AMR/inst/tinytest/test-ab_selectors.R

# ==================================================================== #
# TITLE:                                                               #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE CODE:                                                         #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# PLEASE CITE THIS SOFTWARE AS:                                        #
# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
# Journal of Statistical Software, 104(3), 1-31.                       #
# https://doi.org/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/   #
# ==================================================================== #

# antibiotic class selectors
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)

# Examples:

# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
expect_equal(ncol(example_isolates[, c("mo", aminoglycosides())]), 5, tolerance = 0.5)

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)
expect_equal(ncol(example_isolates[, c(administrable_iv() | penicillins())]), 37, tolerance = 0.5)

# 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)
expect_equal(nrow(example_isolates[any(carbapenems() == "R"), ]), 55, tolerance = 0.5)
expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5)
expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, 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)

x <- data.frame(
  x = 0,
  mo = 0,
  gen = "S",
  genta = "S",
  J01GB03 = "S",
  tobra = "S",
  Tobracin = "S"
)
# should have the first hits
expect_identical(
  colnames(x[, aminoglycosides()]),
  c("gen", "tobra")
)

if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
  expect_equal(example_isolates %>% select(administrable_per_os() & penicillins()) %>% ncol(), 5, tolerance = 0.5)
  expect_equal(example_isolates %>% select(administrable_iv() & penicillins()) %>% ncol(), 7, tolerance = 0.5)
  expect_equal(example_isolates %>% select(administrable_iv() | penicillins()) %>% ncol(), 37, tolerance = 0.5)
  # expect_warning(example_isolates %>% select(GEH = GEN) %>% select(aminoglycosides(only_treatable = TRUE)))
}