mirror of https://github.com/msberends/AMR.git
fix clipboard on linux
This commit is contained in:
parent
abcb4accbd
commit
c182a9673d
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@ -3,3 +3,4 @@
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.RData
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.Ruserdata
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AMR.Rproj
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tests/testthat/Rplots.pdf
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10
.travis.yml
10
.travis.yml
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@ -3,15 +3,9 @@
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# Setting up R deps
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language: r
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r: 3.2
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r_packages:
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- covr
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- testthat
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- dplyr
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- rvest
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- xml2
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- reshape2
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r_packages: covr
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# system deps
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# system deps, install xclip for clipboard support
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os:
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- linux
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- osx
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 0.1.2
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Date: 2018-03-27
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Date: 2018-04-02
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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@ -29,27 +29,39 @@ clipboard_import <- function(sep = '\t',
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file <- pipe("xclip -o", "r")
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on.exit(close(file))
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}
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import_tbl <- read.delim(file = file,
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sep = sep,
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header = header,
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strip.white = TRUE,
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dec = dec,
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na.strings = na,
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fileEncoding = 'UTF-8',
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encoding = 'UTF-8',
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stringsAsFactors = FALSE)
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import_tbl <- tryCatch(read.delim(file = file,
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sep = sep,
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header = header,
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strip.white = TRUE,
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dec = dec,
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na.strings = na,
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fileEncoding = 'UTF-8',
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encoding = 'UTF-8',
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stringsAsFactors = FALSE),
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error = function(e) {
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FALSE
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})
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if (import_tbl == FALSE) {
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cat("No clipboard content found.")
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if (Sys.info()['sysname'] %like% "Linux") {
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cat(" These functions do not work without X11 installed.")
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}
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cat("\n")
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return(invisible())
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}
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# use tibble, so column types will be translated correctly
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import_tbl <- as_tibble(import_tbl)
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if (startrow > 1) {
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# would else lose column headers
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import_tbl <- import_tbl[startrow:nrow(import_tbl),]
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}
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colnames(import_tbl) <- gsub('[.]+', '_', colnames(import_tbl))
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if (NCOL(import_tbl) == 1 & as_vector == TRUE) {
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import_tbl %>% pull(1)
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} else {
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@ -66,14 +78,14 @@ clipboard_export <- function(x,
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na = "",
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header = TRUE,
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info = TRUE) {
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x <- deparse(substitute(x))
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size <- x %>%
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get() %>%
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get() %>%
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object.size() %>%
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formatC(format = 'd') %>%
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as.integer()
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x <- get(x)
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if (is_Windows() == TRUE) {
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@ -86,18 +98,22 @@ clipboard_export <- function(x,
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on.exit(close(file))
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}
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write.table(x = x,
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file = file,
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sep = sep,
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na = na,
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row.names = FALSE,
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col.names = header,
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dec = dec,
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quote = FALSE)
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tryCatch(write.table(x = x,
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file = file,
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sep = sep,
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na = na,
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row.names = FALSE,
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col.names = header,
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dec = dec,
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quote = FALSE),
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error = function(e) {
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FALSE
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})
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if (info == TRUE) {
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cat("Successfully exported to clipboard:", NROW(x), "obs. of", NCOL(x), "variables.\n")
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}
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}
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is_Windows <- function() {
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@ -105,6 +121,10 @@ is_Windows <- function() {
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}
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check_xclip <- function() {
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if (!isTRUE(file.exists(Sys.which("xclip")[1L]))) {
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if (Sys.info()['sysname'] %like% "Linux") {
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stop("Please install Linux package xclip first.")
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} else {
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stop("Please install package xclip first (use `brew install xclip on macOS`).")
