mirror of https://github.com/msberends/AMR.git
250 lines
13 KiB
R
Executable File
250 lines
13 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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context("mdro.R")
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test_that("mdro works", {
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skip_on_cran()
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expect_error(suppressWarnings(mdro(example_isolates, country = "invalid", col_mo = "mo", info = TRUE)))
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expect_error(suppressWarnings(mdro(example_isolates, country = "fr", info = TRUE)))
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expect_error(mdro(example_isolates, guideline = c("BRMO", "MRGN"), info = TRUE))
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expect_error(mdro(example_isolates, col_mo = "invalid", info = TRUE))
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expect_output(suppressMessages(suppressWarnings(mdro(example_isolates, info = TRUE))))
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expect_output(suppressMessages(suppressWarnings(mdro(example_isolates, "eucast3.1", info = TRUE))))
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expect_output(outcome <- suppressMessages(suppressWarnings(eucast_exceptional_phenotypes(example_isolates, info = TRUE))))
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# check class
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expect_equal(class(outcome), c("ordered", "factor"))
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expect_output(outcome <- mdro(example_isolates, "nl", info = TRUE))
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# check class
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expect_equal(class(outcome), c("ordered", "factor"))
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library(dplyr)
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# example_isolates should have these finding using Dutch guidelines
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expect_equal(as.double(table(outcome)),
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c(1970, 24, 6)) # 1970 neg, 24 unconfirmed, 6 pos
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expect_equal(brmo(example_isolates, info = FALSE),
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mdro(example_isolates, guideline = "BRMO", info = FALSE))
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# test Dutch P. aeruginosa MDRO
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expect_equal(
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as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
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cfta = "S",
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cipr = "S",
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mero = "S",
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imip = "S",
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gent = "S",
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tobr = "S",
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pita = "S"),
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guideline = "BRMO",
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col_mo = "mo",
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info = FALSE)),
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"Negative")
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expect_equal(
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as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
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cefta = "R",
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cipr = "R",
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mero = "R",
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imip = "R",
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gent = "R",
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tobr = "R",
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pita = "R"),
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guideline = "BRMO",
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col_mo = "mo",
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info = FALSE)),
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"Positive")
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# German 3MRGN and 4MRGN
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expect_equal(as.character(mrgn(
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data.frame(mo = c("E. coli", "E. coli", "K. pneumoniae", "E. coli",
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"A. baumannii", "A. baumannii", "A. baumannii",
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"P. aeruginosa", "P. aeruginosa", "P. aeruginosa"),
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PIP = c("S", "R", "R", "S",
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"S", "R", "R",
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"S", "R", "R"),
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CTX = c("S", "R", "R", "S",
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"R", "R", "R",
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"R", "R", "R"),
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IPM = c("S", "R", "S", "R",
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"R", "R", "S",
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"S", "R", "R"),
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CIP = c("S", "R", "R", "S",
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"R", "R", "R",
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"R", "S", "R"),
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stringsAsFactors = FALSE))),
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c("Negative", "4MRGN", "3MRGN", "4MRGN", "4MRGN", "4MRGN", "3MRGN", "Negative", "3MRGN", "4MRGN"))
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# MDR TB
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expect_equal(
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# select only rifampicine, mo will be determined automatically (as M. tuberculosis),
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# number of mono-resistant strains should be equal to number of rifampicine-resistant strains
