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(v0.7.1.9005) new rsi calculations, atc class removal

pull/67/head
dr. M.S. (Matthijs) Berends 2019-07-01 14:03:15 +02:00
parent 65c6702b21
commit 156d550895
78 changed files with 1169 additions and 911 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.7.1.9004
Date: 2019-06-27
Version: 0.7.1.9005
Date: 2019-07-01
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

1
NAMESPACE
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@ -224,7 +224,6 @@ importFrom(crayon,black)
importFrom(crayon,blue)
importFrom(crayon,bold)
importFrom(crayon,green)
importFrom(crayon,has_color)
importFrom(crayon,italic)
importFrom(crayon,magenta)
importFrom(crayon,red)

35
NEWS.md
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@ -1,11 +1,42 @@
# AMR 0.7.1.9004
# AMR 0.7.1.9005
### New
* Additional way to calculate co-resistance, i.e. when using multiple antibiotics as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibily of a combination therapy. A new parameter `only_all_tested` replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:
```r
# -------------------------------------------------------------------------
# only_all_tested = FALSE only_all_tested = TRUE
# Antibiotic Antibiotic ----------------------- -----------------------
# A B include as include as include as include as
# numerator denominator numerator denominator
# ---------- ---------- ---------- ----------- ---------- -----------
# S S X X X X
# I S X X X X
# R S X X X X
# not tested S X X - -
# S I X X X X
# I I X X X X
# R I X X X X
# not tested I X X - -
# S R X X X X
# I R X X X X
# R R - X - X
# not tested R - - - -
# S not tested X X - -
# I not tested X X - -
# R not tested - - - -
# not tested not tested - - - -
# -------------------------------------------------------------------------
```
Since this is a major change, usage of the old `also_single_tested` will throw an informative error that it has been replaced by `only_all_tested`.
### Changed
* Removed class `atc` - using `as.atc()` is now deprecated in favour of `ab_atc()` and this will return a character, not the `atc` class anymore
* Removed deprecated functions `abname()`, `ab_official()`, `atc_name()`, `atc_official()`, `atc_property()`, `atc_tradenames()`, `atc_trivial_nl()`
* Fix and speed improvement for `mo_shortname()`
* Fix for `as.mo()` where misspelled input would not be understood
* Fix for `also_single_tested` parameter in `count_*` functions
* Fix for using `mo_*` functions where the coercion uncertainties and failures would not be available through `mo_uncertainties()` and `mo_failures()` anymore
# AMR 0.7.1