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}
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}
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}
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38
R/data.R
38
R/data.R
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@ -42,7 +42,7 @@
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#' @seealso \code{\link{microorganisms}}
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# last two columns created with:
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# antibiotics %>%
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# mutate(useful_gramnegative =
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# mutate(useful_gramnegative =
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# if_else(
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# atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
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# atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
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@ -116,39 +116,39 @@
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#' # ----------- #
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#' # PREPARATION #
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#' # ----------- #
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#'
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#'
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#' # Save this example dataset to an object, so we can edit it:
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#' my_data <- septic_patients
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#'
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#'
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#' # load the dplyr package to make data science A LOT easier
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#' library(dplyr)
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#'
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#'
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#' # Add first isolates to our dataset:
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#' my_data <- my_data %>%
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#' mutate(first_isolates = first_isolate(my_data, date, patient_id, bactid))
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#'
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#' my_data <- my_data %>%
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#' mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
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#'
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#' # -------- #
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#' # ANALYSIS #
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#' # -------- #
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#'
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#' # 1. Get the amoxicillin resistance percentages
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#'
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#' # 1. Get the amoxicillin resistance percentages
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#' # of E. coli, divided by hospital:
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#'
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#'
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#' my_data %>%
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#' filter(bactid == "ESCCOL",
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#' first_isolates == TRUE) %>%
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#' group_by(hospital_id) %>%
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#' first_isolates == TRUE) %>%
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#' group_by(hospital_id) %>%
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#' summarise(n = n(),
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#' amoxicillin_resistance = rsi(amox))
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#'
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#'
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#' # 2. Get the amoxicillin/clavulanic acid resistance
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#'
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#'
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#' # 2. Get the amoxicillin/clavulanic acid resistance
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#' # percentages of E. coli, trend over the years:
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#'
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#' my_data %>%
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#'
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#' my_data %>%
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#' filter(bactid == guess_bactid("E. coli"),
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#' first_isolates == TRUE) %>%
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#' group_by(year = format(date, "%Y")) %>%
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#' first_isolates == TRUE) %>%
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#' group_by(year = format(date, "%Y")) %>%
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#' summarise(n = n(),
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#' amoxclav_resistance = rsi(amcl, minimum = 20))
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"septic_patients"
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@ -18,14 +18,14 @@
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#' Determine first (weighted) isolates
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#'
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' @param tbl a \code{data.frame} containing isolates.
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#' @param col_date column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation
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#' @param col_patient_id column name of the unique IDs of the patients, supports tidyverse-like quotation
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#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset), supports tidyverse-like quotation
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#' @param col_date column name of the result date (or date that is was received on the lab)
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#' @param col_patient_id column name of the unique IDs of the patients
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#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)
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#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
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#' @param col_specimen column name of the specimen type or group, supports tidyverse-like quotation
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#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation
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#' @param col_specimen column name of the specimen type or group
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#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
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#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
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#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
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#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
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@ -36,8 +36,8 @@
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#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
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#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
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#' @param info print progress
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#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms, supports tidyverse-like quotation
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#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms, supports tidyverse-like quotation
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#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms
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#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms
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#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
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#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
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#'
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@ -54,15 +54,13 @@
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#' # septic_patients is a dataset available in the AMR package
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#' ?septic_patients
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#' my_patients <- septic_patients
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#'
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#'
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#' library(dplyr)
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#' my_patients$first_isolate <- my_patients %>%
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#' left_join_microorganisms() %>%
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#' first_isolate(col_date = date,
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#' col_patient_id = patient_id,
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#' col_genus = genus,
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#' col_species = species)
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#'
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#' first_isolate(col_date = "date",
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#' col_patient_id = "patient_id",
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#' col_bactid = "bactid")
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#'
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#' \dontrun{
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#'
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#' # set key antibiotics to a new variable
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@ -121,31 +119,25 @@ first_isolate <- function(tbl,
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info = TRUE,
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col_genus = NA,
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col_species = NA) {
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# support tidyverse-like quotation
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# col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
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# col_patient_id <- quasiquotate(deparse(substitute(col_patient_id)), col_patient_id)
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# col_bactid <- quasiquotate(deparse(substitute(col_bactid)), col_bactid)
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# col_genus <- quasiquotate(deparse(substitute(col_genus)), col_genus)
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# col_species <- quasiquotate(deparse(substitute(col_species)), col_species)
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# col_testcode <- quasiquotate(deparse(substitute(col_testcode)), col_testcode)
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# col_specimen <- quasiquotate(deparse(substitute(col_specimen)), col_specimen)
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# col_icu <- quasiquotate(deparse(substitute(col_icu)), col_icu)
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# col_keyantibiotics <- quasiquotate(deparse(substitute(col_keyantibiotics)), col_keyantibiotics)
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# bactid OR genus+species must be available
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if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) {
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stop('`col_bactid or both `col_genus` and `col_species` must be available.')
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}
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# check if columns exist
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check_columns_existance <- function(column, tblname = tbl) {
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if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
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stop('Please check tbl for existance.')
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}
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if (!is.na(column)) {
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if (!(column %in% colnames(tblname))) {
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stop('Column ', column, ' not found.')
