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styled, unit test fix

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dr. M.S. (Matthijs) Berends
2022-08-28 10:31:50 +02:00
parent 4cb1db4554
commit 4d050aef7c
147 changed files with 10897 additions and 8169 deletions
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140
data-raw/reproduction_of_example_isolates_unclean.R
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@@ -9,7 +9,7 @@
# (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. #
# 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 #
@@ -25,74 +25,118 @@
patients <- unlist(lapply(LETTERS, paste0, 1:10))
patients_table <- data.frame(patient_id = patients,
gender = c(rep("M", 135),
rep("F", 125)))
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_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")
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")
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_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_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)))
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 %>%
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()) %>%
example_isolates_unclean <- example_isolates_unclean %>%
select(patient_id, hospital, date, bacteria, everything()) %>%
dataset_UTF8_to_ASCII()
usethis::use_data(example_isolates_unclean, overwrite = TRUE, internal = FALSE, version = 2, compress = "xz")