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read_4D
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134
R/read_4d.R
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134
R/read_4d.R
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Read data from 4D database
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#'
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#' This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the data set to English, transform the \code{mo} with \code{\link{as.mo}} and transform all antimicrobial columns with \code{\link{as.rsi}}.
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#' @inheritParams utils::read.table
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#' @export
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read_4D <- function(file,
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header = TRUE,
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sep = "\t",
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quote = "\"'",
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dec = ",",
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na.strings = c("NA", "", "."),
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skip = 2,
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check.names = TRUE,
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strip.white = TRUE,
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fill = TRUE,
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blank.lines.skip = TRUE,
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stringsAsFactors = FALSE,
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fileEncoding = "UTF-8",
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encoding = "UTF-8") {
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data_4D <- utils::read.table(file = file,
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header = header,
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sep = sep,
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quote = quote,
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dec = dec,
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na.strings = na.strings,
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skip = skip,
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check.names = check.names,
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strip.white = strip.white,
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fill = fill,
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blank.lines.skip = blank.lines.skip,
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stringsAsFactors = stringsAsFactors,
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fileEncoding = fileEncoding,
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encoding = encoding)
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# helper functions
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to_date_4D <- function(x) {
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date_regular <- as.Date(x, format = "%d-%m-%y")
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posixlt <- as.POSIXlt(date_regular)
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# born after today will be born 100 years ago
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# based on https://stackoverflow.com/a/3312971/4575331
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posixlt[date_regular > Sys.Date()]$year <- posixlt[date_regular > Sys.Date()]$year - 100
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as.Date(posixlt)
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}
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to_age_4D <- function(from, to) {
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from_lt = as.POSIXlt(from)
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to_lt = as.POSIXlt(to)
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age = to_lt$year - from_lt$year
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ifelse(to_lt$mon < from_lt$mon |
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(to_lt$mon == from_lt$mon & to_lt$mday < from_lt$mday),
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age - 1, age)
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}
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colnames(data_4D) <- tolower(colnames(data_4D))
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if (all(c("afnamedat", "gebdatum") %in% colnames(data_4D))) {
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# add age
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data_4D$age <- NA_integer_
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}
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cols_wanted <- c("patientnr", "gebdatum", "age", "mv", "monsternr", "afnamedat", "bepaling",
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"afd.", "spec", "mat", "matbijz.", "mocode",
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"amfo", "amox", "anid", "azit", "casp", "cecl", "cefe", "cfcl",
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"cfot", "cfox", "cfta", "cftr", "cfur", "chlo", "cipr", "clin",
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"cocl", "ctta", "dapt", "doxy", "eryt", "fluo", "fluz", "fosf",
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"fusi", "gehi", "gent", "imip", "kana", "levo", "line", "mero",
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"metr", "mico", "mino", "moxi", "mupi", "nali", "nitr", "norf",
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"oxac", "peni", "pipe", "pita", "poly", "posa", "quda", "rifa",
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"spat", "teic", "tige", "tobr", "trim", "trsu", "vana", "vanb",
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"vanc", "vori")
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# this ones actually exist
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cols_wanted <- cols_wanted[cols_wanted %in% colnames(data_4D)]
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# order of columns
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data_4D <- data_4D[, cols_wanted]
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# rename of columns
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colnames(data_4D) <- gsub("patientnr", "patient_id", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("gebdatum", "date_birth", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("mv", "gender", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("monsternr", "sample_id", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("afnamedat", "date_received", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("bepaling", "sample_test", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("afd.", "department", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("spec", "specialty", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("matbijz.", "specimen_type", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("mat", "specimen_group", colnames(data_4D), fixed = TRUE)
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colnames(data_4D) <- gsub("mocode", "mo", colnames(data_4D), fixed = TRUE)
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if ("date_birth" %in% colnames(data_4D)) {
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data_4D$date_birth <- to_date_4D(data_4D$date_birth)
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}
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if ("date_received" %in% colnames(data_4D)) {
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data_4D$date_received <- to_date_4D(data_4D$date_received)
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}
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if ("age" %in% colnames(data_4D)) {
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data_4D$age <- to_age_4D(data_4D$date_birth, data_4D$date_received)
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}
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if ("gender" %in% colnames(data_4D)) {
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data_4D$gender[data_4D$gender == "V"] <- "F"
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}
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if ("mo" %in% colnames(data_4D)) {
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data_4D$mo <- as.mo(data_4D$mo)
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# column right of mo is:
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drug1 <- colnames(data_4D)[grep("^mo$", colnames(data_4D)) + 1]
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if (!is.na(drug1)) {
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# and last is:
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drug_last <- colnames(data_4D)[length(data_4D)]
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# transform those to rsi:
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data_4D <- suppressWarnings(mutate_at(data_4D, vars(drug1:drug_last), as.rsi))
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}
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}
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data_4D
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}
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