2023-02-10 13:13:17 +01:00
|
|
|
# ==================================================================== #
|
|
|
|
# TITLE #
|
|
|
|
# AMR: An R Package for Working with Antimicrobial Resistance Data #
|
|
|
|
# #
|
|
|
|
# SOURCE #
|
|
|
|
# https://github.com/msberends/AMR #
|
|
|
|
# #
|
|
|
|
# CITE AS #
|
|
|
|
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
|
|
|
|
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
|
|
|
|
# Data. Journal of Statistical Software, 104(3), 1-31. #
|
|
|
|
# doi:10.18637/jss.v104.i03 #
|
|
|
|
# #
|
|
|
|
# Developed at the University of Groningen and the University Medical #
|
|
|
|
# Center Groningen in The Netherlands, in collaboration with many #
|
|
|
|
# colleagues from around the world, see our website. #
|
|
|
|
# #
|
|
|
|
# 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 the full manual and a complete tutorial about #
|
|
|
|
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
|
|
|
|
# ==================================================================== #
|
|
|
|
|
|
|
|
|
2023-02-12 17:10:48 +01:00
|
|
|
# Traditional antibiogram ----------------------------------------------
|
|
|
|
|
|
|
|
ab1 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = c(aminoglycosides(), carbapenems()))
|
|
|
|
|
|
|
|
ab2 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = aminoglycosides(),
|
|
|
|
ab_transform = "atc",
|
|
|
|
mo_transform = "gramstain")
|
|
|
|
|
|
|
|
ab3 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = carbapenems(),
|
|
|
|
ab_transform = "name",
|
|
|
|
mo_transform = "name")
|
|
|
|
|
|
|
|
expect_inherits(ab1, "antibiogram")
|
|
|
|
expect_inherits(ab2, "antibiogram")
|
|
|
|
expect_inherits(ab3, "antibiogram")
|
|
|
|
expect_equal(colnames(ab1), c("Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
|
|
|
|
expect_equal(colnames(ab2), c("Pathogen (N min-max)", "J01GB01", "J01GB03", "J01GB04", "J01GB06"))
|
|
|
|
expect_equal(colnames(ab3), c("Pathogen (N min-max)", "Imipenem", "Meropenem"))
|
|
|
|
expect_equal(ab3$Meropenem, c(52, NA, 100, 100, NA))
|
|
|
|
|
|
|
|
# Combined antibiogram -------------------------------------------------
|
|
|
|
|
|
|
|
# combined antibiotics yield higher empiric coverage
|
|
|
|
ab4 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
|
|
|
mo_transform = "gramstain")
|
|
|
|
|
|
|
|
ab5 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = c("TZP", "TZP+TOB"),
|
|
|
|
mo_transform = "gramstain",
|
|
|
|
ab_transform = "name",
|
|
|
|
sep = " & ",
|
|
|
|
add_total_n = FALSE)
|
|
|
|
|
|
|
|
expect_inherits(ab4, "antibiogram")
|
|
|
|
expect_inherits(ab5, "antibiogram")
|
|
|
|
expect_equal(colnames(ab4), c("Pathogen (N min-max)", "TZP", "TZP + GEN", "TZP + TOB"))
|
|
|
|
expect_equal(colnames(ab5), c("Pathogen", "Piperacillin/tazobactam", "Piperacillin/tazobactam & Tobramycin"))
|
|
|
|
|
|
|
|
# Syndromic antibiogram ------------------------------------------------
|
|
|
|
|
|
|
|
# the data set could contain a filter for e.g. respiratory specimens
|
|
|
|
ab6 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = c(aminoglycosides(), carbapenems()),
|
|
|
|
syndromic_group = "ward")
|
|
|
|
|
|
|
|
# with a custom language, though this will be determined automatically
|
|
|
|
# (i.e., this table will be in Spanish on Spanish systems)
|
|
|
|
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
|
|
|
|
ab7 <- antibiogram(ex1,
|
|
|
|
antibiotics = aminoglycosides(),
|
|
|
|
ab_transform = "name",
|
|
|
|
syndromic_group = ifelse(ex1$ward == "ICU",
|
|
|
|
"UCI", "No UCI"),
|
|
|
|
language = "es")
|
|
|
|
|
|
|
|
expect_inherits(ab6, "antibiogram")
|
|
|
|
expect_inherits(ab7, "antibiogram")
|
|
|
|
expect_equal(colnames(ab6), c("Syndromic Group", "Pathogen (N min-max)", "AMK", "GEN", "IPM", "KAN", "MEM", "TOB"))
|
|
|
|
expect_equal(colnames(ab7), c("Grupo sindrómico", "Patógeno (N min-max)", "Amikacina", "Gentamicina", "Tobramicina"))
|
|
|
|
|
|
|
|
# Weighted-incidence syndromic combination antibiogram (WISCA) ---------
|
|
|
|
|
|
|
|
# the data set could contain a filter for e.g. respiratory specimens
|
|
|
|
ab8 <- antibiogram(example_isolates,
|
|
|
|
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
|
|
|
|
mo_transform = "gramstain",
|
|
|
|
minimum = 10, # this should be >= 30, but now just as example
|
|
|
|
syndromic_group = ifelse(example_isolates$age >= 65 &
|
|
|
|
example_isolates$gender == "M",
|
|
|
|
"WISCA Group 1", "WISCA Group 2"))
|
|
|
|
|
|
|
|
expect_inherits(ab8, "antibiogram")
|
|
|
|
expect_equal(colnames(ab8), c("Syndromic Group", "Pathogen (N min-max)", "AMC", "AMC + CIP", "TZP", "TZP + TOB"))
|
|
|
|
|
|
|
|
# Generate plots with ggplot2 or base R --------------------------------
|
|
|
|
|
|
|
|
pdf(NULL) # prevent Rplots.pdf being created
|
|
|
|
|
|
|
|
expect_silent(plot(ab1))
|
|
|
|
expect_silent(plot(ab2))
|
|
|
|
expect_silent(plot(ab3))
|
|
|
|
expect_silent(plot(ab4))
|
|
|
|
expect_silent(plot(ab5))
|
|
|
|
expect_silent(plot(ab6))
|
|
|
|
expect_silent(plot(ab7))
|
|
|
|
expect_silent(plot(ab8))
|
|
|
|
|
|
|
|
if (AMR:::pkg_is_available("ggplot2")) {
|
|
|
|
expect_inherits(autoplot(ab1), "gg")
|
|
|
|
expect_inherits(autoplot(ab2), "gg")
|
|
|
|
expect_inherits(autoplot(ab3), "gg")
|
|
|
|
expect_inherits(autoplot(ab4), "gg")
|
|
|
|
expect_inherits(autoplot(ab5), "gg")
|
|
|
|
expect_inherits(autoplot(ab6), "gg")
|
|
|
|
expect_inherits(autoplot(ab7), "gg")
|
|
|
|
expect_inherits(autoplot(ab8), "gg")
|
|
|
|
}
|