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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup
This commit is contained in:
@ -1,39 +0,0 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/deprecated.R
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\name{AMR-deprecated}
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\alias{AMR-deprecated}
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\alias{portion_R}
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\alias{portion_IR}
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\alias{portion_I}
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\alias{portion_SI}
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\alias{portion_S}
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\alias{portion_df}
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\title{Deprecated functions}
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\usage{
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portion_R(...)
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portion_IR(...)
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portion_I(...)
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portion_SI(...)
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portion_S(...)
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portion_df(...)
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}
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\description{
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These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
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}
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\section{Retired lifecycle}{
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\if{html}{\figure{lifecycle_retired.svg}{options: style=margin-bottom:5px} \cr}
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The \link[=lifecycle]{lifecycle} of this function is \strong{retired}. A retired function is no longer under active development, and (if appropiate) a better alternative is available. No new arguments will be added, and only the most critical bugs will be fixed. In a future version, this function will be removed.
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}
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\section{Read more on our website!}{
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On our website \url{https://msberends.github.io/AMR} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.github.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}. As we would like to better understand the backgrounds and needs of our users, please \href{https://msberends.github.io/AMR/survey.html}{participate in our survey}!
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}
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\keyword{internal}
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@ -68,7 +68,7 @@ age_groups(ages, "children")
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# same:
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age_groups(ages, c(1, 2, 4, 6, 13, 17))
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\dontrun{
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\donttest{
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# resistance of ciprofloxacine per age group
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library(dplyr)
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example_isolates \%>\%
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@ -68,8 +68,7 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
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}
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\examples{
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\dontrun{
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library(dplyr)
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if (require("dplyr")) {
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# this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
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example_isolates \%>\%
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@ -92,9 +91,9 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
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format()
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data.frame(irrelevant = "value",
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data.frame(some_column = "some_value",
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J01CA01 = "S") \%>\% # ATC code of ampicillin
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select(penicillins()) # the 'J01CA01' column will be selected
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select(penicillins()) # only the 'J01CA01' column will be selected
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}
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}
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@ -38,7 +38,7 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
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}
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\examples{
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\dontrun{
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\donttest{
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# transform existing disk zones to the `disk` class
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library(dplyr)
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df <- data.frame(microorganism = "E. coli",
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@ -46,8 +46,9 @@ df <- data.frame(microorganism = "E. coli",
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CIP = 14,
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GEN = 18,
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TOB = 16)
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df <- df \%>\% mutate_at(vars(AMP:TOB), as.disk)
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df
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df[, 2:5] <- lapply(df[, 2:5], as.disk)
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# same with dplyr:
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# df \%>\% mutate(across(AMP:TOB, as.disk))
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# interpret disk values, see ?as.rsi
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as.rsi(x = as.disk(18),
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41
man/as.mo.Rd
41
man/as.mo.Rd
@ -117,13 +117,7 @@ There are three helper functions that can be run after using the \code{\link[=as
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\subsection{Microbial prevalence of pathogens in humans}{
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The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \link{microorganisms} and \link{microorganisms.old} data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.
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Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Klebsiella}, \emph{Pseudomonas} and \emph{Legionella}.
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Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton} or \emph{Ureaplasma}. This group consequently contains all less common and rare human pathogens.
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Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.
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The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \link{microorganisms} and \link{microorganisms.old} data sets. The grouping into human pathogenic prevalence is explained in the section \emph{Matching score for microorganisms} below.
