mirror of https://github.com/msberends/AMR.git
147 lines
5.0 KiB
R
147 lines
5.0 KiB
R
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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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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# 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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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: 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(
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patient_id = patients,
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gender = c(
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rep("M", 135),
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rep("F", 125)
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)
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)
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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(
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"E. coli", "S. aureus",
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"S. pneumoniae", "K. pneumoniae"
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)
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bacteria_b <- c("esccol", "staaur", "strpne", "klepne")
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bacteria_c <- c(
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"Escherichia coli", "Staphylococcus aureus",
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"Streptococcus pneumoniae", "Klebsiella pneumoniae"
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)
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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(
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date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "A",
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bacteria = sample(bacteria_a,
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size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)
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),
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AMX = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)
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),
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AMC = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)
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),
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CIP = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)
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),
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GEN = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)
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)
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)
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data_b <- data.frame(
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date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "B",
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bacteria = sample(bacteria_b,
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size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)
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),
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AMX = sample(ab_interpretations_messy,
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size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)
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),
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AMC = sample(ab_interpretations_messy,
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size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)
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),
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CIP = sample(ab_interpretations_messy,
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size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)
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),
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GEN = sample(ab_interpretations_messy,
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size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)
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)
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)
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data_c <- data.frame(
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date = sample(dates, size = sample_size, replace = TRUE),
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hospital = "C",
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bacteria = sample(bacteria_c,
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size = sample_size, replace = TRUE,
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prob = c(0.50, 0.25, 0.15, 0.10)
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),
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AMX = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.60, 0.05, 0.35)
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),
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AMC = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.75, 0.10, 0.15)
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),
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CIP = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.80, 0.00, 0.20)
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),
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GEN = sample(ab_interpretations,
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size = sample_size, replace = TRUE,
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prob = c(0.92, 0.00, 0.08)
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)
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)
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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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dataset_UTF8_to_ASCII()
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usethis::use_data(example_isolates_unclean, overwrite = TRUE, internal = FALSE, version = 2, compress = "xz")
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