2018-12-16 22:45:12 +01:00
|
|
|
# ==================================================================== #
|
|
|
|
|
# TITLE #
|
2021-02-02 23:57:35 +01:00
|
|
|
# Antimicrobial Resistance (AMR) Data Analysis for R #
|
2018-12-16 22:45:12 +01:00
|
|
|
# #
|
2019-01-02 23:24:07 +01:00
|
|
|
# SOURCE #
|
2020-07-08 14:48:06 +02:00
|
|
|
# https://github.com/msberends/AMR #
|
2018-12-16 22:45:12 +01:00
|
|
|
# #
|
|
|
|
|
# LICENCE #
|
2020-12-27 00:30:28 +01:00
|
|
|
# (c) 2018-2021 Berends MS, Luz CF et al. #
|
2020-10-08 11:16:03 +02:00
|
|
|
# Developed at the University of Groningen, the Netherlands, in #
|
|
|
|
|
# collaboration with non-profit organisations Certe Medical #
|
|
|
|
|
# Diagnostics & Advice, and University Medical Center Groningen. #
|
2018-12-16 22:45:12 +01:00
|
|
|
# #
|
2019-01-02 23:24:07 +01:00
|
|
|
# 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. #
|
2020-01-05 17:22:09 +01:00
|
|
|
# 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. #
|
2020-10-08 11:16:03 +02:00
|
|
|
# #
|
|
|
|
|
# Visit our website for the full manual and a complete tutorial about #
|
2021-02-02 23:57:35 +01:00
|
|
|
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
|
2018-12-16 22:45:12 +01:00
|
|
|
# ==================================================================== #
|
|
|
|
|
|
2021-06-22 12:16:42 +02:00
|
|
|
# antibiotic class selectors
|
2021-08-19 23:56:18 +02:00
|
|
|
expect_equal(ncol(example_isolates[, ab_class("antimyco"), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, aminoglycosides(), drop = FALSE]), 4, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, aminopenicillins(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, betalactams(), drop = FALSE]), 16, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, carbapenems(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins(), drop = FALSE]), 7, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins_1st(), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins_2nd(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins_3rd(), drop = FALSE]), 3, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins_4th(), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, cephalosporins_5th(), drop = FALSE]), 0, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, fluoroquinolones(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, glycopeptides(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, lincosamides(), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, lipoglycopeptides(), drop = FALSE]), 0, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, macrolides(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, oxazolidinones(), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, penicillins(), drop = FALSE]), 7, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, polymyxins(), drop = FALSE]), 1, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, streptogramins(), drop = FALSE]), 0, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, quinolones(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, tetracyclines(), drop = FALSE]), 3, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, trimethoprims(), drop = FALSE]), 2, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, ureidopenicillins(), drop = FALSE]), 1, tolerance = 0.5)
|
2021-06-22 12:16:42 +02:00
|
|
|
|
|
|
|
|
# Examples:
|
|
|
|
|
|
|
|
|
|
# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
|
|
|
|
|
expect_equal(ncol(example_isolates[, c("mo", aminoglycosides())]), 5, tolerance = 0.5)
|
|
|
|
|
|
2021-08-19 23:56:18 +02:00
|
|
|
expect_equal(ncol(example_isolates[, c(administrable_per_os() & penicillins())]), 5, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[, c(administrable_iv() & penicillins())]), 7, tolerance = 0.5)
|
2021-09-03 13:08:36 +02:00
|
|
|
expect_equal(ncol(example_isolates[, c(administrable_iv() | penicillins())]), 37, tolerance = 0.5)
|
2021-08-16 21:54:34 +02:00
|
|
|
|
2021-06-22 12:16:42 +02:00
|
|
|
# filter using any() or all()
|
|
|
|
|
expect_equal(nrow(example_isolates[any(carbapenems() == "R"), ]), 55, tolerance = 0.5)
|
|
|
|
|
expect_equal(nrow(subset(example_isolates, any(carbapenems() == "R"))), 55, tolerance = 0.5)
|
|
|
|
|
|
|
|
|
|
# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
|
|
|
|
|
expect_equal(nrow(example_isolates[any(carbapenems()), ]), 962, tolerance = 0.5)
|
|
|
|
|
expect_equal(nrow(example_isolates[all(carbapenems()), ]), 756, tolerance = 0.5)
|
|
|
|
|
|
|
|
|
|
# filter with multiple antibiotic selectors using c()
|
|
|
|
|
expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5)
|
|
|
|
|
|
|
|
|
|
# filter + select in one go: get penicillins in carbapenems-resistant strains
|
|
|
|
|
expect_equal(nrow(example_isolates[any(carbapenems() == "R"), penicillins()]), 55, tolerance = 0.5)
|
|
|
|
|
expect_equal(ncol(example_isolates[any(carbapenems() == "R"), penicillins()]), 7, tolerance = 0.5)
|
|
|
|
|
|