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}
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}
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\section{Source}{
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@ -153,16 +147,16 @@ With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\li
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where:
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\itemize{
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\item \eqn{x} is the user input;
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\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
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\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
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\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
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\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
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\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
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\item \eqn{n} is a taxonomic name (genus, species, and subspecies);
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\item \eqn{l_n}{l_n} is the length of \eqn{n};
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\item lev is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function}, which counts any insertion, deletion and substitution as 1 that is needed to change \eqn{x} into \eqn{n};
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\item \eqn{p_n}{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
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\item \eqn{k_n}{p_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
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}
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This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
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The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella},\\\emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium},\emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
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All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
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All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.079}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
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}
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\section{Catalogue of Life}{
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@ -219,25 +213,6 @@ as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA
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# All mo_* functions use as.mo() internally too (see ?mo_property):
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mo_genus("E. coli") # returns "Escherichia"
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mo_gramstain("E. coli") # returns "Gram negative"
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}
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\dontrun{
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df$mo <- as.mo(df$microorganism_name)
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# the select function of the Tidyverse is also supported:
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library(dplyr)
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df$mo <- df \%>\%
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select(microorganism_name) \%>\%
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as.mo()
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# and can even contain 2 columns, which is convenient
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# for genus/species combinations:
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df$mo <- df \%>\%
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select(genus, species) \%>\%
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as.mo()
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# although this works easier and does the same:
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df <- df \%>\%
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mutate(mo = as.mo(paste(genus, species)))
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}
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}
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\seealso{
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@ -167,34 +167,6 @@ df <- data.frame(microorganism = "E. coli",
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NIT = as.mic(32))
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as.rsi(df)
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\dontrun{
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# the dplyr way
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library(dplyr)
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df \%>\% mutate_if(is.mic, as.rsi)
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df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
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df \%>\% mutate(across(where(is.mic), as.rsi))
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df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
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df \%>\% mutate(across(AMP:TOB), as.rsi)
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df \%>\%
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mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
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# to include information about urinary tract infections (UTI)
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data.frame(mo = "E. coli",
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NIT = c("<= 2", 32),
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from_the_bladder = c(TRUE, FALSE)) \%>\%
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as.rsi(uti = "from_the_bladder")
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data.frame(mo = "E. coli",
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NIT = c("<= 2", 32),
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specimen = c("urine", "blood")) \%>\%
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as.rsi() # automatically determines urine isolates
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df \%>\%
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mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE)
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}
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# for single values
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as.rsi(x = as.mic(2),
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mo = as.mo("S. pneumoniae"),
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@ -206,6 +178,32 @@ as.rsi(x = as.disk(18),
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ab = "ampicillin", # and `ab` with as.ab()
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guideline = "EUCAST")
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\donttest{
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# the dplyr way
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if (require("dplyr")) {
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df \%>\% mutate_if(is.mic, as.rsi)
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df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
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df \%>\% mutate(across(where(is.mic), as.rsi))
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df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
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df \%>\% mutate(across(AMP:TOB), as.rsi)
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df \%>\%
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mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
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# to include information about urinary tract infections (UTI)
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data.frame(mo = "E. coli",
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NIT = c("<= 2", 32),
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from_the_bladder = c(TRUE, FALSE)) \%>\%
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as.rsi(uti = "from_the_bladder")
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data.frame(mo = "E. coli",
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NIT = c("<= 2", 32),
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specimen = c("urine", "blood")) \%>\%
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as.rsi() # automatically determines urine isolates
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df \%>\%
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mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE)
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}
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# For CLEANING existing R/SI values ------------------------------------
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@ -216,25 +214,22 @@ is.rsi(rsi_data)
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plot(rsi_data) # for percentages
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barplot(rsi_data) # for frequencies
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\dontrun{
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library(dplyr)
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example_isolates \%>\%
|
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mutate_at(vars(PEN:RIF), as.rsi)
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# same:
|
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example_isolates \%>\%
|
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as.rsi(PEN:RIF)
|
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|
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# fastest way to transform all columns with already valid AMR results to class `rsi`:
|
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example_isolates \%>\%
|
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mutate_if(is.rsi.eligible, as.rsi)
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|
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# note: from dplyr 1.0.0 on, this will be:
|
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# example_isolates \%>\%
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# mutate(across(is.rsi.eligible, as.rsi))
|
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|
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# default threshold of `is.rsi.eligible` is 5\%.
