* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
* Extra function `count_df` (which works like `portion_df`) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
* Functions `as.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release.
* Functions `as.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The `as.mo` function determines microbial IDs using Artificial Intelligence (AI):
```r
as.mo("E. coli")
# [1] ESCCOL
as.mo("MRSA")
# [1] STAAUR
as.mo("S group A")
# [1] STCGRA
```
And with great speed too - on a quite regular Linux server from 2007 it takes us 0.009 seconds to transform 25,000 items:
```r
thousands_of_E_colis <- rep("E. coli", 25000)
microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s")
# Unit: seconds
# min median max neval
# 0.00861352 0.008774335 0.01952958 100
```
* Renamed all previous references to `bactid` to `mo`, like:
* Column names inputs of `EUCAST_rules`, `first_isolate` and `key_antibiotics`
* Column names of datasets `microorganisms` and `septic_patients`