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AMR/tests/testthat/test-mdro.R

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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.github.io/AMR. #
# ==================================================================== #
context("mdro.R")
test_that("mdro works", {
skip_on_cran()
expect_error(suppressWarnings(mdro(example_isolates, country = "invalid", col_mo = "mo", info = TRUE)))
expect_error(suppressWarnings(mdro(example_isolates, country = "fr", info = TRUE)))
expect_error(mdro(example_isolates, guideline = c("BRMO", "MRGN"), info = TRUE))
expect_error(mdro(example_isolates, col_mo = "invalid", info = TRUE))
outcome <- suppressWarnings(mdro(example_isolates))
outcome <- mdro(example_isolates, "eucast3.1", info = TRUE)
outcome <- eucast_exceptional_phenotypes(example_isolates, info = TRUE)
# check class
expect_equal(class(outcome), c("ordered", "factor"))
outcome <- mdro(example_isolates, "nl", info = TRUE)
# check class
expect_equal(class(outcome), c("ordered", "factor"))
library(dplyr)
# example_isolates should have these finding using Dutch guidelines
expect_equal(outcome %>% cleaner::freq() %>% pull(count),
c(1969, 25, 6)) # 1969 neg, 25 unconfirmed, 6 pos
expect_equal(brmo(example_isolates, info = FALSE),
mdro(example_isolates, guideline = "BRMO", info = FALSE))
# test Dutch P. aeruginosa MDRO
expect_equal(
as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
cfta = "S",
cipr = "S",
mero = "S",
imip = "S",
gent = "S",
tobr = "S",
pita = "S"),
guideline = "BRMO",
col_mo = "mo",
info = FALSE)),
"Negative")
expect_equal(
as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
cefta = "R",
cipr = "R",
mero = "R",
imip = "R",
gent = "R",
tobr = "R",
pita = "R"),
guideline = "BRMO",
col_mo = "mo",
info = FALSE)),
"Positive")
# German 3MRGN and 4MRGN
expect_equal(as.character(mrgn(
data.frame(mo = c("E. coli", "E. coli", "K. pneumoniae", "E. coli",
"A. baumannii", "A. baumannii", "A. baumannii",
"P. aeruginosa", "P. aeruginosa", "P. aeruginosa"),
PIP = c("S", "R", "R", "S",
"S", "R", "R",
"S", "R", "R"),
CTX = c("S", "R", "R", "S",
"R", "R", "R",
"R", "R", "R"),
IPM = c("S", "R", "S", "R",
"R", "R", "S",
"S", "R", "R"),
CIP = c("S", "R", "R", "S",
"R", "R", "R",
"R", "S", "R"),
stringsAsFactors = FALSE))),
c("Negative", "4MRGN", "3MRGN", "4MRGN", "4MRGN", "4MRGN", "3MRGN", "Negative", "3MRGN", "4MRGN"))
# MDR TB
expect_equal(
# select only rifampicine, mo will be determined automatically (as M. tuberculosis),
# number of mono-resistant strains should be equal to number of rifampicine-resistant strains
cleaner::freq(mdr_tb(example_isolates[, "RIF", drop = FALSE]))$count[2],
count_R(example_isolates$RIF))
sample_rsi <- function() {
sample(c("S", "I", "R"),
size = 5000,
prob = c(0.5, 0.1, 0.4),
replace = TRUE)
}
x <- data.frame(rifampicin = sample_rsi(),
inh = sample_rsi(),
gatifloxacin = sample_rsi(),
eth = sample_rsi(),
pza = sample_rsi(),
MFX = sample_rsi(),
KAN = sample_rsi())
expect_gt(n_distinct(mdr_tb(x)), 2)
# check the guideline by Magiorakos et al. (2012), the default guideline
stau <- data.frame(mo = c("S. aureus", "S. aureus", "S. aureus", "S. aureus"),
GEN = c("R", "R", "S", "R"),
RIF = c("S", "R", "S", "R"),
CPT = c("S", "R", "R", "R"),
OXA = c("S", "R", "R", "R"),
CIP = c("S", "S", "R", "R"),
MFX = c("S", "S", "R", "R"),
SXT = c("S", "S", "R", "R"),
FUS = c("S", "S", "R", "R"),
VAN = c("S", "S", "R", "R"),
TEC = c("S", "S", "R", "R"),
TLV = c("S", "S", "R", "R"),
TGC = c("S", "S", "R", "R"),
CLI = c("S", "S", "R", "R"),
DAP = c("S", "S", "R", "R"),
ERY = c("S", "S", "R", "R"),
LNZ = c("S", "S", "R", "R"),
CHL = c("S", "S", "R", "R"),
FOS = c("S", "S", "R", "R"),