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is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
|
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is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
|
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# the dplyr way
|
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if (require("dplyr")) {
|
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example_isolates \%>\%
|
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mutate_at(vars(PEN:RIF), as.rsi)
|
||||
# same:
|
||||
example_isolates \%>\%
|
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as.rsi(PEN:RIF)
|
||||
|
||||
# fastest way to transform all columns with already valid AMR results to class `rsi`:
|
||||
example_isolates \%>\%
|
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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{
|
||||
|
||||
@ -80,16 +80,13 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
|
||||
}
|
||||
|
||||
\examples{
|
||||
\dontrun{
|
||||
\donttest{
|
||||
# oral DDD (Defined Daily Dose) of amoxicillin
|
||||
atc_online_property("J01CA04", "DDD", "O")
|
||||
|
||||
# parenteral DDD (Defined Daily Dose) of amoxicillin
|
||||
atc_online_property("J01CA04", "DDD", "P")
|
||||
|
||||
atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
|
||||
# [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
|
||||
# [2] "ANTIBACTERIALS FOR SYSTEMIC USE"
|
||||
# [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
|
||||
# [4] "Penicillins with extended spectrum"
|
||||
}
|
||||
}
|
||||
|
||||
@ -36,17 +36,10 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
|
||||
\examples{
|
||||
availability(example_isolates)
|
||||
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
example_isolates \%>\% availability()
|
||||
|
||||
example_isolates \%>\%
|
||||
select_if(is.rsi) \%>\%
|
||||
availability()
|
||||
|
||||
example_isolates \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
select_if(is.rsi) \%>\%
|
||||
availability()
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
select_if(is.rsi) \%>\%
|
||||
availability()
|
||||
}
|
||||
}
|
||||
|
||||
@ -45,7 +45,7 @@ bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
|
||||
|
||||
\item{add_ab_group}{logical to indicate where the group of the antimicrobials must be included as a first column}
|
||||
|
||||
\item{remove_intrinsic_resistant}{logical to indicate that rows with 100\% resistance for all tested antimicrobials must be removed from the table}
|
||||
\item{remove_intrinsic_resistant}{logical to indicate that rows and columns with 100\% resistance for all tested antimicrobials must be removed from the table}
|
||||
|
||||
\item{decimal.mark}{the character to be used to indicate the numeric
|
||||
decimal point.}
|
||||
@ -83,12 +83,12 @@ x
|
||||
format(x, translate_ab = "name (atc)")
|
||||
|
||||
# Use FUN to change to transformation of microorganism codes
|
||||
x <- bug_drug_combinations(example_isolates,
|
||||
FUN = mo_gramstain)
|
||||
bug_drug_combinations(example_isolates,
|
||||
FUN = mo_gramstain)
|
||||
|
||||
x <- bug_drug_combinations(example_isolates,
|
||||
FUN = function(x) ifelse(x == as.mo("E. coli"),
|
||||
"E. coli",
|
||||
"Others"))
|
||||
bug_drug_combinations(example_isolates,
|
||||
FUN = function(x) ifelse(x == as.mo("E. coli"),
|
||||
"E. coli",
|
||||
"Others"))
|
||||
}
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
BIN
man/figures/mo_matching_score.png
Normal file
BIN
man/figures/mo_matching_score.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
@ -71,40 +71,43 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
|
||||
}
|
||||
|
||||
\examples{
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
filter_aminoglycosides(example_isolates)
|
||||
|
||||
# filter on isolates that have any result for any aminoglycoside
|
||||
example_isolates \%>\% filter_ab_class("aminoglycoside")
|
||||
example_isolates \%>\% filter_aminoglycosides()
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
|
||||
# this is essentially the same as (but without determination of column names):
|
||||
example_isolates \%>\%
|
||||
filter_at(.vars = vars(c("GEN", "TOB", "AMK", "KAN")),
