mirror of
https://github.com/msberends/AMR.git
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update to septic_patients, speed improvements
This commit is contained in:
@ -221,7 +221,6 @@ as.bactid <- function(x) {
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}
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class(x) <- "bactid"
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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16
R/classes.R
16
R/classes.R
@ -21,18 +21,18 @@
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#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
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#' @rdname as.rsi
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#' @param x vector
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#' @return Ordered factor with new class \code{rsi} and new attributes \code{package} and \code{package.version}
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#' @return Ordered factor with new class \code{rsi} and new attribute \code{package}
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#' @keywords rsi
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#' @export
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#' @importFrom dplyr %>%
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#' @importFrom utils packageDescription
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#' @seealso \code{\link{as.mic}}
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#' @examples
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
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#' is.rsi(rsi_data)
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#'
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#' # this can also coerce combined MIC/RSI values:
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#' as.rsi("<= 0.002; R") # will return R
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#' as.rsi("<= 0.002; S") # will return S
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#'
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#' plot(rsi_data) # for percentages
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#' barplot(rsi_data) # for frequencies
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@ -76,7 +76,6 @@ as.rsi <- function(x) {
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x <- x %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
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class(x) <- c('rsi', 'ordered', 'factor')
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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}
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@ -196,21 +195,21 @@ barplot.rsi <- function(height, ...) {
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#' Class 'mic'
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#'
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#' This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
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#' This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
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#' @rdname as.mic
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#' @param x vector
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#' @param na.rm a logical indicating whether missing values should be removed
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#' @return Ordered factor with new class \code{mic} and new attributes \code{package} and \code{package.version}
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#' @return Ordered factor with new class \code{mic} and new attribute \code{package}
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#' @keywords mic
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#' @export
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#' @importFrom dplyr %>%
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#' @importFrom utils packageDescription
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#' @seealso \code{\link{as.rsi}}
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#' @examples
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#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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#' is.mic(mic_data)
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#'
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#' # this can also coerce combined MIC/RSI values:
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#' as.mic("<=0.002; R") # will return <=0.002
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#' as.mic("<=0.002; S") # will return <=0.002
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#'
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#' plot(mic_data)
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#' barplot(mic_data)
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@ -319,7 +318,6 @@ as.mic <- function(x, na.rm = FALSE) {
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ordered = TRUE)
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class(x) <- c('mic', 'ordered', 'factor')
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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}
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24
R/data.R
24
R/data.R
@ -252,7 +252,7 @@
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#' \item{\code{type_nl}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
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#' \item{\code{gramstain_nl}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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# source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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#' @seealso \code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
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"microorganisms"
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@ -264,14 +264,14 @@
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#' \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
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#' \item{\code{bactid}}{Code of microorganism in \code{\link{microorganisms}}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
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# source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
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#' @seealso \code{\link{guess_bactid}} \code{\link{microorganisms}}
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"microorganisms.umcg"
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#' Dataset with 2000 blood culture isolates of septic patients
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#'
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#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1.
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#' @format A data.frame with 2000 observations and 47 variables:
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#' @format A data.frame with 2000 observations and 49 variables:
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#' \describe{
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#' \item{\code{date}}{date of receipt at the laboratory}
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#' \item{\code{hospital_id}}{ID of the hospital}
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@ -282,9 +282,9 @@
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#' \item{\code{sex}}{sex of the patient}
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#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
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#' \item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
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#' \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} and can be translated with \code{\link{abname}}}
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#' \item{\code{peni:rifa}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{abname}}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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# source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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#' @examples
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#' # ----------- #
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#' # PREPARATION #
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@ -304,15 +304,15 @@
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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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#' # of E. coli, divided by hospital:
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#' # 1. Get the amoxicillin resistance percentages (p)
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#' # and numbers (n) of E. coli, divided by hospital:
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#'
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#' my_data %>%
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#' filter(bactid == "ESCCOL",
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#' filter(bactid == guess_bactid("E. coli"),
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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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#' summarise(n = n_rsi(amox),
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#' p = resistance(amox))
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#'
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#'
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#' # 2. Get the amoxicillin/clavulanic acid resistance
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@ -322,6 +322,6 @@
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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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#' summarise(n = n(),
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#' amoxclav_resistance = rsi(amcl, minimum = 20))
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#' summarise(n = n_rsi(amcl),
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#' p = resistance(amcl, minimum = 20))
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"septic_patients"
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@ -41,7 +41,7 @@
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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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#' @section Key antibiotics:
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#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates: \cr
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#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
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#'
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#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
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#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
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@ -65,6 +65,24 @@
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#' col_patient_id = "patient_id",
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#' col_bactid = "bactid")
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#'
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#' # Now let's see if first isolates matter:
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#' A <- my_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(gent), # gentamicin
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#' resistance = resistance(gent))
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#'
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#' B <- my_patients %>%
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#' filter(first_isolate == TRUE) %>%
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(gent), # gentamicin
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#' resistance = resistance(gent))
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#'
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#' # Have a look at A and B. B is more reliable because every isolate is
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#' # counted once. Gentamicin resitance in hospital D seems to be 5%
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#' # higher than originally thought.