QDA = c("S", "S", "R", "R"),
TCY = c("S", "S", "R", "R"),
DOX = c("S", "S", "R", "R"),
MNO = c("S", "S", "R", "R"),
stringsAsFactors = FALSE)
expect_equal(as.integer(mdro(stau)), c(1:4))
expect_s3_class(mdro(stau, verbose = TRUE), "data.frame")
ente <- data.frame(mo = c("Enterococcus", "Enterococcus", "Enterococcus", "Enterococcus"),
GEH = c("R", "R", "S", "R"),
STH = c("S", "R", "S", "R"),
IPM = c("S", "R", "R", "R"),
MEM = c("S", "R", "R", "R"),
DOR = c("S", "S", "R", "R"),
CIP = c("S", "S", "R", "R"),
LVX = c("S", "S", "R", "R"),
MFX = c("S", "S", "R", "R"),
VAN = c("S", "S", "R", "R"),
TEC = c("S", "S", "R", "R"),
TGC = c("S", "S", "R", "R"),
DAP = c("S", "S", "R", "R"),
LNZ = c("S", "S", "R", "R"),
AMP = c("S", "S", "R", "R"),
QDA = c("S", "S", "R", "R"),
DOX = c("S", "S", "R", "R"),
MNO = c("S", "S", "R", "R"),
stringsAsFactors = FALSE)
expect_equal(as.integer(mdro(ente)), c(1:4))
expect_s3_class(mdro(ente, verbose = TRUE), "data.frame")
entero <- data.frame(mo = c("E. coli", "E. coli", "E. coli", "E. coli"),
GEN = c("R", "R", "S", "R"), TOB = c("S", "R", "S", "R"),
AMK = c("S", "R", "R", "R"), NET = c("S", "R", "R", "R"),
CPT = c("S", "R", "R", "R"), TCC = c("S", "R", "R", "R"),
TZP = c("S", "S", "R", "R"), ETP = c("S", "S", "R", "R"),
IPM = c("S", "S", "R", "R"), MEM = c("S", "S", "R", "R"),
DOR = c("S", "S", "R", "R"), CZO = c("S", "S", "R", "R"),
CXM = c("S", "S", "R", "R"), CTX = c("S", "S", "R", "R"),
CAZ = c("S", "S", "R", "R"), FEP = c("S", "S", "R", "R"),
FOX = c("S", "S", "R", "R"), CTT = c("S", "S", "R", "R"),
CIP = c("S", "S", "R", "R"), SXT = c("S", "S", "R", "R"),
TGC = c("S", "S", "R", "R"), ATM = c("S", "S", "R", "R"),
AMP = c("S", "S", "R", "R"), AMC = c("S", "S", "R", "R"),
SAM = c("S", "S", "R", "R"), CHL = c("S", "S", "R", "R"),
FOS = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
TCY = c("S", "S", "R", "R"), DOX = c("S", "S", "R", "R"),
MNO = c("S", "S", "R", "R"),
stringsAsFactors = FALSE)
expect_equal(as.integer(mdro(entero)), c(1:4))
expect_s3_class(mdro(entero, verbose = TRUE), "data.frame")
pseud <- data.frame(mo = c("P. aeruginosa", "P. aeruginosa", "P. aeruginosa", "P. aeruginosa"),
GEN = c("R", "R", "S", "R"), TOB = c("S", "S", "S", "R"),
AMK = c("S", "S", "R", "R"), NET = c("S", "S", "R", "R"),
IPM = c("S", "R", "R", "R"), MEM = c("S", "S", "R", "R"),
DOR = c("S", "S", "R", "R"), CAZ = c("S", "S", "R", "R"),
FEP = c("S", "R", "R", "R"), CIP = c("S", "S", "R", "R"),
LVX = c("S", "S", "R", "R"), TCC = c("S", "S", "R", "R"),
TZP = c("S", "S", "R", "R"), ATM = c("S", "S", "R", "R"),
FOS = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
PLB = c("S", "S", "R", "R"),
stringsAsFactors = FALSE)
expect_equal(as.integer(mdro(pseud)), c(1:4))
expect_s3_class(mdro(pseud, verbose = TRUE), "data.frame")
acin <- data.frame(mo = c("A. baumannii", "A. baumannii", "A. baumannii", "A. baumannii"),
GEN = c("R", "R", "S", "R"), TOB = c("S", "R", "S", "R"),
AMK = c("S", "R", "R", "R"), NET = c("S", "R", "R", "R"),
IPM = c("S", "S", "R", "R"), MEM = c("S", "R", "R", "R"),
DOR = c("S", "S", "R", "R"), CIP = c("S", "S", "R", "R"),
LVX = c("S", "S", "R", "R"), TZP = c("S", "S", "R", "R"),
TCC = c("S", "S", "R", "R"), CTX = c("S", "S", "R", "R"),
CRO = c("S", "S", "R", "R"), CAZ = c("S", "S", "R", "R"),
FEP = c("S", "R", "R", "R"), SXT = c("S", "S", "R", "R"),
SAM = c("S", "S", "R", "R"), COL = c("S", "S", "R", "R"),
PLB = c("S", "S", "R", "R"), TCY = c("S", "S", "R", "R"),
DOX = c("S", "S", "R", "R"), MNO = c("S", "S", "R", "R"),
stringsAsFactors = FALSE)
expect_equal(as.integer(mdro(acin)), c(1:4))
expect_s3_class(mdro(acin, verbose = TRUE), "data.frame")
})
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