76
R/count.R
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@ -34,6 +34,7 @@
#' The function \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of S, I and R. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
#'
#' The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R.
#' @inheritSection portion Combination therapy
#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
#' @keywords resistance susceptibility rsi antibiotics isolate isolates
@ -61,8 +62,8 @@
#' # Since n_rsi counts available isolates, you can
#' # calculate back to count e.g. non-susceptible isolates.
#' # This results in the same:
#' count_IR(septic_patients$AMX)
#' portion_IR(septic_patients$AMX) * n_rsi(septic_patients$AMX)
#' count_SI(septic_patients$AMX)
#' portion_SI(septic_patients$AMX) * n_rsi(septic_patients$AMX)
#'
#' library(dplyr)
#' septic_patients %>%
@ -76,17 +77,17 @@
#'
#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy.
#' # Please mind that `portion_S` calculates percentages right away instead.
#' count_S(septic_patients$AMC) # S = 1342 (71.4%)
#' count_all(septic_patients$AMC) # n = 1879
#' # Please mind that `portion_SI` calculates percentages right away instead.
#' count_SI(septic_patients$AMC) # 1433
#' count_all(septic_patients$AMC) # 1879
#'
#' count_S(septic_patients$GEN) # S = 1372 (74.0%)
#' count_all(septic_patients$GEN) # n = 1855
#' count_SI(septic_patients$GEN) # 1399
#' count_all(septic_patients$GEN) # 1855
#'
#' with(septic_patients,
#' count_S(AMC, GEN)) # S = 1660 (92.3%)
#' with(septic_patients, # n = 1798
#' n_rsi(AMC, GEN))
#' count_SI(AMC, GEN)) # 1764
#' with(septic_patients,
#' n_rsi(AMC, GEN)) # 1936
#'
#' # Get portions S/I/R immediately of all rsi columns
#' septic_patients %>%
@ -99,71 +100,56 @@
#' group_by(hospital_id) %>%
#' count_df(translate = FALSE)
#'
count_R <- function(..., also_single_tested = FALSE) {
count_R <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = also_single_tested,
ab_result = "R",
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count
#' @export
count_IR <- function(..., also_single_tested = FALSE) {
count_IR <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
also_single_tested = also_single_tested,
ab_result = c("I", "R"),
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count
#' @export
count_I <- function(..., also_single_tested = FALSE) {
count_I <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
type = "I",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = also_single_tested,
ab_result = "I",
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count
#' @export
count_SI <- function(..., also_single_tested = FALSE) {
count_SI <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
also_single_tested = also_single_tested,
ab_result = c("S", "I"),
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count
#' @export
count_S <- function(..., also_single_tested = FALSE) {
count_S <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = also_single_tested,
ab_result = "S",
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count
#' @export
count_all <- function(..., also_single_tested = FALSE) {
res_SI <- count_SI(..., also_single_tested = also_single_tested)
# only print warnings once, if needed
res_R <- suppressWarnings(count_R(..., also_single_tested = also_single_tested))
res_SI + res_R
count_all <- function(..., only_all_tested = FALSE) {
rsi_calc(...,
ab_result = c("S", "I", "R"),
only_all_tested = only_all_tested,
only_count = TRUE)
}
#' @rdname count

