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(v0.8.0.9027) adding susceptibility() and resistance()

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dr. M.S. (Matthijs) Berends
2019-11-10 12:16:56 +01:00
parent 228a4245cb
commit 59ededa8dc
85 changed files with 1526 additions and 932 deletions
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74
man/count.Rd
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@ -2,6 +2,8 @@
% Please edit documentation in R/count.R
\name{count}
\alias{count}
\alias{count_resistant}
\alias{count_susceptible}
\alias{count_R}
\alias{count_IR}
\alias{count_I}
@ -11,10 +13,11 @@
\alias{n_rsi}
\alias{count_df}
\title{Count isolates}
\source{
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
}
\usage{
count_resistant(..., only_all_tested = FALSE)
count_susceptible(..., only_all_tested = FALSE)
count_R(..., only_all_tested = FALSE)
count_IR(..., only_all_tested = FALSE)
@ -53,16 +56,16 @@ Integer
\description{
These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
\code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
\code{count_resistant} should be used to count resistant isolates, \code{count_susceptible} should be used to count susceptible isolates.\cr
}
\details{
These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
These functions are meant to count isolates. Use the \code{\link{resistance}}/\code{\link{susceptibility}} functions to calculate microbial resistance/susceptibility.
The function \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
The function \code{count_resistant} is equal to the function \code{count_R}. The function \code{count_susceptible} is equal to the function \code{count_SI}.
The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of S, I and R. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
The function \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R.
The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the number of S's, I's and R's. The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R.
}
\section{Interpretation of S, I and R}{
@ -76,12 +79,12 @@ In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST)
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates.
This AMR package honours this new insight. Use \code{\link{susceptibility}} (equal to \code{\link{proportion_SI}}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}} (equal to \code{\link{count_SI}}) to count susceptible isolates.
}
\section{Combination therapy}{
When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{portion_SI} works to calculate the \%SI:
When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI:
\preformatted{
--------------------------------------------------------------------
@ -104,13 +107,13 @@ When using more than one variable for \code{...} (= combination therapy)), use \
Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:
\preformatted{
count_S() + count_I() + count_R() == count_all()
portion_S() + portion_I() + portion_R() == 1
count_S() + count_I() + count_R() = count_all()
proportion_S() + proportion_I() + proportion_R() = 1
}
and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:
\preformatted{
count_S() + count_I() + count_R() >= count_all()
portion_S() + portion_I() + portion_R() >= 1
count_S() + count_I() + count_R() >= count_all()
proportion_S() + proportion_I() + proportion_R() >= 1
}
Using \code{only_all_tested} has no impact when only using one antibiotic as input.
@ -125,23 +128,27 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
# example_isolates is a data set available in the AMR package.
?example_isolates
# Count resistant isolates
count_R(example_isolates$AMX)
count_IR(example_isolates$AMX)
count_resistant(example_isolates$AMX) # counts "R"
count_susceptible(example_isolates$AMX) # counts "S" and "I"
count_all(example_isolates$AMX) # counts "S", "I" and "R"
# Or susceptible isolates
# be more specific
count_S(example_isolates$AMX)
count_SI(example_isolates$AMX)
count_I(example_isolates$AMX)
count_IR(example_isolates$AMX)
count_R(example_isolates$AMX)
# Count all available isolates
count_all(example_isolates$AMX)
n_rsi(example_isolates$AMX)
# Since n_rsi counts available isolates, you can
# calculate back to count e.g. non-susceptible isolates.
# This results in the same:
count_SI(example_isolates$AMX)
portion_SI(example_isolates$AMX) * n_rsi(example_isolates$AMX)
# n_rsi() is an alias of count_all().
# Since it counts all available isolates, you can
# calculate back to count e.g. susceptible isolates.
# These results are the same:
count_susceptible(example_isolates$AMX)
susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
library(dplyr)
example_isolates \%>\%
@ -155,19 +162,16 @@ example_isolates \%>\%
# Count co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy.
# Please mind that `portion_SI` calculates percentages right away instead.
count_SI(example_isolates$AMC) # 1433
count_all(example_isolates$AMC) # 1879
# Please mind that `susceptibility()` calculates percentages right away instead.
example_isolates \%>\% count_susceptible(AMC) # 1433
example_isolates \%>\% count_all(AMC) # 1879
count_SI(example_isolates$GEN) # 1399
count_all(example_isolates$GEN) # 1855
example_isolates \%>\% count_susceptible(GEN) # 1399
example_isolates \%>\% count_all(GEN) # 1855
with(example_isolates,
count_SI(AMC, GEN)) # 1764
with(example_isolates,
n_rsi(AMC, GEN)) # 1936
# Get portions S/I/R immediately of all rsi columns
example_isolates \%>\% count_susceptible(AMC, GEN) # 1764
example_isolates \%>\% count_all(AMC, GEN) # 1936
# Get number of S+I vs. R immediately of selected columns
example_isolates \%>\%
select(AMX, CIP) \%>\%
count_df(translate = FALSE)
@ -180,5 +184,5 @@ example_isolates \%>\%
}
\seealso{
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
\code{\link{proportion}_*} to calculate microbial resistance and susceptibility.
}