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(v1.1.0.9005) lose dependencies

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dr. M.S. (Matthijs) Berends
2020-05-16 20:08:21 +02:00
parent 7f3da74b17
commit df2456b91f
30 changed files with 342 additions and 736 deletions
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131
man/proportion.Rd
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@ -74,7 +74,7 @@ rsi_df(
A \code{\link{double}} or, when \code{as_percent = TRUE}, a \code{\link{character}}.
}
\description{
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}.
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}.
\code{\link[=resistance]{resistance()}} should be used to calculate resistance, \code{\link[=susceptibility]{susceptibility()}} should be used to calculate susceptibility.\cr
}
@ -160,70 +160,71 @@ proportion_I(example_isolates$AMX)
proportion_IR(example_isolates$AMX)
proportion_R(example_isolates$AMX)
\dontrun{
library(dplyr)
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(r = resistance(CIP),
n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(R = resistance(CIP, as_percent = TRUE),
SI = susceptibility(CIP, as_percent = TRUE),
n1 = count_all(CIP), # the actual total; sum of all three
n2 = n_rsi(CIP), # same - analogous to n_distinct
total = n()) # NOT the number 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:
example_isolates \%>\% susceptibility(AMC) # \%SI = 76.3\%
example_isolates \%>\% count_all(AMC) # n = 1879
example_isolates \%>\% susceptibility(GEN) # \%SI = 75.4\%
example_isolates \%>\% count_all(GEN) # n = 1855
example_isolates \%>\% susceptibility(AMC, GEN) # \%SI = 94.1\%
example_isolates \%>\% count_all(AMC, GEN) # n = 1939
# See Details on how `only_all_tested` works. Example:
example_isolates \%>\%
summarise(numerator = count_susceptible(AMC, GEN),
denominator = count_all(AMC, GEN),
proportion = susceptibility(AMC, GEN))
example_isolates \%>\%
summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
denominator = count_all(AMC, GEN, only_all_tested = TRUE),
proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
cipro_n = count_all(CIP),
genta_p = susceptibility(GEN, as_percent = TRUE),
genta_n = count_all(GEN),
combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
combination_n = count_all(CIP, GEN))
# Get proportions S/I/R immediately of all rsi columns
example_isolates \%>\%
select(AMX, CIP) \%>\%
proportion_df(translate = FALSE)
# It also supports grouping variables
example_isolates \%>\%
select(hospital_id, AMX, CIP) \%>\%
group_by(hospital_id) \%>\%
proportion_df(translate = FALSE)
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole
n = count_all(AMX, MTR))
if (!require("dplyr")) {
library(dplyr)
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(r = resistance(CIP),
n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(R = resistance(CIP, as_percent = TRUE),
SI = susceptibility(CIP, as_percent = TRUE),
n1 = count_all(CIP), # the actual total; sum of all three
n2 = n_rsi(CIP), # same - analogous to n_distinct
total = n()) # NOT the number 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:
example_isolates \%>\% susceptibility(AMC) # \%SI = 76.3\%
example_isolates \%>\% count_all(AMC) # n = 1879
example_isolates \%>\% susceptibility(GEN) # \%SI = 75.4\%
example_isolates \%>\% count_all(GEN) # n = 1855
example_isolates \%>\% susceptibility(AMC, GEN) # \%SI = 94.1\%
example_isolates \%>\% count_all(AMC, GEN) # n = 1939
# See Details on how `only_all_tested` works. Example:
example_isolates \%>\%
summarise(numerator = count_susceptible(AMC, GEN),
denominator = count_all(AMC, GEN),
proportion = susceptibility(AMC, GEN))
example_isolates \%>\%
summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
denominator = count_all(AMC, GEN, only_all_tested = TRUE),
proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
cipro_n = count_all(CIP),
genta_p = susceptibility(GEN, as_percent = TRUE),
genta_n = count_all(GEN),
combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
combination_n = count_all(CIP, GEN))
# Get proportions S/I/R immediately of all rsi columns
example_isolates \%>\%
select(AMX, CIP) \%>\%
proportion_df(translate = FALSE)
# It also supports grouping variables
example_isolates \%>\%
select(hospital_id, AMX, CIP) \%>\%
group_by(hospital_id) \%>\%
proportion_df(translate = FALSE)
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = susceptibility(AMX, MTR), # amoxicillin with metronidazole
n = count_all(AMX, MTR))
}
}
\seealso{