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new MOs, cleanup

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dr. M.S. (Matthijs) Berends
2018-09-01 21:19:46 +02:00
parent 5965d3c794
commit 75fe4d401f
20 changed files with 166 additions and 179 deletions
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6
man/antibiotics.Rd
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@ -3,8 +3,8 @@
\docType{data}
\name{antibiotics}
\alias{antibiotics}
\title{Dataset with 423 antibiotics}
\format{A data.frame with 423 observations and 18 variables:
\title{Data set with 423 antibiotics}
\format{A \code{\link{tibble}} with 423 observations and 18 variables:
\describe{
\item{\code{atc}}{ATC code, like \code{J01CR02}}
\item{\code{certe}}{Certe code, like \code{amcl}}
@ -32,7 +32,7 @@
antibiotics
}
\description{
A dataset containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDD's. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
}
\seealso{
\code{\link{microorganisms}}

2
man/as.atc.Rd
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@ -45,6 +45,6 @@ ab_official(Cipro) # returns "Ciprofloxacin"
ab_umcg(Cipro) # returns "CIPR", the code used in the UMCG
}
\seealso{
\code{\link{antibiotics}} for the dataframe that is being used to determine ATC's.
\code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
}
\keyword{atc}

6
man/microorganisms.Rd
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@ -3,8 +3,8 @@
\docType{data}
\name{microorganisms}
\alias{microorganisms}
\title{Dataset with ~2650 microorganisms}
\format{A data.frame with 2,646 observations and 12 variables:
\title{Data set with human pathogenic microorganisms}
\format{A \code{\link{tibble}} with 2,664 observations and 12 variables:
\describe{
\item{\code{mo}}{ID of microorganism}
\item{\code{bactsys}}{Bactsyscode of microorganism}
@ -23,7 +23,7 @@
microorganisms
}
\description{
A dataset containing 2,646 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
A data set containing 2,664 (potential) human pathogenic microorganisms. MO codes can be looked up using \code{\link{guess_mo}}.
}
\seealso{
\code{\link{guess_mo}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}

6
man/microorganisms.umcg.Rd
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@ -3,8 +3,8 @@
\docType{data}
\name{microorganisms.umcg}
\alias{microorganisms.umcg}
\title{Translation table for UMCG with ~1100 microorganisms}
\format{A data.frame with 1090 observations and 2 variables:
\title{Translation table for UMCG with ~1,100 microorganisms}
\format{A \code{\link{tibble}} with 1,090 observations and 2 variables:
\describe{
\item{\code{umcg}}{Code of microorganism according to UMCG MMB}
\item{\code{mo}}{Code of microorganism in \code{\link{microorganisms}}}
@ -13,7 +13,7 @@
microorganisms.umcg
}
\description{
A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{mo}'s with \code{\link{guess_mo}}.
A data set containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{MO}s with \code{\link{guess_mo}}.
}
\seealso{
\code{\link{guess_mo}} \code{\link{microorganisms}}

10
man/septic_patients.Rd
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@ -3,8 +3,8 @@
\docType{data}
\name{septic_patients}
\alias{septic_patients}
\title{Dataset with 2000 blood culture isolates of septic patients}
\format{A data.frame with 2000 observations and 49 variables:
\title{Data set with 2000 blood culture isolates of septic patients}
\format{A \code{\link{tibble}} with 2,000 observations and 49 variables:
\describe{
\item{\code{date}}{date of receipt at the laboratory}
\item{\code{hospital_id}}{ID of the hospital, from A to D}
@ -21,20 +21,20 @@
septic_patients
}
\description{
An anonymised dataset containing 2000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
}
\examples{
# ----------- #
# PREPARATION #
# ----------- #
# Save this example dataset to an object, so we can edit it:
# Save this example data set to an object, so we can edit it:
my_data <- septic_patients
# load the dplyr package to make data science A LOT easier
library(dplyr)
# Add first isolates to our dataset:
# Add first isolates to our data set:
my_data <- my_data \%>\%
mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))