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Replace RSI with SIR
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R/data.R
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R/data.R
@ -173,7 +173,7 @@
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#' - `gender`\cr Gender of the patient, either `r vector_or(example_isolates$gender)`
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#' - `ward`\cr Ward type where the patient was admitted, either `r vector_or(example_isolates$ward)`
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#' - `mo`\cr ID of microorganism created with [as.mo()], see also the [microorganisms] data set
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#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [set_ab_names()] or [ab_name()]
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#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.sir))` different antibiotics with class [`sir`] (see [as.sir()]); these column names occur in the [antibiotics] data set and can be translated with [set_ab_names()] or [ab_name()]
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#' @details
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#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
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#' @examples
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@ -188,7 +188,7 @@
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#' - `date`\cr date of receipt at the laboratory
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#' - `hospital`\cr ID of the hospital, from A to C
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#' - `bacteria`\cr info about microorganism that can be transformed with [as.mo()], see also [microorganisms]
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#' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.rsi()]
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#' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.sir()]
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#' @details
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#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
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#' @examples
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@ -224,19 +224,19 @@
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#' - `Inducible clindamycin resistance`\cr Clindamycin can be induced?
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#' - `Comment`\cr Other comments
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#' - `Date of data entry`\cr [Date] this data was entered in WHONET
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#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()].
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#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.sir))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.sir()].
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#' @details
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#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
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#' @examples
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#' WHONET
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"WHONET"
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#' Data Set for R/SI Interpretation
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#' Data Set with Clinical Breakpoints for SIR Interpretation
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#'
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#' Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
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#' @format A [tibble][tibble::tibble] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables:
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#' Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. Currently implemented guidelines are EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`). Use [as.sir()] to transform MICs or disks measurements to SIR values.
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#' @format A [tibble][tibble::tibble] with `r format(nrow(clinical_breakpoints), big.mark = ",")` observations and `r ncol(clinical_breakpoints)` variables:
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#' - `guideline`\cr Name of the guideline
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#' - `method`\cr Either `r vector_or(rsi_translation$method)`
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#' - `method`\cr Either `r vector_or(clinical_breakpoints$method)`
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#' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
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#' - `mo`\cr Microbial ID, see [as.mo()]
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#' - `rank_index`\cr Taxonomic rank index of `mo` from 1 (subspecies/infraspecies) to 5 (unknown microorganism)
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@ -252,8 +252,8 @@
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#' They **allow for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.
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#' @seealso [intrinsic_resistant]
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#' @examples
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#' rsi_translation
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"rsi_translation"
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#' clinical_breakpoints
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"clinical_breakpoints"
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#' Data Set with Bacterial Intrinsic Resistance
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#'
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