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more unit tests
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NEWS.md
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NEWS.md
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# 0.2.0
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#### New
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* Full support for Windows, Linux and macOS
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* Full support for old R versions, only R-3.0.0 (April 2013) or later is needed
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* Function `guess_bactid` to determine the ID of a microorganism based on genus/species or known abbreviations like MRSA
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* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS
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* Function `MDRO` to determine Multi Drug Resistant Organisms (MDRO) with support for country-specific guidelines. Suggest your own via [this link](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E). Functions `BRMO` and `MRGN` are wrappers for Dutch and German guidelines, respectively
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* New print format for tibbles and data.tables
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#### Changed
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* Support for old R versions, only R-3.0.0 (April 2013) or later is needed
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* Renamed dataset `ablist` to `antibiotics`
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* Renamed dataset `bactlist` to `microorganisms`
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* Added more microorganisms to `bactlist`
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@ -50,6 +50,7 @@
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#' @export
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#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
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#' @return A vector to add to table, see Examples.
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#' @source Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
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#' @examples
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#' # septic_patients is a dataset available in the AMR package
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#' ?septic_patients
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15
README.md
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README.md
@ -8,12 +8,15 @@ This R package was created for academic research by PhD students of the Faculty
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## Why this package?
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This R package contains functions to make microbiological, epidemiological data analysis easier. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
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With AMR you can also apply EUCAST rules to isolates, identify first isolates of every patient, translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`), or predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function.
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With `AMR` you can also:
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* Conduct AMR analysis with the `rsi()` function, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`) to calculate the resistance percentages of different antibiotic columns of a table.
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* Predict antimicrobial resistance for the nextcoming years with the `rsi_predict()` function
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* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules()` function
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* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate()` function
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* Translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) with the `abname()` function
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With the `MDRO` function (abbreviation of mutli-drug resistant organisms), you can check your isolates for exceptional resistance with country-specific guidelines. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
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For regular AMR analysis, the `rsi` function can be used. This function als works with the `dplyr` package (e.g. in conjunction with `summarise`) to calculate the resistance percentages of different antibiotic columns of a table.
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This package contains an example data set `septic_patients`, consisting of 2000 isolates from anonymised septic patients between 2001 and 2017.
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## How to get it?
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@ -32,7 +35,7 @@ This package is available on CRAN and also here on GitHub.
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- `install.packages("AMR")`
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- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
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- (Exploratory.io costs $40/month, but is free for students and teachers; if you have an `@umcg.nl` or `@rug.nl` email address, [click here to enroll](https://exploratory.io/plan?plan=Community))
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- (Exploratory.io costs $40/month but the somewhat limited Community Plan is free for students and teachers, [click here to enroll](https://exploratory.io/plan?plan=Community))
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- Start the software and log in
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- Click on your username at the right hand side top
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- Click on `R Packages`
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@ -276,7 +279,7 @@ bactlist # A tibble: 2,507 x 10
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<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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<sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands
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<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands
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## Copyright
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[![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)
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@ -287,6 +290,8 @@ This R package is licensed under the [GNU General Public License (GPL) v2.0](htt
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- May be used for private purposes
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- May **not** be used for patent purposes
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- May be modified, although:
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- Modifications **must** be released under the same license when distributing the package
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\name{first_isolate}
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\alias{first_isolate}
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\title{Determine first (weighted) isolates}
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\source{
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Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
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}
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\usage{
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first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
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col_testcode = NA, col_specimen = NA, col_icu = NA,
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info = FALSE)), 1960)
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# septic_patients contains 1962 out of 2000 first *weighted* isolates
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#septic_ptns$keyab <- suppressWarnings(key_antibiotics(septic_ptns))
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expect_equal(
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suppressWarnings(sum(
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first_isolate(tbl = septic_patients %>% mutate(keyab = key_antibiotics(.)),
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info = TRUE))),
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1962)
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# septic_patients contains 1733 out of 2000 first non-ICU isolates
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expect_equal(
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sum(
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first_isolate(septic_patients, col_bactid = "bactid", col_date = "date", col_patient_id = "patient_id", col_icu = "ward_icu", info = TRUE, icu_exclude = TRUE)),
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1733
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)
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# set 1500 random observations to be of specimen type 'Urine'
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random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
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expect_lt(sum(
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context("misc.R")
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test_that("`like` works", {
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expect_true("test" %like% "^t")
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expect_true(suppressWarnings("test" %like% c("^t", "^s")))
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expect_true("test" %like% "test")
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expect_true("test" %like% "TEST")
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expect_true(as.factor("test") %like% "TEST")
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test_that("tibble printing works", {
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library(dplyr)
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library(data.table)
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expect_output(print(starwars))
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expect_output(print(starwars %>% group_by(homeworld, gender)))
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expect_output(print(starwars %>% as.data.table(), print.keys = TRUE))
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expect_output(print(septic_patients))
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})
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