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(v2.1.1.9242) sir log

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dr. M.S. (Matthijs) Berends 2025-04-18 16:03:50 +02:00
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2
DESCRIPTION
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Package: AMR Package: AMR
Version: 2.1.1.9241 Version: 2.1.1.9242
Date: 2025-04-18 Date: 2025-04-18
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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NEWS.md
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# AMR 2.1.1.9241 # AMR 2.1.1.9242
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)* *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)*

89
R/sir.R
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@ -111,7 +111,7 @@
#' #'
#' 4. For **interpreting a complete data set**, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.sir(your_data)`. #' 4. For **interpreting a complete data set**, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.sir(your_data)`.
#' #'
#' **For points 2, 3 and 4: Use [sir_interpretation_history()]** to retrieve a [data.frame] (or [tibble][tibble::tibble()] if the `tibble` package is installed) with all results of the last [as.sir()] call. #' **For points 2, 3 and 4: Use [sir_interpretation_history()]** to retrieve a [data.frame] with all results of all previous [as.sir()] calls. It also contains notes about interpretation, and the exact input and output values.
#' #'
#' ### Supported Guidelines #' ### Supported Guidelines
#' #'
@ -1506,30 +1506,29 @@ as_sir_method <- function(method_short,
} }
if (NROW(breakpoints_current) == 0) { if (NROW(breakpoints_current) == 0) {
AMR_env$sir_interpretation_history <- rbind_AMR( out <- data.frame(
AMR_env$sir_interpretation_history,
# recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
data.frame( datetime = vectorise_log_entry(Sys.time(), length(rows)),
datetime = vectorise_log_entry(Sys.time(), length(rows)), index = rows,
index = rows, method = vectorise_log_entry(method_coerced, length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)), ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)), mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)), host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)), input_given = vectorise_log_entry(as.character(values_bak), length(rows)),
input_given = vectorise_log_entry(as.character(values_bak), length(rows)), ab = vectorise_log_entry(ab_current, length(rows)),
ab = vectorise_log_entry(ab_current, length(rows)), mo = vectorise_log_entry(mo_current, length(rows)),
mo = vectorise_log_entry(mo_current, length(rows)), host = vectorise_log_entry(host_current, length(rows)),
host = vectorise_log_entry(host_current, length(rows)), input = vectorise_log_entry(as.character(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)), outcome = vectorise_log_entry(NA_sir_, length(rows)),
outcome = vectorise_log_entry(NA_sir_, length(rows)), notes = vectorise_log_entry("No breakpoint available", length(rows)),
notes = vectorise_log_entry("No breakpoint available", length(rows)), guideline = vectorise_log_entry(guideline_current, length(rows)),
guideline = vectorise_log_entry(guideline_current, length(rows)), ref_table = vectorise_log_entry(NA_character_, length(rows)),
ref_table = vectorise_log_entry(NA_character_, length(rows)), uti = vectorise_log_entry(uti_current, length(rows)),
uti = vectorise_log_entry(uti_current, length(rows)), breakpoint_S_R = vectorise_log_entry(NA_character_, length(rows)),
breakpoint_S_R = vectorise_log_entry(NA_character_, length(rows)), stringsAsFactors = FALSE
stringsAsFactors = FALSE
)
) )
out <- subset(out, !is.na(input_given))
AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
notes <- c(notes, notes_current) notes <- c(notes, notes_current)
next next
} }
@ -1636,6 +1635,7 @@ as_sir_method <- function(method_short,
) )
} }
## actual interpretation ----
if (method == "mic") { if (method == "mic") {
new_sir <- case_when_AMR( new_sir <- case_when_AMR(
is.na(values) ~ NA_sir_, is.na(values) ~ NA_sir_,
@ -1668,30 +1668,29 @@ as_sir_method <- function(method_short,
# write to verbose output # write to verbose output
notes_current <- trimws2(notes_current) notes_current <- trimws2(notes_current)
notes_current[notes_current == ""] <- NA_character_ notes_current[notes_current == ""] <- NA_character_
AMR_env$sir_interpretation_history <- rbind_AMR( out <- data.frame(
AMR_env$sir_interpretation_history,
# recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
data.frame( datetime = vectorise_log_entry(Sys.time(), length(rows)),
datetime = vectorise_log_entry(Sys.time(), length(rows)), index = rows,
index = rows, method = vectorise_log_entry(method_coerced, length(rows)),
method = vectorise_log_entry(method_coerced, length(rows)), ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)), mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)), host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)), input_given = vectorise_log_entry(as.character(values_bak), length(rows)),
input_given = vectorise_log_entry(as.character(values_bak), length(rows)), ab = vectorise_log_entry(breakpoints_current[, "ab", drop = TRUE], length(rows)),
ab = vectorise_log_entry(breakpoints_current[, "ab", drop = TRUE], length(rows)), mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)),
mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)), host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)),
host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)), input = vectorise_log_entry(as.character(values), length(rows)),
input = vectorise_log_entry(as.character(values), length(rows)), outcome = vectorise_log_entry(as.sir(new_sir), length(rows)),
outcome = vectorise_log_entry(as.sir(new_sir), length(rows)), notes = font_stripstyle(notes_current), # vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)),
notes = font_stripstyle(notes_current), # vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)), guideline = vectorise_log_entry(guideline_current, length(rows)),
guideline = vectorise_log_entry(guideline_current, length(rows)), ref_table = vectorise_log_entry(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
ref_table = vectorise_log_entry(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)), uti = vectorise_log_entry(breakpoints_current[, "uti", drop = TRUE], length(rows)),
uti = vectorise_log_entry(breakpoints_current[, "uti", drop = TRUE], length(rows)), breakpoint_S_R = vectorise_log_entry(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
breakpoint_S_R = vectorise_log_entry(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)), stringsAsFactors = FALSE
stringsAsFactors = FALSE
)
) )
out <- subset(out, !is.na(input_given))
AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
} }
notes <- c(notes, notes_current) notes <- c(notes, notes_current)