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stop('Column `', column, '` not found.')
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}
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}
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}
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check_columns_existance(col_date)
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check_columns_existance(col_patient_id)
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check_columns_existance(col_bactid)
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@ -154,13 +146,13 @@ first_isolate <- function(tbl,
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check_columns_existance(col_testcode)
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check_columns_existance(col_icu)
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check_columns_existance(col_keyantibiotics)
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if (!is.na(col_bactid)) {
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tbl <- tbl %>% left_join_microorganisms()
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tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
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col_genus <- "genus"
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col_species <- "species"
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}
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if (is.na(col_testcode)) {
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testcodes_exclude <- NA
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}
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@ -168,18 +160,18 @@ first_isolate <- function(tbl,
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if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
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cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
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}
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if (is.na(col_icu)) {
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icu_exclude <- FALSE
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} else {
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tbl <- tbl %>%
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mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
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}
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if (is.na(col_specimen)) {
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filter_specimen <- ''
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}
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specgroup.notice <- ''
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weighted.notice <- ''
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# filter on specimen group and keyantibiotics when they are filled in
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@ -196,11 +188,11 @@ first_isolate <- function(tbl,
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} else {
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tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
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}
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if (is.na(testcodes_exclude[1])) {
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testcodes_exclude <- ''
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}
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# create new dataframe with original row index and right sorting
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tbl <- tbl %>%
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mutate(first_isolate_row_index = 1:nrow(tbl),
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@ -211,9 +203,9 @@ first_isolate <- function(tbl,
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genus = tbl %>% pull(col_genus)) %>%
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mutate(species = if_else(is.na(species), '', species),
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genus = if_else(is.na(genus), '', genus))
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if (filter_specimen == '') {
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if (icu_exclude == FALSE) {
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if (info == TRUE) {
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cat('Isolates from ICU will *NOT* be ignored.\n')
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@ -235,7 +227,7 @@ first_isolate <- function(tbl,
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col_genus,
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col_species,
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col_date))
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suppressWarnings(
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row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
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)
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@ -243,7 +235,7 @@ first_isolate <- function(tbl,
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row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
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)
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}
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} else {
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# sort on specimen and only analyse these row to save time
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if (icu_exclude == FALSE) {
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@ -282,9 +274,9 @@ first_isolate <- function(tbl,
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& tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
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)
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}
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}
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if (abs(row.start) == Inf | abs(row.end) == Inf) {
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if (info == TRUE) {
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cat('No isolates found.\n')
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@ -297,14 +289,14 @@ first_isolate <- function(tbl,
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}
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return(tbl %>% pull(real_first_isolate))
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}
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scope.size <- tbl %>%
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filter(row_number() %>%
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between(row.start,
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row.end),
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genus != '') %>%
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nrow()
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# Analysis of first isolate ----
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all_first <- tbl %>%
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mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
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@ -316,7 +308,7 @@ first_isolate <- function(tbl,
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mutate(days_diff = if_else(other_pat_or_mo == FALSE,
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(date_lab - lag(date_lab)) + lag(days_diff),
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0))
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if (col_keyantibiotics != '') {
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if (info == TRUE) {
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if (type == 'keyantibiotics') {
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@ -365,7 +357,7 @@ first_isolate <- function(tbl,
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TRUE,
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FALSE))
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}
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# first one as TRUE
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all_first[row.start, 'real_first_isolate'] <- TRUE
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# no tests that should be included, or ICU
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@ -375,15 +367,15 @@ first_isolate <- function(tbl,
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if (icu_exclude == TRUE) {
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all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
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}
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# NA's where genus is unavailable
|
||||
all_first <- all_first %>%
|
||||
mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
|
||||
|
||||
|
||||
all_first <- all_first %>%
|
||||
arrange(first_isolate_row_index) %>%
|
||||
pull(real_first_isolate)
|
||||
|
||||
|
||||
if (info == TRUE) {
|
||||
cat(paste0('\nFound ',
|
||||
all_first %>% sum(na.rm = TRUE),
|
||||
|
|
@ -393,13 +385,13 @@ first_isolate <- function(tbl,
|
|||
(all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
|
||||
' of total)\n'))
|
||||
}
|
||||
|
||||
|
||||
if (output_logical == FALSE) {
|
||||
all_first <- all_first %>% as.integer()
|
||||
}
|
||||
|
||||
|
||||
all_first
|
||||
|
||||
|
||||
}
|
||||
|
||||
#' Key antibiotics based on bacteria ID
|
||||
|
|
@ -409,10 +401,10 @@ first_isolate <- function(tbl,
|
|||
#' @param info print warnings
|
||||
#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics, case-insensitive
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% mutate if_else
|
||||
#' @importFrom dplyr %>% mutate if_else
|
||||
#' @return Character of length 1.
