mirror of https://github.com/msberends/AMR.git
95 lines
5.1 KiB
R
95 lines
5.1 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.github.io/AMR. #
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# ==================================================================== #
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patients <- unlist(lapply(LETTERS, paste0, 1:10))
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patients_table <- data.frame(patient_id = patients,
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gender = c(rep("M", 135),
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rep("F", 125)))
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dates <- seq(as.Date("2011-01-01"), as.Date("2020-01-01"), by = "day")
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bacteria_a <- c("E. coli", "S. aureus",
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"S. pneumoniae", "K. pneumoniae")
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bacteria_b <- c("esccol", "staaur", "strpne", "klepne")
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bacteria_c <- c("Escherichia coli", "Staphylococcus aureus",
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"Streptococcus pneumoniae", "Klebsiella pneumoniae")
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ab_interpretations <- c("S", "I", "R")
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ab_interpretations_messy = c("R", "< 0.5 S", "I")
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sample_size <- 1000
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data_a <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "A",
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bacteria = sample(bacteria_a, size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)),
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AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)),
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AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)),
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CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)),
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GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)))
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data_b <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "B",
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bacteria = sample(bacteria_b, size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)),
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AMX = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)),
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AMC = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)),
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CIP = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)),
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GEN = sample(ab_interpretations_messy, size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)))
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data_c <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "C",
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bacteria = sample(bacteria_c, size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)),
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AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)),
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AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)),
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CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)),
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GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)))
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example_isolates_unclean <- data_a %>%
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bind_rows(data_b, data_c)
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example_isolates_unclean$patient_id <- sample(patients, size = nrow(example_isolates_unclean), replace = TRUE)
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example_isolates_unclean <- example_isolates_unclean %>%
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select(patient_id, hospital, date, bacteria, everything())
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usethis::use_data(example_isolates_unclean, overwrite = TRUE)
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