# ==================================================================== #
# 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)