|
||||
#' @seealso \code{\link{mo_property}} \code{\link{antibiotics}}
|
||||
#' @examples
|
||||
#' @examples
|
||||
#' \donttest{
|
||||
#' #' # set key antibiotics to a new variable
|
||||
#' tbl$keyab <- key_antibiotics(tbl)
|
||||
|
|
@ -440,9 +432,9 @@ key_antibiotics <- function(tbl,
|
|||
teic = 'teic',
|
||||
trsu = 'trsu',
|
||||
vanc = 'vanc') {
|
||||
|
||||
|
||||
keylist <- character(length = nrow(tbl))
|
||||
|
||||
|
||||
# check columns
|
||||
col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
|
||||
clin, clox, doxy, gent, line, mero, peni,
|
||||
|
|
@ -486,12 +478,12 @@ key_antibiotics <- function(tbl,
|
|||
teic <- col.list[17]
|
||||
trsu <- col.list[18]
|
||||
vanc <- col.list[19]
|
||||
|
||||
|
||||
# join microorganisms
|
||||
tbl <- tbl %>% left_join_microorganisms(col_bactid)
|
||||
|
||||
|
||||
tbl$key_ab <- NA_character_
|
||||
|
||||
|
||||
# Staphylococcus
|
||||
list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
|
||||
list_ab <- list_ab[list_ab %in% colnames(tbl)]
|
||||
|
|
@ -501,7 +493,7 @@ key_antibiotics <- function(tbl,
|
|||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
|
||||
# Rest of Gram +
|
||||
list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
|
||||
list_ab <- list_ab[list_ab %in% colnames(tbl)]
|
||||
|
|
@ -511,7 +503,7 @@ key_antibiotics <- function(tbl,
|
|||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
|
||||
# Gram -
|
||||
list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
|
||||
list_ab <- list_ab[list_ab %in% colnames(tbl)]
|
||||
|
|
@ -521,76 +513,76 @@ key_antibiotics <- function(tbl,
|
|||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
|
||||
# format
|
||||
tbl <- tbl %>%
|
||||
mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
|
||||
|
||||
|
||||
tbl$key_ab
|
||||
|
||||
|
||||
}
|
||||
|
||||
#' @importFrom dplyr progress_estimated %>%
|
||||
#' @noRd
|
||||
key_antibiotics_equal <- function(x,
|
||||
y,
|
||||
type = c("keyantibiotics", "points"),
|
||||
y,
|
||||
type = c("keyantibiotics", "points"),
|
||||
ignore_I = TRUE,
|
||||
points_threshold = 2,
|
||||
points_threshold = 2,
|
||||
info = FALSE) {
|
||||
# x is active row, y is lag
|
||||
|
||||
type <- type[1]
|
||||
|
||||
|
||||
if (length(x) != length(y)) {
|
||||
stop('Length of `x` and `y` must be equal.')
|
||||
}
|
||||
|
||||
|
||||
result <- logical(length(x))
|
||||
|
||||
|
||||
if (info == TRUE) {
|
||||
p <- dplyr::progress_estimated(length(x))
|
||||
}
|
||||
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
|
||||
if (info == TRUE) {
|
||||
p$tick()$print()
|
||||
}
|
||||
|
||||
|
||||
if (is.na(x[i])) {
|
||||
x[i] <- ''
|
||||
}
|
||||
if (is.na(y[i])) {
|
||||
y[i] <- ''
|
||||
}
|
||||
|
||||
|
||||
if (nchar(x[i]) != nchar(y[i])) {
|
||||
|
||||
|
||||
result[i] <- FALSE
|
||||
|
||||
|
||||
} else if (x[i] == '' & y[i] == '') {
|
||||
|
||||
|
||||
result[i] <- TRUE
|
||||
|
||||
|
||||
} else {
|
||||
|
||||
|
||||
x2 <- strsplit(x[i], "")[[1]]
|
||||
y2 <- strsplit(y[i], "")[[1]]
|
||||
|
||||
|
||||
if (type == 'points') {
|
||||
# count points for every single character:
|
||||
# - no change is 0 points
|
||||
# - I <-> S|R is 0.5 point
|
||||
# - S|R <-> R|S is 1 point
|
||||
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
||||
|
||||
|
||||
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
|
||||
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
|
||||
|
||||
|
||||
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
|
||||
result[i] <- ((points / 2) >= points_threshold)
|
||||
|
||||
|
||||
} else if (type == 'keyantibiotics') {
|
||||
# check if key antibiotics are exactly the same
|
||||
# also possible to ignore I, so only S <-> R and S <-> R are counted
|
||||
|
|
@ -599,15 +591,15 @@ key_antibiotics_equal <- function(x,
|
|||
} else {
|
||||
valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
|
||||
}
|
||||
|
||||
|
||||
# remove invalid values (like "-", NA) on both locations
|
||||
x2[which(!x2 %in% valid_chars)] <- '?'
