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new portion functions

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dr. M.S. (Matthijs) Berends
2018-08-10 15:01:05 +02:00
parent ae2433a020
commit 53fa198e35
19 changed files with 892 additions and 1140 deletions
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29
man/first_isolate.Rd
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@ -75,31 +75,32 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
}
\examples{
# septic_patients is a dataset available in the AMR package
# septic_patients is a dataset available in the AMR package. It is true data.
?septic_patients
my_patients <- septic_patients
library(dplyr)
my_patients$first_isolate <- my_patients \%>\%
first_isolate(col_date = "date",
col_patient_id = "patient_id",
col_bactid = "bactid")
my_patients <- septic_patients \%>\%
mutate(first_isolate = first_isolate(.,
col_date = "date",
col_patient_id = "patient_id",
col_bactid = "bactid"))
# Now let's see if first isolates matter:
A <- my_patients \%>\%
group_by(hospital_id) \%>\%
summarise(count = n_rsi(gent), # gentamicin
resistance = resistance(gent))
summarise(count = n_rsi(gent), # gentamicin availability
resistance = portion_IR(gent)) # gentamicin resistance
B <- my_patients \%>\%
filter(first_isolate == TRUE) \%>\% # the 1st isolate filter
filter(first_isolate == TRUE) \%>\% # the 1st isolate filter
group_by(hospital_id) \%>\%
summarise(count = n_rsi(gent),
resistance = resistance(gent))
summarise(count = n_rsi(gent), # gentamicin availability
resistance = portion_IR(gent)) # gentamicin resistance
# Have a look at A and B. B is more reliable because every isolate is
# counted once. Gentamicin resitance in hospital D appears to be 5\%
# higher than originally thought.
# Have a look at A and B.
# B is more reliable because every isolate is only counted once.
# Gentamicin resitance in hospital D appears to be 5.4\% higher than
# when you (erroneously) would have used all isolates!
## OTHER EXAMPLES:

29
man/n_rsi.Rd Normal file
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@ -0,0 +1,29 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/n_rsi.R
\name{n_rsi}
\alias{n_rsi}
\title{Count cases with antimicrobial results}
\usage{
n_rsi(ab1, ab2 = NULL)
}
\arguments{
\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
}
\description{
This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
}
\examples{
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = portion_S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
}
\seealso{
The \code{\link{portion}} functions to calculate resistance and susceptibility.
}

127
man/portion.Rd Normal file
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@ -0,0 +1,127 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/portion.R
\name{portion}
\alias{portion}
\alias{portion_R}
\alias{portion_IR}
\alias{portion_I}
\alias{portion_SI}
\alias{portion_S}
\title{Calculate resistance of isolates}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
portion_IR(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
portion_I(ab1, minimum = 30, as_percent = FALSE)
portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
\code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
}
\details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
\cr
For two antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
\cr
Theoretically for three antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
}
}
\examples{
# Calculate resistance
portion_R(septic_patients$amox)
portion_IR(septic_patients$amox)
# Or susceptibility
portion_S(septic_patients$amox)
portion_SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to count e.g. non-susceptible isolates:
portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = portion_S(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = portion_R(cipr, as_percent = TRUE),
I = portion_I(cipr, as_percent = TRUE),
S = portion_S(cipr, as_percent = TRUE),
n = n_rsi(cipr), # works like n_distinct in dplyr
total = n()) # NOT the amount of tested isolates!
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
portion_S(septic_patients$amcl) # S = 67.3\%
n_rsi(septic_patients$amcl) # n = 1570
portion_S(septic_patients$gent) # S = 74.0\%
n_rsi(septic_patients$gent) # n = 1842
with(septic_patients,
portion_S(amcl, gent)) # S = 92.1\%
with(septic_patients, # n = 1504
n_rsi(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = portion_S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
}
}
\seealso{
\code{\link{n_rsi}} to count cases with antimicrobial results.
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{rsi_df}
\keyword{susceptibility}

4
man/resistance_predict.Rd
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@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsi_IR.R
% Please edit documentation in R/resistance_predict.R
\name{resistance_predict}
\alias{resistance_predict}
\alias{rsi_predict}
@ -118,5 +118,5 @@ if (!require(ggplot2)) {
}
}
\seealso{
\code{\link{resistance}} \cr \code{\link{lm}} \code{\link{glm}}
The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
}