|
||||
.vars_predicate = any_vars(. \%in\% c("S", "I", "R")))
|
||||
# filter on isolates that have any result for any aminoglycoside
|
||||
example_isolates \%>\% filter_aminoglycosides()
|
||||
example_isolates \%>\% filter_ab_class("aminoglycoside")
|
||||
|
||||
# this is essentially the same as (but without determination of column names):
|
||||
example_isolates \%>\%
|
||||
filter_at(.vars = vars(c("GEN", "TOB", "AMK", "KAN")),
|
||||
.vars_predicate = any_vars(. \%in\% c("S", "I", "R")))
|
||||
|
||||
|
||||
# filter on isolates that show resistance to ANY aminoglycoside
|
||||
example_isolates \%>\% filter_aminoglycosides("R", "any")
|
||||
|
||||
# filter on isolates that show resistance to ALL aminoglycosides
|
||||
example_isolates \%>\% filter_aminoglycosides("R", "all")
|
||||
|
||||
# filter on isolates that show resistance to
|
||||
# any aminoglycoside and any fluoroquinolone
|
||||
example_isolates \%>\%
|
||||
filter_aminoglycosides("R") \%>\%
|
||||
filter_fluoroquinolones("R")
|
||||
|
||||
# filter on isolates that show resistance to
|
||||
# all aminoglycosides and all fluoroquinolones
|
||||
example_isolates \%>\%
|
||||
filter_aminoglycosides("R", "all") \%>\%
|
||||
filter_fluoroquinolones("R", "all")
|
||||
|
||||
# with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
|
||||
example_isolates \%>\% filter_carbapenems("R", "all")
|
||||
example_isolates \%>\% filter(across(carbapenems(), ~. == "R"))
|
||||
# filter on isolates that show resistance to ANY aminoglycoside
|
||||
example_isolates \%>\% filter_aminoglycosides("R", "any")
|
||||
|
||||
# filter on isolates that show resistance to ALL aminoglycosides
|
||||
example_isolates \%>\% filter_aminoglycosides("R", "all")
|
||||
|
||||
# filter on isolates that show resistance to
|
||||
# any aminoglycoside and any fluoroquinolone
|
||||
example_isolates \%>\%
|
||||
filter_aminoglycosides("R") \%>\%
|
||||
filter_fluoroquinolones("R")
|
||||
|
||||
# filter on isolates that show resistance to
|
||||
# all aminoglycosides and all fluoroquinolones
|
||||
example_isolates \%>\%
|
||||
filter_aminoglycosides("R", "all") \%>\%
|
||||
filter_fluoroquinolones("R", "all")
|
||||
|
||||
# with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
|
||||
example_isolates \%>\% filter_carbapenems("R", "all")
|
||||
example_isolates \%>\% filter(across(carbapenems(), ~. == "R"))
|
||||
}
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
|
||||
@ -98,15 +98,16 @@ To conduct an analysis of antimicrobial resistance, you should only include the
|
||||
|
||||
All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
|
||||
|
||||
The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to one of:\preformatted{ x \%>\% filter(first_isolate(., ...))
|
||||
The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to either:\preformatted{ x[first_isolate(x, ...), ]
|
||||
x \%>\% filter(first_isolate(x, ...))
|
||||
}
|
||||
|
||||
The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} is essentially equal to:\preformatted{ x \%>\%
|
||||
mutate(keyab = key_antibiotics(.)) \%>\%
|
||||
mutate(only_weighted_firsts = first_isolate(x,
|
||||
col_keyantibiotics = "keyab", ...)) \%>\%
|
||||
filter(only_weighted_firsts == TRUE) \%>\%
|
||||
select(-only_weighted_firsts, -keyab)
|
||||
The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} is essentially equal to:\preformatted{ x \%>\%
|
||||
mutate(keyab = key_antibiotics(.)) \%>\%
|
||||
mutate(only_weighted_firsts = first_isolate(x,
|
||||
col_keyantibiotics = "keyab", ...)) \%>\%
|
||||
filter(only_weighted_firsts == TRUE) \%>\%
|
||||
select(-only_weighted_firsts, -keyab)
|
||||
}
|
||||
}
|
||||
\section{Key antibiotics}{
|
||||
@ -139,49 +140,40 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
|
||||
# `example_isolates` is a dataset available in the AMR package.
|
||||
# See ?example_isolates.
|
||||
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
# Filter on first isolates:
|
||||
example_isolates \%>\%
|
||||
mutate(first_isolate = first_isolate(.)) \%>\%
|
||||
filter(first_isolate == TRUE)
|
||||
# basic filtering on first isolates
|
||||
example_isolates[first_isolate(example_isolates), ]