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#'
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#' ## OTHER EXAMPLES:
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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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@ -153,7 +171,7 @@ first_isolate <- function(tbl,
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if (!is.na(col_bactid)) {
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if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
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tbl[, col_bactid] <- tbl %>% pull(col_bactid) %>% as.bactid()
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warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
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}
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tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
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col_genus <- "genus"
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@ -179,7 +197,6 @@ first_isolate <- function(tbl,
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filter_specimen <- ''
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}
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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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if (!is.na(filter_specimen) & filter_specimen != '') {
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check_columns_existance(col_specimen, tbl)
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@ -317,7 +334,9 @@ first_isolate <- function(tbl,
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(date_lab - lag(date_lab)) + lag(days_diff),
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0))
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weighted.notice <- ''
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if (col_keyantibiotics != '') {
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weighted.notice <- 'weighted '
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if (info == TRUE) {
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if (type == 'keyantibiotics') {
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cat('[Criteria] Inclusion based on key antibiotics, ')
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3
R/freq.R
3
R/freq.R
@ -415,7 +415,6 @@ frequency_tbl <- function(x,
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class(df) <- c('frequency_tbl', class(df))
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attr(df, 'package') <- 'AMR'
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attr(df, 'package.version') <- packageDescription('AMR')$Version
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if (markdown == TRUE) {
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tbl_format <- 'markdown'
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@ -567,7 +566,6 @@ print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default =
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#' @export
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as.data.frame.frequency_tbl <- function(x, ...) {
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attr(x, 'package') <- NULL
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attr(x, 'package.version') <- NULL
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attr(x, 'opt') <- NULL
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as.data.frame.data.frame(x, ...)
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}
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@ -578,7 +576,6 @@ as.data.frame.frequency_tbl <- function(x, ...) {
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#' @importFrom dplyr as_tibble
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as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
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attr(x, 'package') <- NULL
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attr(x, 'package.version') <- NULL
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attr(x, 'opt') <- NULL
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as_tibble(x = as.data.frame(x), validate = validate, ..., rownames = rownames)
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}
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@ -26,8 +26,8 @@
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#' df2 <- left_join_microorganisms(df, "bacteria_id")
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#' colnames(df2)
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inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -48,8 +48,8 @@ inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
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#' @rdname join
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#' @export
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left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -70,8 +70,8 @@ left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
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#' @rdname join
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#' @export
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right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -92,8 +92,8 @@ right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...
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#' @rdname join
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#' @export
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full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -114,8 +114,8 @@ full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...)
