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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup

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dr. M.S. (Matthijs) Berends
2020-09-29 23:35:46 +02:00
parent 68e6e1e329
commit 4e0374af29
94 changed files with 1143 additions and 1165 deletions
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89
man/as.rsi.Rd
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@ -167,34 +167,6 @@ df <- data.frame(microorganism = "E. coli",
NIT = as.mic(32))
as.rsi(df)
\dontrun{
# the dplyr way
library(dplyr)
df \%>\% mutate_if(is.mic, as.rsi)
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
df \%>\% mutate(across(where(is.mic), as.rsi))
df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
df \%>\% mutate(across(AMP:TOB), as.rsi)
df \%>\%
mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
# to include information about urinary tract infections (UTI)
data.frame(mo = "E. coli",
NIT = c("<= 2", 32),
from_the_bladder = c(TRUE, FALSE)) \%>\%
as.rsi(uti = "from_the_bladder")
data.frame(mo = "E. coli",
NIT = c("<= 2", 32),
specimen = c("urine", "blood")) \%>\%
as.rsi() # automatically determines urine isolates
df \%>\%
mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE)
}
# for single values
as.rsi(x = as.mic(2),
mo = as.mo("S. pneumoniae"),
@ -206,6 +178,32 @@ as.rsi(x = as.disk(18),
ab = "ampicillin", # and `ab` with as.ab()
guideline = "EUCAST")
\donttest{
# the dplyr way
if (require("dplyr")) {
df \%>\% mutate_if(is.mic, as.rsi)
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
df \%>\% mutate(across(where(is.mic), as.rsi))
df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
df \%>\% mutate(across(AMP:TOB), as.rsi)
df \%>\%
mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
# to include information about urinary tract infections (UTI)
data.frame(mo = "E. coli",
NIT = c("<= 2", 32),
from_the_bladder = c(TRUE, FALSE)) \%>\%
as.rsi(uti = "from_the_bladder")
data.frame(mo = "E. coli",
NIT = c("<= 2", 32),
specimen = c("urine", "blood")) \%>\%
as.rsi() # automatically determines urine isolates
df \%>\%
mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE)
}
# For CLEANING existing R/SI values ------------------------------------
@ -216,25 +214,22 @@ is.rsi(rsi_data)
plot(rsi_data) # for percentages
barplot(rsi_data) # for frequencies
\dontrun{
library(dplyr)
example_isolates \%>\%
mutate_at(vars(PEN:RIF), as.rsi)
# same:
example_isolates \%>\%
as.rsi(PEN:RIF)
# fastest way to transform all columns with already valid AMR results to class `rsi`:
example_isolates \%>\%
mutate_if(is.rsi.eligible, as.rsi)
# note: from dplyr 1.0.0 on, this will be:
# example_isolates \%>\%
# mutate(across(is.rsi.eligible, as.rsi))
# default threshold of `is.rsi.eligible` is 5\%.
is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
# the dplyr way
if (require("dplyr")) {
example_isolates \%>\%
mutate_at(vars(PEN:RIF), as.rsi)
# same:
example_isolates \%>\%
as.rsi(PEN:RIF)
# fastest way to transform all columns with already valid AMR results to class `rsi`:
example_isolates \%>\%
mutate_if(is.rsi.eligible, as.rsi)
# note: from dplyr 1.0.0 on, this will be:
# example_isolates \%>\%
# mutate(across(is.rsi.eligible, as.rsi))
}
}
}
\seealso{