|
||||
|
||||
# Now let's see if first isolates matter:
|
||||
A <- example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
B <- example_isolates \%>\%
|
||||
filter_first_weighted_isolate() \%>\% # the 1st isolate filter
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
# Have a look at A and B.
|
||||
# B is more reliable because every isolate is counted only once.
|
||||
# Gentamicin resistance in hospital D appears to be 3.7\% higher than
|
||||
# when you (erroneously) would have used all isolates for analysis.
|
||||
|
||||
|
||||
## OTHER EXAMPLES:
|
||||
|
||||
# Short-hand versions:
|
||||
example_isolates \%>\%
|
||||
filter_first_isolate()
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
# Filter on first isolates:
|
||||
example_isolates \%>\%
|
||||
mutate(first_isolate = first_isolate(.)) \%>\%
|
||||
filter(first_isolate == TRUE)
|
||||
|
||||
# Short-hand versions:
|
||||
example_isolates \%>\%
|
||||
filter_first_isolate()
|
||||
|
||||
example_isolates \%>\%
|
||||
filter_first_weighted_isolate()
|
||||
|
||||
example_isolates \%>\%
|
||||
filter_first_weighted_isolate()
|
||||
|
||||
|
||||
# set key antibiotics to a new variable
|
||||
x$keyab <- key_antibiotics(x)
|
||||
|
||||
x$first_isolate <- first_isolate(x)
|
||||
|
||||
x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
|
||||
|
||||
x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
|
||||
# Now let's see if first isolates matter:
|
||||
A <- example_isolates \%>\%
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
B <- example_isolates \%>\%
|
||||
filter_first_weighted_isolate() \%>\% # the 1st isolate filter
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(GEN), # gentamicin availability
|
||||
resistance = resistance(GEN)) # gentamicin resistance
|
||||
|
||||
# Have a look at A and B.
|
||||
# B is more reliable because every isolate is counted only once.
|
||||
# Gentamicin resistance in hospital D appears to be 3.7\% higher than
|
||||
# when you (erroneously) would have used all isolates for analysis.
|
||||
}
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
|
||||
@ -118,8 +118,7 @@ The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unly
|
||||
# See ?example_isolates.
|
||||
|
||||
# See ?pca for more info about Principal Component Analysis (PCA).
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
if (require("dplyr")) {
|
||||
pca_model <- example_isolates \%>\%
|
||||
filter(mo_genus(mo) == "Staphylococcus") \%>\%
|
||||
group_by(species = mo_shortname(mo)) \%>\%
|
||||
|
||||
@ -193,14 +193,14 @@ if (require("ggplot2") & require("dplyr")) {
|
||||
|
||||
}
|
||||
|
||||
\dontrun{
|
||||
\donttest{
|
||||
|
||||
# resistance of ciprofloxacine per age group
|
||||
example_isolates \%>\%
|
||||
mutate(first_isolate = first_isolate(.)) \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
mo == as.mo("E. coli")) \%>\%
|
||||
# `age_group` is also a function of this package:
|
||||
# `age_groups` is also a function of this AMR package:
|
||||
group_by(age_group = age_groups(age)) \%>\%
|
||||
select(age_group,
|
||||
CIP) \%>\%
|
||||
@ -209,7 +209,8 @@ example_isolates \%>\%
|
||||
# for colourblind mode, use divergent colours from the viridis package:
|
||||
example_isolates \%>\%
|
||||
select(AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
ggplot_rsi() + scale_fill_viridis_d()
|
||||
ggplot_rsi() +
|
||||
scale_fill_viridis_d()
|
||||
# a shorter version which also adjusts data label colours:
|
||||
example_isolates \%>\%
|
||||
select(AMX, NIT, FOS, TMP, CIP) \%>\%
|
||||
|
||||
29
man/join.Rd
29
man/join.Rd
@ -57,18 +57,21 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
|
||||
left_join_microorganisms(as.mo("K. pneumoniae"))
|
||||
left_join_microorganisms("B_KLBSL_PNE")
|
||||
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
example_isolates \%>\% left_join_microorganisms()
|
||||
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
|
||||
"E. coli", "E. coli", "E. coli")),
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(df)
|
||||
df_joined <- left_join_microorganisms(df, "bacteria")
|
||||
colnames(df_joined)
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
left_join_microorganisms() \%>\%
|
||||
colnames()
|
||||
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
|
||||
"E. coli", "E. coli", "E. coli")),
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(df)
|
||||
df_joined <- left_join_microorganisms(df, "bacteria")
|
||||
colnames(df_joined)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -136,32 +136,34 @@ On our website \url{https://msberends.github.io/AMR} you can find \href{https://
