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(v1.1.0.9005) lose dependencies
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68
man/count.Rd
68
man/count.Rd
@ -59,7 +59,7 @@ count_df(
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An \code{\link{integer}}
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}
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\description{
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These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{\link[=summarise]{summarise()}} and support grouped variables, see \emph{Examples}.
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These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{summarise()} from the \code{dplyr} package and also support grouped variables, please see \emph{Examples}.
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\code{\link[=count_resistant]{count_resistant()}} should be used to count resistant isolates, \code{\link[=count_susceptible]{count_susceptible()}} should be used to count susceptible isolates.
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}
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@ -68,7 +68,7 @@ These functions are meant to count isolates. Use the \code{\link[=resistance]{re
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The function \code{\link[=count_resistant]{count_resistant()}} is equal to the function \code{\link[=count_R]{count_R()}}. The function \code{\link[=count_susceptible]{count_susceptible()}} is equal to the function \code{\link[=count_SI]{count_SI()}}.
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The function \code{\link[=n_rsi]{n_rsi()}} 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{\link[=n_distinct]{n_distinct()}}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
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The function \code{\link[=n_rsi]{n_rsi()}} 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(...)}.
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The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=rsi_df]{rsi_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
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}
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@ -157,38 +157,40 @@ n_rsi(example_isolates$AMX)
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count_susceptible(example_isolates$AMX)
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susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
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library(dplyr)
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = count_R(CIP),
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I = count_I(CIP),
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S = count_S(CIP),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_rsi(CIP), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Count co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy.
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# Please mind that `susceptibility()` calculates percentages right away instead.
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example_isolates \%>\% count_susceptible(AMC) # 1433
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example_isolates \%>\% count_all(AMC) # 1879
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example_isolates \%>\% count_susceptible(GEN) # 1399
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example_isolates \%>\% count_all(GEN) # 1855
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example_isolates \%>\% count_susceptible(AMC, GEN) # 1764
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example_isolates \%>\% count_all(AMC, GEN) # 1936
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# Get number of S+I vs. R immediately of selected columns
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example_isolates \%>\%
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select(AMX, CIP) \%>\%
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count_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates \%>\%
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select(hospital_id, AMX, CIP) \%>\%
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group_by(hospital_id) \%>\%
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count_df(translate = FALSE)
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if (!require("dplyr")) {
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = count_R(CIP),
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I = count_I(CIP),
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S = count_S(CIP),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_rsi(CIP), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Count co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy.
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# Please mind that `susceptibility()` calculates percentages right away instead.
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example_isolates \%>\% count_susceptible(AMC) # 1433
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example_isolates \%>\% count_all(AMC) # 1879
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example_isolates \%>\% count_susceptible(GEN) # 1399
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example_isolates \%>\% count_all(GEN) # 1855
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example_isolates \%>\% count_susceptible(AMC, GEN) # 1764
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example_isolates \%>\% count_all(AMC, GEN) # 1936
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# Get number of S+I vs. R immediately of selected columns
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example_isolates \%>\%
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select(AMX, CIP) \%>\%
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count_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates \%>\%
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select(hospital_id, AMX, CIP) \%>\%
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group_by(hospital_id) \%>\%
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count_df(translate = FALSE)
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}
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}
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\seealso{
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\code{\link[=proportion]{proportion_*}} to calculate microbial resistance and susceptibility.
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@ -102,7 +102,7 @@ ggplot_pca(
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Produces a \code{ggplot2} variant of a so-called \href{https://en.wikipedia.org/wiki/Biplot}{biplot} for PCA (principal component analysis), but is more flexible and more appealing than the base \R \code{\link[=biplot]{biplot()}} function.
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}
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\details{
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The colours for labels and points can be changed by adding another scale layer for colour, like \code{\link[=scale_colour_viridis_d]{scale_colour_viridis_d()}} or \code{\link[=scale_colour_brewer]{scale_colour_brewer()}}.
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The colours for labels and points can be changed by adding another scale layer for colour, like \code{scale_colour_viridis_d()} or \code{scale_colour_brewer()}.
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}
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\section{Maturing lifecycle}{
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@ -74,7 +74,7 @@ rsi_df(
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A \code{\link{double}} or, when \code{as_percent = TRUE}, a \code{\link{character}}.
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}
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\description{
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These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{\link[=summarise]{summarise()}} from the \code{dplyr} package and also supports grouped variables, please see \emph{Examples}.
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These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{summarise()} from the \code{dplyr} package and also support grouped variables, please see \emph{Examples}.