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as.double(table(mdr_tb(example_isolates[, "RIF", drop = FALSE])))[2],
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count_R(example_isolates$RIF))
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sample_rsi <- function() {
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sample(c("S", "I", "R"),
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size = 5000,
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prob = c(0.5, 0.1, 0.4),
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replace = TRUE)
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}
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x <- data.frame(rifampicin = sample_rsi(),
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inh = sample_rsi(),
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gatifloxacin = sample_rsi(),
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eth = sample_rsi(),
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pza = sample_rsi(),
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MFX = sample_rsi(),
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KAN = sample_rsi())
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expect_gt(n_distinct(mdr_tb(x)), 2)
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# check the guideline by Magiorakos et al. (2012), the default guideline
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stau <- data.frame(mo = c("S. aureus", "S. aureus", "S. aureus", "S. aureus"),
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GEN = c("R", "R", "S", "R"),
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RIF = c("S", "R", "S", "R"),
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CPT = c("S", "R", "R", "R"),
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OXA = c("S", "R", "R", "R"),
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CIP = c("S", "S", "R", "R"),
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MFX = c("S", "S", "R", "R"),
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SXT = c("S", "S", "R", "R"),
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FUS = c("S", "S", "R", "R"),
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VAN = c("S", "S", "R", "R"),
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TEC = c("S", "S", "R", "R"),
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TLV = c("S", "S", "R", "R"),
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TGC = c("S", "S", "R", "R"),
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CLI = c("S", "S", "R", "R"),
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DAP = c("S", "S", "R", "R"),
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ERY = c("S", "S", "R", "R"),
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LNZ = c("S", "S", "R", "R"),
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CHL = c("S", "S", "R", "R"),
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FOS = c("S", "S", "R", "R"),
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QDA = c("S", "S", "R", "R"),
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TCY = c("S", "S", "R", "R"),
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DOX = c("S", "S", "R", "R"),
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MNO = c("S", "S", "R", "R"),
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stringsAsFactors = FALSE)
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expect_equal(as.integer(mdro(stau)), c(1:4))
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expect_s3_class(mdro(stau, verbose = TRUE), "data.frame")
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ente <- data.frame(mo = c("Enterococcus", "Enterococcus", "Enterococcus", "Enterococcus"),
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GEH = c("R", "R", "S", "R"),
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STH = c("S", "R", "S", "R"),
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IPM = c("S", "R", "R", "R"),
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MEM = c("S", "R", "R", "R"),
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DOR = c("S", "S", "R", "R"),
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CIP = c("S", "S", "R", "R"),
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LVX = c("S", "S", "R", "R"),
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MFX = c("S", "S", "R", "R"),
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VAN = c("S", "S", "R", "R"),
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TEC = c("S", "S", "R", "R"),
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TGC = c("S", "S", "R", "R"),
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DAP = c("S", "S", "R", "R"),
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LNZ = c("S", "S", "R", "R"),
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AMP = c("S", "S", "R", "R"),
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QDA = c("S", "S", "R", "R"),
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DOX = c("S", "S", "R", "R"),
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MNO = c("S", "S", "R", "R"),
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stringsAsFactors = FALSE)
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expect_equal(as.integer(mdro(ente)), c(1:4))
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expect_s3_class(mdro(ente, verbose = TRUE), "data.frame")
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entero <- data.frame(mo = c("E. coli", "E. coli", "E. coli", "E. coli"),
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GEN = c("R", "R", "S", "R"), TOB = c("S", "R", "S", "R"),
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AMK = c("S", "R", "R", "R"), NET = c("S", "R", "R", "R"),
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CPT = c("S", "R", "R", "R"), TCC = c("S", "R", "R", "R"),
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TZP = c("S", "S", "R", "R"), ETP = c("S", "S", "R", "R"),
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IPM = c("S", "S", "R", "R"), MEM = c("S", "S", "R", "R"),
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DOR = c("S", "S", "R", "R"), CZO = c("S", "S", "R", "R"),
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CXM = c("S", "S", "R", "R"), CTX = c("S", "S", "R", "R"),
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CAZ = c("S", "S", "R", "R"), FEP = c("S", "S", "R", "R"),