4
data-raw/gpt_training_text_v2.1.1.9241.txt → data-raw/gpt_training_text_v2.1.1.9242.txt
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This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse. This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
First and foremost, you are trained on version 2.1.1.9241. Remember this whenever someone asks which AMR package version you’re at. First and foremost, you are trained on version 2.1.1.9242. Remember this whenever someone asks which AMR package version you’re at.
Below are the contents of the NAMESPACE file, the index.md file, and all the man/*.Rd files (documentation) in the package. Every file content is split using 100 hypens. Below are the contents of the NAMESPACE file, the index.md file, and all the man/*.Rd files (documentation) in the package. Every file content is split using 100 hypens.
---------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------
@ -3567,7 +3567,7 @@ your_data \%>\% mutate_if(is.disk, as.sir, host = "column_with_animal_species",
\item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.sir(your_data)}. \item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.sir(your_data)}.
} }
\strong{For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}}} to retrieve a \link{data.frame} (or \link[tibble:tibble]{tibble} if the \code{tibble} package is installed) with all results of the last \code{\link[=as.sir]{as.sir()}} call. \strong{For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}}} to retrieve a \link{data.frame} with all results of all previous \code{\link[=as.sir]{as.sir()}} calls. It also contains notes about interpretation, and the exact input and output values.
} }
\subsection{Supported Guidelines}{ \subsection{Supported Guidelines}{

2
man/as.sir.Rd
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@ -191,7 +191,7 @@ your_data \%>\% mutate_if(is.disk, as.sir, host = "column_with_animal_species",
\item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.sir(your_data)}. \item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.sir(your_data)}.
} }
\strong{For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}}} to retrieve a \link{data.frame} (or \link[tibble:tibble]{tibble} if the \code{tibble} package is installed) with all results of the last \code{\link[=as.sir]{as.sir()}} call. \strong{For points 2, 3 and 4: Use \code{\link[=sir_interpretation_history]{sir_interpretation_history()}}} to retrieve a \link{data.frame} with all results of all previous \code{\link[=as.sir]{as.sir()}} calls. It also contains notes about interpretation, and the exact input and output values.
} }
\subsection{Supported Guidelines}{ \subsection{Supported Guidelines}{