97
man/rsi.Rd
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@ -1,106 +1,25 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsi_IR.R
% Please edit documentation in R/rsi.R
\name{rsi}
\alias{rsi}
\title{Calculate resistance of isolates}
\usage{
rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
as_percent = FALSE, info = FALSE, warning = TRUE)
rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
as_percent = FALSE, ...)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
\item{interpretation}{antimicrobial interpretation}
\item{interpretation}{antimicrobial interpretation to check for}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{info}{calculate the amount of available isolates and print it, like \code{n = 423}}
\item{warning}{show a warning when the available amount of isolates is below \code{minimum}}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
\item{...}{deprecated parameters to support usage on older versions}
}
\description{
This function is deprecated. Use \code{\link{rsi_IR}} instead.
}
\details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{rsi_IR} and \code{rsi_S}, respectively. All functions use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{\link{rsi}} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
\cr
For two antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
\cr
Theoretically for three antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
}
}
\examples{
# Calculate resistance
rsi_R(septic_patients$amox)
rsi_IR(septic_patients$amox)
# Or susceptibility
rsi_S(septic_patients$amox)
rsi_SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
rsi_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = rsi_S(cipr),
n = rsi_n(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = rsi_R(cipr, as_percent = TRUE),
I = rsi_I(cipr, as_percent = TRUE),
S = rsi_S(cipr, as_percent = TRUE),
n = rsi_n(cipr), # also: n_rsi, works like n_distinct in dplyr
total = n()) # this is the length, NOT the amount of tested isolates
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
rsi_S(septic_patients$amcl) # S = 67.3\%
rsi_n(septic_patients$amcl) # n = 1570
rsi_S(septic_patients$gent) # S = 74.0\%
rsi_n(septic_patients$gent) # n = 1842
with(septic_patients,
rsi_S(amcl, gent)) # S = 92.1\%
with(septic_patients, # n = 1504
rsi_n(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = rsi_S(cipr, as_percent = TRUE),
cipro_n = rsi_n(cipr),
genta_p = rsi_S(gent, as_percent = TRUE),
genta_n = rsi_n(gent),
combination_p = rsi_S(cipr, gent, as_percent = TRUE),
combination_n = rsi_n(cipr, gent))
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = rsi_S(amox, metr), # amoxicillin with metronidazole
n = rsi_n(amox, metr))
}
This function is deprecated. Use the \code{\link{portion}} functions instead.
}

141
man/rsi_IR.Rd
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@ -1,141 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsi_IR.R
\name{rsi_IR}
\alias{rsi_IR}
\alias{rsi_R}
\alias{rsi_I}
\alias{rsi_SI}
\alias{rsi_S}
\alias{resistance}
\alias{intermediate}
\alias{susceptibility}
\alias{rsi_n}
\alias{n_rsi}
\title{Calculate resistance of isolates}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
rsi_R(ab1, minimum = 30, as_percent = FALSE)
rsi_IR(ab1, minimum = 30, as_percent = FALSE)
rsi_I(ab1, minimum = 30, as_percent = FALSE)
rsi_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
rsi_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
resistance(ab1, include_I = TRUE, minimum = 30, as_percent = FALSE)
intermediate(ab1, minimum = 30, as_percent = FALSE)
susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
as_percent = FALSE)
rsi_n(ab1, ab2 = NULL)
n_rsi(ab1, ab2 = NULL)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
\code{rsi_R} and \code{rsi_IR} can be used to calculate resistance, \code{rsi_S} and \code{rsi_SI} can be used to calculate susceptibility.\cr
\code{rsi_n} counts all cases where antimicrobial interpretations are available.
}
\details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{rsi_IR} and \code{rsi_S}, respectively. All functions use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{\link{rsi}} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
\cr
For two antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
\cr
Theoretically for three antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
}
}
\examples{
# Calculate resistance
rsi_R(septic_patients$amox)
rsi_IR(septic_patients$amox)
# Or susceptibility
rsi_S(septic_patients$amox)
rsi_SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
rsi_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = rsi_S(cipr),
n = rsi_n(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = rsi_R(cipr, as_percent = TRUE),
I = rsi_I(cipr, as_percent = TRUE),
S = rsi_S(cipr, as_percent = TRUE),
n = rsi_n(cipr), # also: n_rsi, works like n_distinct in dplyr
total = n()) # this is the length, NOT the amount of tested isolates
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
rsi_S(septic_patients$amcl) # S = 67.3\%
rsi_n(septic_patients$amcl) # n = 1570
rsi_S(septic_patients$gent) # S = 74.0\%
rsi_n(septic_patients$gent) # n = 1842
with(septic_patients,
rsi_S(amcl, gent)) # S = 92.1\%
with(septic_patients, # n = 1504
rsi_n(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = rsi_S(cipr, as_percent = TRUE),
cipro_n = rsi_n(cipr),
genta_p = rsi_S(gent, as_percent = TRUE),
genta_n = rsi_n(gent),
combination_p = rsi_S(cipr, gent, as_percent = TRUE),
combination_n = rsi_n(cipr, gent))
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = rsi_S(amox, metr), # amoxicillin with metronidazole
n = rsi_n(amox, metr))
}
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{rsi_df}
\keyword{susceptibility}

4
man/septic_patients.Rd
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@ -50,7 +50,7 @@ my_data \%>\%
first_isolates == TRUE) \%>\%
group_by(hospital_id) \%>\%
summarise(n = n_rsi(amox),
p = resistance(amox))
p = portion_IR(amox))
# 2. Get the amoxicillin/clavulanic acid resistance
@ -61,6 +61,6 @@ my_data \%>\%
first_isolates == TRUE) \%>\%
group_by(year = format(date, "\%Y")) \%>\%
summarise(n = n_rsi(amcl),
p = resistance(amcl, minimum = 20))
p = portion_IR(amcl, minimum = 20))
}
\keyword{datasets}