|
||||
x2[which(!y2 %in% valid_chars)] <- '?'
|
||||
y2[which(!x2 %in% valid_chars)] <- '?'
|
||||
y2[which(!y2 %in% valid_chars)] <- '?'
|
||||
|
||||
|
||||
result[i] <- all(x2 == y2)
|
||||
|
||||
|
||||
} else {
|
||||
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
||||
}
|
||||
|
|
@ -627,7 +619,7 @@ key_antibiotics_equal <- function(x,
|
|||
#' @importFrom dplyr %>% filter slice pull
|
||||
#' @return Character (vector).
|
||||
#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
|
||||
#' @examples
|
||||
#' @examples
|
||||
#' # These examples all return "STAAUR", the ID of S. aureus:
|
||||
#' guess_bactid("stau")
|
||||
#' guess_bactid("STAU")
|
||||
|
|
@ -646,7 +638,7 @@ guess_bactid <- function(x) {
|
|||
# add start and stop
|
||||
x_species <- paste(x, 'species')
|
||||
x <- paste0('^', x, '$')
|
||||
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
if (tolower(x[i]) == '^e.*coli$') {
|
||||
# avoid detection of Entamoeba coli in case of E. coli
|
||||
|
|
@ -681,7 +673,7 @@ guess_bactid <- function(x) {
|
|||
|
||||
# let's try the ID's first
|
||||
found <- AMR::microorganisms %>% filter(bactid == x.bak[i])
|
||||
|
||||
|
||||
if (nrow(found) == 0) {
|
||||
# now try exact match
|
||||
found <- AMR::microorganisms %>% filter(fullname == x[i])
|
||||
|
|
@ -709,10 +701,10 @@ guess_bactid <- function(x) {
|
|||
x.bak[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
|
||||
found <- AMR::microorganisms %>% filter(fullname %like% paste0('^', x[i]))
|
||||
}
|
||||
|
||||
|
||||
if (nrow(found) != 0) {
|
||||
x[i] <- found %>%
|
||||
slice(1) %>%
|
||||
x[i] <- found %>%
|
||||
slice(1) %>%
|
||||
pull(bactid)
|
||||
} else {
|
||||
x[i] <- ""
|
||||
|
|
|
|||
6
R/join.R
6
R/join.R
|
|
@ -10,12 +10,12 @@
|
|||
#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
|
||||
#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
|
||||
#' @export
|
||||
#' @examples
|
||||
#' @examples
|
||||
#' left_join_microorganisms("STAAUR")
|
||||
#'
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% left_join_microorganisms()
|
||||
#'
|
||||
#'
|
||||
#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
#' to = as.Date("2018-01-07"),
|
||||
#' by = 1),
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ clipboard_export(x, sep = "\\t", dec = ".", na = "", header = TRUE,
|
|||
data.frame
|
||||
}
|
||||
\description{
|
||||
These are helper functions around \code{\link{read.table}} and \code{\link{write.table}} to import from and export to clipboard. The data will be read and written as tab-separated by default, which makes it possible to copy and paste from other software like Excel and SPSS without further transformation.
|
||||
These are helper functions around \code{\link{read.table}} and \code{\link{write.table}} to import from and export to clipboard, with support for Windows, Linux and macOS. The data will be read and written as tab-separated by default, which makes it possible to copy and paste from other software like Excel and SPSS without further transformation.
|
||||
}
|
||||
\details{
|
||||
For \code{clipboard_export}, the reserved clipboard size for exporting will be set automatically to 125\% of the object size of \code{x}. This way, it is possible to export data with thousands of rows as the only limit will be your systems RAM.