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FOX = c("S", "S", "R", "R"), CTT = c("S", "S", "R", "R"),
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CIP = c("S", "S", "R", "R"), SXT = c("S", "S", "R", "R"),
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TGC = c("S", "S", "R", "R"), ATM = c("S", "S", "R", "R"),
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AMP = c("S", "S", "R", "R"), AMC = c("S", "S", "R", "R"),
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SAM = c("S", "S", "R", "R"), CHL = c("S", "S", "R", "R"),
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FOS = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
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TCY = c("S", "S", "R", "R"), DOX = c("S", "S", "R", "R"),
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MNO = c("S", "S", "R", "R"),
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stringsAsFactors = FALSE)
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expect_equal(as.integer(mdro(entero)), c(1:4))
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expect_s3_class(mdro(entero, verbose = TRUE), "data.frame")
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pseud <- data.frame(mo = c("P. aeruginosa", "P. aeruginosa", "P. aeruginosa", "P. aeruginosa"),
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GEN = c("R", "R", "S", "R"), TOB = c("S", "S", "S", "R"),
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AMK = c("S", "S", "R", "R"), NET = c("S", "S", "R", "R"),
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IPM = c("S", "R", "R", "R"), MEM = c("S", "S", "R", "R"),
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DOR = c("S", "S", "R", "R"), CAZ = c("S", "S", "R", "R"),
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FEP = c("S", "R", "R", "R"), CIP = c("S", "S", "R", "R"),
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LVX = c("S", "S", "R", "R"), TCC = c("S", "S", "R", "R"),
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TZP = c("S", "S", "R", "R"), ATM = c("S", "S", "R", "R"),
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FOS = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
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PLB = c("S", "S", "R", "R"),
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stringsAsFactors = FALSE)
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expect_equal(as.integer(mdro(pseud)), c(1:4))
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expect_s3_class(mdro(pseud, verbose = TRUE), "data.frame")
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acin <- data.frame(mo = c("A. baumannii", "A. baumannii", "A. baumannii", "A. baumannii"),
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GEN = c("R", "R", "S", "R"), TOB = c("S", "R", "S", "R"),
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AMK = c("S", "R", "R", "R"), NET = c("S", "R", "R", "R"),
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IPM = c("S", "S", "R", "R"), MEM = c("S", "R", "R", "R"),
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DOR = c("S", "S", "R", "R"), CIP = c("S", "S", "R", "R"),
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LVX = c("S", "S", "R", "R"), TZP = c("S", "S", "R", "R"),
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TCC = c("S", "S", "R", "R"), CTX = c("S", "S", "R", "R"),
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CRO = c("S", "S", "R", "R"), CAZ = c("S", "S", "R", "R"),
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FEP = c("S", "R", "R", "R"), SXT = c("S", "S", "R", "R"),
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SAM = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
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PLB = c("S", "S", "R", "R"), TCY = c("S", "S", "R", "R"),
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DOX = c("S", "S", "R", "R"), MNO = c("S", "S", "R", "R"),
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stringsAsFactors = FALSE)
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expect_equal(as.integer(mdro(acin)), c(1:4))
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expect_s3_class(mdro(acin, verbose = TRUE), "data.frame")
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# custom rules
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custom <- custom_mdro_guideline("CIP == 'R' & age > 60" ~ "Elderly Type A",
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"ERY == 'R' & age > 60" ~ "Elderly Type B",
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as_factor = TRUE)
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expect_output(print(custom))
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expect_output(x <- mdro(example_isolates, guideline = custom, info = TRUE))
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expect_equal(as.double(table(x)), c(1066, 43, 891))
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expect_output(print(custom_mdro_guideline(AMX == "R" ~ "test", as_factor = FALSE)))
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expect_error(custom_mdro_guideline())
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expect_error(custom_mdro_guideline("test"))
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expect_error(custom_mdro_guideline("test" ~ c(1:3)))
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expect_error(custom_mdro_guideline("test" ~ A))
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expect_warning(mdro(example_isolates,
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# since `test` gives an error, it will be ignored with a warning
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guideline = custom_mdro_guideline(test ~ "A"),
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info = FALSE))
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# print groups
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library(dplyr)
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expect_output(x <- mdro(example_isolates %>% group_by(hospital_id), info = TRUE))
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expect_output(x <- mdro(example_isolates %>% group_by(hospital_id), guideline = custom, info = TRUE))
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})
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