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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# 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/ #
# ==================================================================== #
patients <- unlist(lapply(LETTERS, paste0, 1:10))
patients_table <- data.frame(patient_id = patients,
gender = c(rep("M", 135),
rep("F", 125)))
dates <- seq(as.Date("2011-01-01"), as.Date("2020-01-01"), by = "day")
bacteria_a <- c("E. coli", "S. aureus",
"S. pneumoniae", "K. pneumoniae")
bacteria_b <- c("esccol", "staaur", "strpne", "klepne")
bacteria_c <- c("Escherichia coli", "Staphylococcus aureus",
"Streptococcus pneumoniae", "Klebsiella pneumoniae")
ab_interpretations <- c("S", "I", "R")
ab_interpretations_messy = c("R", "< 0.5 S", "I")
sample_size <- 1000
data_a <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
hospital = "A",
bacteria = sample(bacteria_a, size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)),
AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.60, 0.05, 0.35)),
AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.75, 0.10, 0.15)),
CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08)))
data_b <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
hospital = "B",
bacteria = sample(bacteria_b, size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)),
AMX = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
prob = c(0.60, 0.05, 0.35)),
AMC = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
prob = c(0.75, 0.10, 0.15)),
CIP = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
GEN = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08)))
data_c <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
hospital = "C",
bacteria = sample(bacteria_c, size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)),
AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.60, 0.05, 0.35)),
AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.75, 0.10, 0.15)),
CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08)))
example_isolates_unclean <- data_a %>%
bind_rows(data_b, data_c)
example_isolates_unclean$patient_id <- sample(patients, size = nrow(example_isolates_unclean), replace = TRUE)
example_isolates_unclean <- example_isolates_unclean %>%
select(patient_id, hospital, date, bacteria, everything())
usethis::use_data(example_isolates_unclean, overwrite = TRUE)