|
||||
|
|
|
|||
|
|
@ -14,17 +14,17 @@ first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
|
|||
\arguments{
|
||||
\item{tbl}{a \code{data.frame} containing isolates.}
|
||||
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation}
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab)}
|
||||
|
||||
\item{col_patient_id}{column name of the unique IDs of the patients, supports tidyverse-like quotation}
|
||||
\item{col_patient_id}{column name of the unique IDs of the patients}
|
||||
|
||||
\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset), supports tidyverse-like quotation}
|
||||
\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)}
|
||||
|
||||
\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}
|
||||
|
||||
\item{col_specimen}{column name of the specimen type or group, supports tidyverse-like quotation}
|
||||
\item{col_specimen}{column name of the specimen type or group}
|
||||
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation}
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
|
||||
|
||||
\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.}
|
||||
|
||||
|
|
@ -46,9 +46,9 @@ first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
|
|||
|
||||
\item{info}{print progress}
|
||||
|
||||
\item{col_genus}{(deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms, supports tidyverse-like quotation}
|
||||
\item{col_genus}{(deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms}
|
||||
|
||||
\item{col_species}{(deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms, supports tidyverse-like quotation}
|
||||
\item{col_species}{(deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms}
|
||||
}
|
||||
\value{
|
||||
A vector to add to table, see Examples.
|
||||
|
|
@ -73,12 +73,10 @@ my_patients <- septic_patients
|
|||
|
||||
library(dplyr)
|
||||
my_patients$first_isolate <- my_patients \%>\%
|
||||
left_join_microorganisms() \%>\%
|
||||
first_isolate(col_date = date,
|
||||
col_patient_id = patient_id,
|
||||
col_genus = genus,
|
||||
col_species = species)
|
||||
|
||||
first_isolate(col_date = "date",
|
||||
col_patient_id = "patient_id",
|
||||
col_bactid = "bactid")
|
||||
|
||||
\dontrun{
|
||||
|
||||
# set key antibiotics to a new variable
|
||||
|
|
|
|||
|
|
@ -38,31 +38,31 @@ my_data <- septic_patients
|
|||
library(dplyr)
|
||||
|
||||
# Add first isolates to our dataset:
|
||||
my_data <- my_data \%>\%
|
||||
mutate(first_isolates = first_isolate(my_data, date, patient_id, bactid))
|
||||
my_data <- my_data \%>\%
|
||||
mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
|
||||
|
||||
# -------- #
|
||||
# ANALYSIS #
|
||||
# -------- #
|
||||
|
||||
# 1. Get the amoxicillin resistance percentages
|
||||
# 1. Get the amoxicillin resistance percentages
|
||||
# of E. coli, divided by hospital:
|
||||
|
||||
my_data \%>\%
|
||||
filter(bactid == "ESCCOL",
|
||||
first_isolates == TRUE) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
first_isolates == TRUE) \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(n = n(),
|
||||
amoxicillin_resistance = rsi(amox))
|
||||
|
||||
|
||||
# 2. Get the amoxicillin/clavulanic acid resistance
|
||||
|
||||
|
||||
# 2. Get the amoxicillin/clavulanic acid resistance
|
||||
# percentages of E. coli, trend over the years:
|
||||
|
||||
my_data \%>\%
|
||||
my_data \%>\%
|
||||
filter(bactid == guess_bactid("E. coli"),
|
||||
first_isolates == TRUE) \%>\%
|
||||
group_by(year = format(date, "\%Y")) \%>\%
|
||||
first_isolates == TRUE) \%>\%
|
||||
group_by(year = format(date, "\%Y")) \%>\%
|
||||
summarise(n = n(),
|
||||
amoxclav_resistance = rsi(amcl, minimum = 20))
|
||||
}
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -1,9 +1,9 @@
|
|||
context("clipboard.R")
|
||||
|
||||
test_that("clipboard works", {
|
||||
skip_on_os(c("linux", "solaris"))
|
||||
t1 <<- AMR::antibiotics # why is the <<- needed? Won't work without it...
|
||||
clipboard_export(t1, info = FALSE)
|
||||
t2 <- clipboard_import()
|
||||
skip_if(is.null(t1) | is.null(t2), message = "No clipboard content found: skipping.")
|
||||
expect_equal(t1, t2)
|
||||
})
|
||||
|
|
|
|||
Loading…
Reference in New Issue