|
||||
# `example_isolates` is a dataset available in the AMR package.
|
||||
# See ?example_isolates.
|
||||
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
# set key antibiotics to a new variable
|
||||
my_patients <- example_isolates \%>\%
|
||||
mutate(keyab = key_antibiotics(.)) \%>\%
|
||||
mutate(
|
||||
# now calculate first isolates
|
||||
first_regular = first_isolate(., col_keyantibiotics = FALSE),
|
||||
# and first WEIGHTED isolates
|
||||
first_weighted = first_isolate(., col_keyantibiotics = "keyab")
|
||||
)
|
||||
|
||||
# Check the difference, in this data set it results in 7\% more isolates:
|
||||
sum(my_patients$first_regular, na.rm = TRUE)
|
||||
sum(my_patients$first_weighted, na.rm = TRUE)
|
||||
}
|
||||
|
||||
# output of the `key_antibiotics` function could be like this:
|
||||
strainA <- "SSSRR.S.R..S"
|
||||
strainB <- "SSSIRSSSRSSS"
|
||||
|
||||
# can those strings can be compared with:
|
||||
key_antibiotics_equal(strainA, strainB)
|
||||
# TRUE, because I is ignored (as well as missing values)
|
||||
|
||||
key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
|
||||
# FALSE, because I is not ignored and so the 4th value differs
|
||||
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
# set key antibiotics to a new variable
|
||||
my_patients <- example_isolates \%>\%
|
||||
mutate(keyab = key_antibiotics(.)) \%>\%
|
||||
mutate(
|
||||
# now calculate first isolates
|
||||
first_regular = first_isolate(., col_keyantibiotics = FALSE),
|
||||
# and first WEIGHTED isolates
|
||||
first_weighted = first_isolate(., col_keyantibiotics = "keyab")
|
||||
)
|
||||
|
||||
# Check the difference, in this data set it results in 7\% more isolates:
|
||||
sum(my_patients$first_regular, na.rm = TRUE)
|
||||
sum(my_patients$first_weighted, na.rm = TRUE)
|
||||
}
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
\code{\link[=first_isolate]{first_isolate()}}
|
||||
|
||||
@ -68,10 +68,11 @@ a \%like\% b
|
||||
#> TRUE TRUE TRUE
|
||||
|
||||
# get isolates whose name start with 'Ent' or 'ent'
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
example_isolates \%>\%
|
||||
filter(mo_name(mo) \%like\% "^ent")
|
||||
\donttest{
|
||||
if (require("dplyr")) {
|
||||
example_isolates \%>\%
|
||||
filter(mo_name(mo) \%like\% "^ent")
|
||||
}
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
|
||||
33
man/mdro.Rd
33
man/mdro.Rd
File diff suppressed because one or more lines are too long
@ -23,21 +23,22 @@ With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\li
|
||||
where:
|
||||
\itemize{
|
||||
\item \eqn{x} is the user input;
|
||||
\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
|
||||
\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
|
||||
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
|
||||
\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
|
||||
\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
|
||||
\item \eqn{n} is a taxonomic name (genus, species, and subspecies);
|
||||
\item \eqn{l_n}{l_n} is the length of \eqn{n};
|
||||
\item lev is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function}, which counts any insertion, deletion and substitution as 1 that is needed to change \eqn{x} into \eqn{n};
|
||||
\item \eqn{p_n}{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
|
||||
\item \eqn{k_n}{p_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
|
||||
}
|
||||
|
||||
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
|
||||
The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella},\\\emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium},\emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
|
||||
|
||||
All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
|
||||
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.079}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
|
||||
}
|
||||
|
||||
\examples{
|
||||
as.mo("E. coli")
|
||||
mo_uncertainties()
|
||||
|
||||
mo_matching_score("E. coli", "Escherichia coli")
|
||||
mo_matching_score(x = "E. coli",
|
||||
n = c("Escherichia coli", "Entamoeba coli"))
|
||||
}
|
||||
|
||||
@ -133,16 +133,16 @@ With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\li