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\code{\link[=resistance]{resistance()}} should be used to calculate resistance, \code{\link[=susceptibility]{susceptibility()}} should be used to calculate susceptibility.\cr
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}
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@ -160,70 +160,71 @@ proportion_I(example_isolates$AMX)
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proportion_IR(example_isolates$AMX)
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proportion_R(example_isolates$AMX)
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\dontrun{
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library(dplyr)
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(r = resistance(CIP),
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n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = resistance(CIP, as_percent = TRUE),
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SI = susceptibility(CIP, as_percent = TRUE),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_rsi(CIP), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy:
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example_isolates \%>\% susceptibility(AMC) # \%SI = 76.3\%
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example_isolates \%>\% count_all(AMC) # n = 1879
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example_isolates \%>\% susceptibility(GEN) # \%SI = 75.4\%
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example_isolates \%>\% count_all(GEN) # n = 1855
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example_isolates \%>\% susceptibility(AMC, GEN) # \%SI = 94.1\%
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example_isolates \%>\% count_all(AMC, GEN) # n = 1939
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# See Details on how `only_all_tested` works. Example:
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example_isolates \%>\%
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summarise(numerator = count_susceptible(AMC, GEN),
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denominator = count_all(AMC, GEN),
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proportion = susceptibility(AMC, GEN))
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example_isolates \%>\%
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summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
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denominator = count_all(AMC, GEN, only_all_tested = TRUE),
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proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
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cipro_n = count_all(CIP),
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genta_p = susceptibility(GEN, as_percent = TRUE),
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genta_n = count_all(GEN),
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combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
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combination_n = count_all(CIP, GEN))
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# Get proportions S/I/R immediately of all rsi columns
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example_isolates \%>\%
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select(AMX, CIP) \%>\%
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proportion_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates \%>\%
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select(hospital_id, AMX, CIP) \%>\%
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group_by(hospital_id) \%>\%
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proportion_df(translate = FALSE)
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# calculate current empiric combination therapy of Helicobacter gastritis:
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my_table \%>\%
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filter(first_isolate == TRUE,
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genus == "Helicobacter") \%>\%
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summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole
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n = count_all(AMX, MTR))
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if (!require("dplyr")) {
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library(dplyr)
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(r = resistance(CIP),
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n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = resistance(CIP, as_percent = TRUE),
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SI = susceptibility(CIP, as_percent = TRUE),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_rsi(CIP), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy:
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example_isolates \%>\% susceptibility(AMC) # \%SI = 76.3\%
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example_isolates \%>\% count_all(AMC) # n = 1879
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example_isolates \%>\% susceptibility(GEN) # \%SI = 75.4\%
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example_isolates \%>\% count_all(GEN) # n = 1855
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example_isolates \%>\% susceptibility(AMC, GEN) # \%SI = 94.1\%
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example_isolates \%>\% count_all(AMC, GEN) # n = 1939
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# See Details on how `only_all_tested` works. Example:
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example_isolates \%>\%
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summarise(numerator = count_susceptible(AMC, GEN),
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denominator = count_all(AMC, GEN),
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proportion = susceptibility(AMC, GEN))
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example_isolates \%>\%
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summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
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denominator = count_all(AMC, GEN, only_all_tested = TRUE),
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proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
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cipro_n = count_all(CIP),
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genta_p = susceptibility(GEN, as_percent = TRUE),
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genta_n = count_all(GEN),
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combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
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combination_n = count_all(CIP, GEN))
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# Get proportions S/I/R immediately of all rsi columns
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example_isolates \%>\%
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select(AMX, CIP) \%>\%
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proportion_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates \%>\%
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select(hospital_id, AMX, CIP) \%>\%
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group_by(hospital_id) \%>\%
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proportion_df(translate = FALSE)
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# calculate current empiric combination therapy of Helicobacter gastritis:
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my_table \%>\%
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filter(first_isolate == TRUE,
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genus == "Helicobacter") \%>\%
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summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole
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n = count_all(AMX, MTR))
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}
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}
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\seealso{
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148
man/read.4D.Rd
148
man/read.4D.Rd
@ -1,148 +0,0 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/read.4d.R
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\name{read.4D}
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\alias{read.4D}
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\title{Read data from 4D database}
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\usage{
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read.4D(
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file,
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info = interactive(),
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header = TRUE,
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row.names = NULL,
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sep = "\\t",
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quote = "\\"'",
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dec = ",",
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na.strings = c("NA", "", "."),
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skip = 2,
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check.names = TRUE,
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strip.white = TRUE,
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fill = TRUE,
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blank.lines.skip = TRUE,
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stringsAsFactors = FALSE,
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fileEncoding = "UTF-8",
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encoding = "UTF-8"
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)
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}
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\arguments{
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\item{file}{the name of the file which the data are to be read from.
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Each row of the table appears as one line of the file. If it does
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not contain an \emph{absolute} path, the file name is
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\emph{relative} to the current working directory,
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\code{\link{getwd}()}. Tilde-expansion is performed where supported.
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This can be a compressed file (see \code{\link{file}}).
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Alternatively, \code{file} can be a readable text-mode
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\link{connection} (which will be opened for reading if
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necessary, and if so \code{\link{close}}d (and hence destroyed) at
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the end of the function call). (If \code{\link{stdin}()} is used,
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the prompts for lines may be somewhat confusing. Terminate input
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with a blank line or an EOF signal, \code{Ctrl-D} on Unix and
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\code{Ctrl-Z} on Windows. Any pushback on \code{stdin()} will be
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cleared before return.)
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\code{file} can also be a complete URL. (For the supported URL
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schemes, see the \sQuote{URLs} section of the help for
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\code{\link{url}}.)