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#' @rdname join
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#' @export
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semi_join_microorganisms <- function(x, by = 'bactid', ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -132,8 +132,8 @@ semi_join_microorganisms <- function(x, by = 'bactid', ...) {
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#' @rdname join
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#' @export
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anti_join_microorganisms <- function(x, by = 'bactid', ...) {
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if (!any(class(x) %in% c("bactid", "data.frame", "matrix"))) {
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x <- data.frame(bactid = as.bactid(x), stringsAsFactors = FALSE)
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if (!any(class(x) %in% c("data.frame", "matrix"))) {
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x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
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}
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -42,19 +42,26 @@
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#' @importFrom dplyr %>% mutate if_else
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#' @seealso \code{\link{first_isolate}}
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#' @examples
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#' \dontrun{
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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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#' library(dplyr)
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#' # set key antibiotics to a new variable
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#' tbl$keyab <- key_antibiotics(tbl)
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#' my_patients <- my_patients %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
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#' mutate(
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#' # now calculate first isolates
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#' first_regular = first_isolate(., "date", "patient_id", "bactid"),
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#' # and first WEIGHTED isolates
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#' first_weighted = first_isolate(., "date", "patient_id", "bactid",
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#' col_keyantibiotics = "keyab")
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#' )
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#'
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#' # add regular first isolates
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#' tbl$first_isolate <-
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#' first_isolate(tbl)
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#' # Check the difference, in this data set it results in 7% more isolates:
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#' sum(my_patients$first_regular, na.rm = TRUE)
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#' sum(my_patients$first_weighted, na.rm = TRUE)
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#'
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#' # add first WEIGHTED isolates using key antibiotics
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#' tbl$first_isolate_weighed <-
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#' first_isolate(tbl,
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#' col_keyantibiotics = 'keyab')
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#' }
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#'
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#' # output of the `key_antibiotics` function could be like this:
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#' strainA <- "SSSRR.S.R..S"
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@ -169,87 +176,80 @@ key_antibiotics_equal <- function(x,
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points_threshold = 2,
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info = FALSE) {
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# x is active row, y is lag
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type <- type[1]
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if (length(x) != length(y)) {
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stop('Length of `x` and `y` must be equal.')
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}
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# only show progress bar on points or when at least 5000 isolates
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info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
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result <- logical(length(x))
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if (type == "keyantibiotics") {
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if (ignore_I == TRUE) {
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# evaluation using regular expression will treat '.' as any character
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# so I is actually ignored then
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x <- gsub('I', '.', x, ignore.case = TRUE)
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y <- gsub('I', '.', y, ignore.case = TRUE)
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if (info_needed == TRUE) {
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p <- dplyr::progress_estimated(length(x))
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}
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||||
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for (i in 1:length(x)) {
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||||
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||||
if (info_needed == TRUE) {
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p$tick()$print()
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}
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|
||||
for (i in 1:length(x)) {
|
||||
if (is.na(x[i])) {
|
||||
x[i] <- ''
|
||||
}
|
||||
if (is.na(y[i])) {
|
||||
y[i] <- ''
|
||||
}
|
||||
|
||||
if (x[i] == y[i]) {
|
||||
|
||||
result[i] <- TRUE
|
||||
|
||||
} else if (nchar(x[i]) != nchar(y[i])) {
|
||||
|
||||
result[i] <- FALSE
|
||||
|
||||
} else {
|
||||
|
||||
x_split <- strsplit(x[i], "")[[1]]
|
||||
y_split <- strsplit(y[i], "")[[1]]
|
||||
y_split[x_split == "."] <- "."
|
||||
x_split[y_split == "."] <- "."
|
||||
x_checkfor <- paste(x_split, collapse = "")
|
||||
y_checkfor <- paste(y_split, collapse = "")
|
||||
result[i] <- nchar(x[i]) == nchar(y[i]) &
|
||||
(x_checkfor %like% y_checkfor |
|
||||
y_checkfor %like% x_checkfor)
|
||||
}
|
||||
return(result)
|
||||
|
||||
} else {
|
||||
if (type == 'keyantibiotics') {
|
||||
|
||||
if (type != 'points') {
|
||||
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
||||
}
|
||||
if (ignore_I == TRUE) {
|
||||
x_split[x_split == "I"] <- "."
|
||||
y_split[y_split == "I"] <- "."
|
||||
}
|
||||
|
||||
if (info == TRUE) {
|
||||
p <- dplyr::progress_estimated(length(x))
|
||||
}
|
||||
y_split[x_split == "."] <- "."
|
||||
x_split[y_split == "."] <- "."
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
if (info == TRUE) {
|
||||
p$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]]
|
||||
result[i] <- all(x_split == y_split)
|
||||
|
||||
} else 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())
|
||||
suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
|
||||
suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
|
||||
|
||||
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
|
||||
result[i] <- ((points / 2) >= points_threshold)
|
||||
points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
|
||||
result[i] <- points >= points_threshold
|
||||
|
||||
} else {
|
||||
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
||||
}
|
||||
}
|
||||
if (info == TRUE) {
|
||||
cat('\n')
|
||||
}
|
||||
result
|
||||
}
|
||||
if (info_needed == TRUE) {
|
||||
cat('\n')
|
||||
}
|
||||
result
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user