|
||||
where:
|
||||
\itemize{
|
||||
\item \eqn{x} is the user input;
|
||||
\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
|
||||
\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
|
||||
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
|
||||
\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
|
||||
\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
|
||||
\item \eqn{n} is a taxonomic name (genus, species, and subspecies);
|
||||
\item \eqn{l_n}{l_n} is the length of \eqn{n};
|
||||
\item lev is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function}, which counts any insertion, deletion and substitution as 1 that is needed to change \eqn{x} into \eqn{n};
|
||||
\item \eqn{p_n}{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
|
||||
\item \eqn{k_n}{p_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
|
||||
}
|
||||
|
||||
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
|
||||
The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella},\\\emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium},\emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
|
||||
|
||||
All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
|
||||
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.079}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
|
||||
}
|
||||
|
||||
\section{Catalogue of Life}{
|
||||
|
||||
@ -17,6 +17,9 @@ Text
|
||||
\description{
|
||||
Return the symbol related to the p-value: 0 '\verb{***}' 0.001 '\verb{**}' 0.01 '\code{*}' 0.05 '\code{.}' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
|
||||
}
|
||||
\details{
|
||||
\strong{NOTE}: this function will be moved to the \code{cleaner} package when a new version is being published on CRAN.
|
||||
}
|
||||
\section{Questioning lifecycle}{
|
||||
|
||||
\if{html}{\figure{lifecycle_questioning.svg}{options: style=margin-bottom:5px} \cr}
|
||||
|
||||
34
man/pca.Rd
34
man/pca.Rd
@ -69,21 +69,23 @@ The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unly
|
||||
# `example_isolates` is a dataset available in the AMR package.
|
||||
# See ?example_isolates.
|
||||
|
||||
\dontrun{
|
||||
# calculate the resistance per group first
|
||||
library(dplyr)
|
||||
resistance_data <- example_isolates \%>\%
|
||||
group_by(order = mo_order(mo), # group on anything, like order
|
||||
genus = mo_genus(mo)) \%>\% # and genus as we do here
|
||||
summarise_if(is.rsi, resistance) # then get resistance of all drugs
|
||||
|
||||
# now conduct PCA for certain antimicrobial agents
|
||||
pca_result <- resistance_data \%>\%
|
||||
pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
|
||||
|
||||
pca_result
|
||||
summary(pca_result)
|
||||
biplot(pca_result)
|
||||
ggplot_pca(pca_result) # a new and convenient plot function
|
||||
\donttest{
|
||||
|
||||
if (require("dplyr")) {
|
||||
# calculate the resistance per group first
|
||||
resistance_data <- example_isolates \%>\%
|
||||
group_by(order = mo_order(mo), # group on anything, like order
|
||||
genus = mo_genus(mo)) \%>\% # and genus as we do here
|
||||
summarise_if(is.rsi, resistance) # then get resistance of all drugs
|
||||
|
||||
# now conduct PCA for certain antimicrobial agents
|
||||
pca_result <- resistance_data \%>\%
|
||||
pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)
|
||||
|
||||
pca_result
|
||||
summary(pca_result)
|
||||
biplot(pca_result)
|
||||
ggplot_pca(pca_result) # a new and convenient plot function
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -218,15 +218,6 @@ if (require("dplyr")) {
|
||||
group_by(hospital_id) \%>\%
|
||||
proportion_df(translate = FALSE)
|
||||
}
|
||||
|
||||
\dontrun{
|
||||
# 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{
|
||||
\code{\link[=count]{count()}} to count resistant and susceptible isolates.
|
||||
|
||||
@ -150,9 +150,7 @@ if (require("dplyr")) {
|
||||
}
|
||||
|
||||
# create nice plots with ggplot2 yourself
|
||||
\dontrun{
|
||||
library(dplyr)
|
||||
library(ggplot2)
|
||||
if (require("dplyr") & require("ggplot2")) {
|
||||
|
||||
data <- example_isolates \%>\%
|
||||
filter(mo == as.mo("E. coli")) \%>\%
|
||||
|
||||
Reference in New Issue
Block a user