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}
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\item{info}{a logical to indicate whether info about the import should be printed, defaults to \code{TRUE} in interactive sessions}
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\item{header}{a logical value indicating whether the file contains the
|
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names of the variables as its first line. If missing, the value is
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determined from the file format: \code{header} is set to \code{TRUE}
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if and only if the first row contains one fewer field than the
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number of columns.}
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\item{row.names}{a vector of row names. This can be a vector giving
|
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the actual row names, or a single number giving the column of the
|
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table which contains the row names, or character string giving the
|
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name of the table column containing the row names.
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If there is a header and the first row contains one fewer field than
|
||||
the number of columns, the first column in the input is used for the
|
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row names. Otherwise if \code{row.names} is missing, the rows are
|
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numbered.
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Using \code{row.names = NULL} forces row numbering. Missing or
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\code{NULL} \code{row.names} generate row names that are considered
|
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to be \sQuote{automatic} (and not preserved by \code{\link{as.matrix}}).
|
||||
}
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||||
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\item{sep}{the field separator character. Values on each line of the
|
||||
file are separated by this character. If \code{sep = ""} (the
|
||||
default for \code{read.table}) the separator is \sQuote{white space},
|
||||
that is one or more spaces, tabs, newlines or carriage returns.}
|
||||
|
||||
\item{quote}{the set of quoting characters. To disable quoting
|
||||
altogether, use \code{quote = ""}. See \code{\link{scan}} for the
|
||||
behaviour on quotes embedded in quotes. Quoting is only considered
|
||||
for columns read as character, which is all of them unless
|
||||
\code{colClasses} is specified.}
|
||||
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||||
\item{dec}{the character used in the file for decimal points.}
|
||||
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||||
\item{na.strings}{a character vector of strings which are to be
|
||||
interpreted as \code{\link{NA}} values. Blank fields are also
|
||||
considered to be missing values in logical, integer, numeric and
|
||||
complex fields. Note that the test happens \emph{after}
|
||||
white space is stripped from the input, so \code{na.strings}
|
||||
values may need their own white space stripped in advance.}
|
||||
|
||||
\item{skip}{integer: the number of lines of the data file to skip before
|
||||
beginning to read data.}
|
||||
|
||||
\item{check.names}{logical. If \code{TRUE} then the names of the
|
||||
variables in the data frame are checked to ensure that they are
|
||||
syntactically valid variable names. If necessary they are adjusted
|
||||
(by \code{\link{make.names}}) so that they are, and also to ensure
|
||||
that there are no duplicates.}
|
||||
|
||||
\item{strip.white}{logical. Used only when \code{sep} has
|
||||
been specified, and allows the stripping of leading and trailing
|
||||
white space from unquoted \code{character} fields (\code{numeric} fields
|
||||
are always stripped). See \code{\link{scan}} for further details
|
||||
(including the exact meaning of \sQuote{white space}),
|
||||
remembering that the columns may include the row names.}
|
||||
|
||||
\item{fill}{logical. If \code{TRUE} then in case the rows have unequal
|
||||
length, blank fields are implicitly added. See \sQuote{Details}.}
|
||||
|
||||
\item{blank.lines.skip}{logical: if \code{TRUE} blank lines in the
|
||||
input are ignored.}
|
||||
|
||||
\item{stringsAsFactors}{logical: should character vectors be converted
|
||||
to factors? Note that this is overridden by \code{as.is} and
|
||||
\code{colClasses}, both of which allow finer control.}
|
||||
|
||||
\item{fileEncoding}{character string: if non-empty declares the
|
||||
encoding used on a file (not a connection) so the character data can
|
||||
be re-encoded. See the \sQuote{Encoding} section of the help for
|
||||
\code{\link{file}}, the \sQuote{R Data Import/Export Manual} and
|
||||
\sQuote{Note}.
|
||||
}
|
||||
|
||||
\item{encoding}{encoding to be assumed for input strings. It is
|
||||
used to mark character strings as known to be in
|
||||
Latin-1 or UTF-8 (see \code{\link{Encoding}}): it is not used to
|
||||
re-encode the input, but allows \R to handle encoded strings in
|
||||
their native encoding (if one of those two). See \sQuote{Value}
|
||||
and \sQuote{Note}.
|
||||
}
|
||||
}
|
||||
\description{
|
||||
This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with \code{\link[=as.mo]{as.mo()}} and transform all antimicrobial columns with \code{\link[=as.rsi]{as.rsi()}}.
|
||||
}
|
||||
\details{
|
||||
Column names will be transformed, but the original column names are set as a "label" attribute and can be seen in e.g. RStudio Viewer.
|
||||
}
|
||||
\section{Dormant lifecycle}{
|
||||
|
||||
\if{html}{\figure{lifecycle_dormant.svg}{options: style=margin-bottom:5px} \cr}
|
||||
The \link[AMR:lifecycle]{lifecycle} of this function is \strong{dormant}. A dormant function is currently not under active development and has not reached a stable phase. We might return to it in the future. As with experimental functions, you are best off waiting until a function is more mature before you use it in production code.
|
||||
}
|
||||
|
||||
\section{Read more on our website!}{
|
||||
|
||||
On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user