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dr. M.S. (Matthijs) Berends
2023-01-21 11:47:02 +01:00
parent ee38689172
commit 79c8415d3e
13 changed files with 22 additions and 22 deletions
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2
NAMESPACE
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@@ -387,9 +387,9 @@ export(set_AMR_locale)
export(set_ab_names)
export(set_mo_source)
export(sir_confidence_interval)
export(sir_df)
export(sir_interpretation_history)
export(sir_predict)
export(sir_sf)
export(skewness)
export(streptogramins)
export(susceptibility)

2
R/bug_drug_combinations.R
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@@ -37,7 +37,7 @@
#' @param FUN the function to call on the `mo` column to transform the microorganism codes, defaults to [mo_shortname()]
#' @param translate_ab a [character] of length 1 containing column names of the [antibiotics] data set
#' @param ... arguments passed on to `FUN`
#' @inheritParams sir_sf
#' @inheritParams sir_df
#' @inheritParams base::formatC
#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S.
#' @export

2
R/count.R
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@@ -41,7 +41,7 @@
#'
#' The function [n_sir()] is an alias of [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 `n_distinct()`. Their function is equal to `count_susceptible(...) + count_resistant(...)`.
#'
#' The function [count_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and counts the number of S's, I's and R's. It also supports grouped variables. The function [sir_sf()] works exactly like [count_df()], but adds the percentage of S, I and R.
#' The function [count_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and counts the number of S's, I's and R's. It also supports grouped variables. The function [sir_df()] works exactly like [count_df()], but adds the percentage of S, I and R.
#' @inheritSection proportion Combination Therapy
#' @seealso [`proportion_*`][proportion] to calculate microbial resistance and susceptibility.
#' @return An [integer]

6
R/ggplot_sir.R
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@@ -53,7 +53,7 @@
#' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` argument. See [count_df()].
#'
#' ### The Functions
#' [geom_sir()] will take any variable from the data that has an [`sir`] class (created with [as.sir()]) using [sir_sf()] and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
#' [geom_sir()] will take any variable from the data that has an [`sir`] class (created with [as.sir()]) using [sir_df()] and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
#'
#' [facet_sir()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()].
#'
@@ -340,7 +340,7 @@ geom_sir <- function(position = NULL,
ggplot2::geom_col(
data = function(x) {
sir_sf(
sir_df(
data = x,
translate_ab = translate_ab,
language = language,
@@ -521,7 +521,7 @@ labels_sir_count <- function(position = NULL,
colour = datalabels.colour,
lineheight = 0.75,
data = function(x) {
transformed <- sir_sf(
transformed <- sir_df(
data = x,
translate_ab = translate_ab,
combine_SI = combine_SI,

6
R/proportion.R
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@@ -53,7 +53,7 @@
#'
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
#'
#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_sf()] works exactly like [proportion_df()], but adds the number of isolates.
#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_df()] works exactly like [proportion_df()], but adds the number of isolates.
#' @section Combination Therapy:
#' When using more than one variable for `...` (= combination therapy), use `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, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
#'
@@ -206,11 +206,11 @@
#' proportion_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' # (use sir_sf to also include the count)
#' # (use sir_df to also include the count)
#' example_isolates %>%
#' select(ward, AMX, CIP) %>%
#' group_by(ward) %>%
#' sir_sf(translate = FALSE)
#' sir_df(translate = FALSE)
#' }
#' }
resistance <- function(...,

2
R/sir_calc.R
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@@ -372,5 +372,5 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
rownames(out) <- NULL
out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
structure(out, class = c("sir_sf", class(out)))
structure(out, class = c("sir_df", "rsi_df", class(out)))
}

2
R/sir_df.R
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@@ -29,7 +29,7 @@
#' @rdname proportion
#' @export
sir_sf <- function(data,
sir_df <- function(data,
translate_ab = "name",
language = get_AMR_locale(),
minimum = 30,

BIN
R/sysdata.rda
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4
R/zzz.R
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@@ -98,14 +98,14 @@ if (utf8_supported && !is_latex) {
s3_register("pillar::pillar_shaft", "av")
s3_register("pillar::pillar_shaft", "mo")
s3_register("pillar::pillar_shaft", "sir")
s3_register("pillar::pillar_shaft", "rsi") # TODO deprecate in a later version
s3_register("pillar::pillar_shaft", "rsi") # remove in a later version
s3_register("pillar::pillar_shaft", "mic")
s3_register("pillar::pillar_shaft", "disk")
s3_register("pillar::type_sum", "ab")
s3_register("pillar::type_sum", "av")
s3_register("pillar::type_sum", "mo")
s3_register("pillar::type_sum", "sir")
s3_register("pillar::type_sum", "rsi")
s3_register("pillar::type_sum", "rsi") # remove in a later version
s3_register("pillar::type_sum", "mic")
s3_register("pillar::type_sum", "disk")
# Support for frequency tables from the cleaner package

4
inst/tinytest/test-count.R
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@@ -103,10 +103,10 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
)
)
# grouping in sir_calc_df() (= backbone of sir_sf())
# grouping in sir_calc_df() (= backbone of sir_df())
expect_true("ward" %in% (example_isolates %>%
group_by(ward) %>%
select(ward, AMX, CIP, gender) %>%
sir_sf() %>%
sir_df() %>%
colnames()))
}

2
man/count.Rd
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@@ -67,7 +67,7 @@ The function \code{\link[=count_resistant]{count_resistant()}} is equal to the f
The function \code{\link[=n_sir]{n_sir()}} is an alias of \code{\link[=count_all]{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{n_distinct()}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=sir_sf]{sir_sf()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=sir_df]{sir_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
}
\section{Interpretation of SIR}{

2
man/ggplot_sir.Rd
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@@ -120,7 +120,7 @@ Use these functions to create bar plots for AMR data analysis. All functions rel
At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} argument. See \code{\link[=count_df]{count_df()}}.
\subsection{The Functions}{
\code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_sf]{sir_sf()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
\code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_df]{sir_df()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
\code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.

10
man/proportion.Rd
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@@ -12,7 +12,7 @@
\alias{proportion_SI}
\alias{proportion_S}
\alias{proportion_df}
\alias{sir_sf}
\alias{sir_df}
\title{Calculate Microbial Resistance}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
@@ -52,7 +52,7 @@ proportion_df(
confidence_level = 0.95
)
sir_sf(
sir_df(
data,
translate_ab = "name",
language = get_AMR_locale(),
@@ -102,7 +102,7 @@ Use \code{\link[=sir_confidence_interval]{sir_confidence_interval()}} to calcula
These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[=count]{count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} argument).}
The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and calculates the proportions S, I, and R. It also supports grouped variables. The function \code{\link[=sir_sf]{sir_sf()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and calculates the proportions S, I, and R. It also supports grouped variables. The function \code{\link[=sir_df]{sir_df()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
}
\section{Combination Therapy}{
@@ -270,11 +270,11 @@ if (require("dplyr")) {
proportion_df(translate = FALSE)
# It also supports grouping variables
# (use sir_sf to also include the count)
# (use sir_df to also include the count)
example_isolates \%>\%
select(ward, AMX, CIP) \%>\%
group_by(ward) \%>\%
sir_sf(translate = FALSE)
sir_df(translate = FALSE)
}
}
}