110
R/mo.R
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@ -87,12 +87,9 @@
#' \strong{Uncertain results} \cr
#' The algorithm can additionally use three different levels of uncertainty to guess valid results. The default is \code{allow_uncertain = TRUE}, which is equal to uncertainty level 2. Using \code{allow_uncertain = FALSE} will skip all of these additional rules:
#' \itemize{
#' \item{(uncertainty level 1): It tries to look for only matching genera}
#' \item{(uncertainty level 1): It tries to look for previously accepted (but now invalid) taxonomic names}
#' \item{(uncertainty level 2): It strips off values between brackets and the brackets itself, and re-evaluates the input with all previous rules}
#' \item{(uncertainty level 2): It strips off words from the end one by one and re-evaluates the input with all previous rules}
#' \item{(uncertainty level 3): It strips off words from the start one by one and re-evaluates the input with all previous rules}
#' \item{(uncertainty level 3): It tries any part of the name}
#' \item{(uncertainty level 1): It tries to look for only matching genera, previously accepted (but now invalid) taxonomic names and misspelled input}
#' \item{(uncertainty level 2): It removed parts between brackets, strips off words from the end one by one and re-evaluates the input with all previous rules}
#' \item{(uncertainty level 3): It strips off words from the start one by one and tries any part of the name}
#' }
#'
#' You can also use e.g. \code{as.mo(..., allow_uncertain = 1)} to only allow up to level 1 uncertainty.
@ -281,7 +278,7 @@ is.mo <- function(x) {
#' @importFrom dplyr %>% pull left_join n_distinct progress_estimated filter distinct
#' @importFrom data.table data.table as.data.table setkey
#' @importFrom crayon magenta red blue silver italic has_color
#' @importFrom crayon magenta red blue silver italic
# param property a column name of AMR::microorganisms
# param initial_search logical - is FALSE when coming from uncertain tries, which uses exec_as.mo internally too
# param force_mo_history logical - whether found result must be saved with set_mo_history (default FALSE on non-interactive sessions)
@ -486,7 +483,7 @@ exec_as.mo <- function(x,
# remove genus as first word
x <- gsub("^Genus ", "", x)
# allow characters that resemble others
if (uncertainty_level >= 2) {
if (initial_search == FALSE) {
x <- tolower(x)
x <- gsub("[iy]+", "[iy]+", x)
x <- gsub("(c|k|q|qu|s|z|x|ks)+", "(c|k|q|qu|s|z|x|ks)+", x)
@ -768,31 +765,24 @@ exec_as.mo <- function(x,
}
next
}
if (grepl("[sS]almonella [A-Z][a-z]+ ?.*", x_backup_without_spp[i], ignore.case = FALSE)) {
if (x_backup_without_spp[i] %like% "salmonella [a-z]+ ?.*") {
if (x_backup_without_spp[i] %like% "Salmonella group") {
# Salmonella Group A to Z, just return S. species for now
x[i] <- microorganismsDT[mo == 'B_SLMNL', ..property][[1]][1L]
if (initial_search == TRUE) {
set_mo_history(x_backup[i], get_mo_code(x[i], property), 0, force = force_mo_history)
}
options(mo_renamed = c(getOption("mo_renamed"),
magenta(paste0("NOTE: ",
italic("Salmonella"), " ", trimws(gsub("Salmonella", "", x_backup_without_spp[i])),
" was considered ",
italic("Salmonella species"),
" (B_SLMNL)"))))
} else {
} else if (grepl("[sS]almonella [A-Z][a-z]+ ?.*", x_backup_without_spp[i], ignore.case = FALSE)) {
# Salmonella with capital letter species like "Salmonella Goettingen" - they're all S. enterica
x[i] <- microorganismsDT[mo == 'B_SLMNL_ENT', ..property][[1]][1L]
if (initial_search == TRUE) {
set_mo_history(x_backup[i], get_mo_code(x[i], property), 0, force = force_mo_history)
}
options(mo_renamed = c(getOption("mo_renamed"),
magenta(paste0("NOTE: ",
italic("Salmonella"), " ", trimws(gsub("Salmonella", "", x_backup_without_spp[i])),
" was considered a subspecies of ",
italic("Salmonella enterica"),
" (B_SLMNL_ENT)"))))
uncertainties <- rbind(uncertainties,
data.frame(uncertainty = 1,
input = x_backup_without_spp[i],
fullname = microorganismsDT[mo == "B_SLMNL_ENT", fullname][[1]],
mo = "B_SLMNL_ENT"))
}
next
}
@ -1041,9 +1031,27 @@ exec_as.mo <- function(x,
}
return(x)
}
# (2) Try with misspelled input ----
# just rerun with initial_search = FALSE will used the extensive regex part above
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup, initial_search = FALSE, allow_uncertain = FALSE)))
if (!empty_result(found)) {
found_result <- found
found <- microorganismsDT[mo == found, ..property][[1]]
uncertainties <<- rbind(uncertainties,
data.frame(uncertainty = 1,
input = a.x_backup,
fullname = microorganismsDT[mo == found_result[1L], fullname][[1]],
mo = found_result[1L]))
if (initial_search == TRUE) {
set_mo_history(a.x_backup, get_mo_code(found[1L], property), 1, force = force_mo_history)
}
return(found[1L])
}
}
if (uncertainty_level >= 2) {
# (3) look for genus only, part of name ----
if (nchar(g.x_backup_without_spp) > 4 & !b.x_trimmed %like% " ") {
if (!grepl("^[A-Z][a-z]+", b.x_trimmed, ignore.case = FALSE)) {
@ -1286,10 +1294,11 @@ exec_as.mo <- function(x,
post_Becker <- c("argensis", "caeli", "cornubiensis", "edaphicus")
if (any(x %in% MOs_staph[species %in% post_Becker, ..property][[1]])) {
warning("Becker ", italic("et al."), " (2014, 2019) does not contain species named after their publication: ",
warning("Becker ", italic("et al."), " (2014, 2019) does not contain these species named after their publication: ",
italic(paste("S.",
sort(mo_species(unique(x[x %in% MOs_staph[species %in% post_Becker, ..property][[1]]]))),
collapse = ", ")),
".",
call. = FALSE,
immediate. = TRUE)
}
@ -1352,15 +1361,7 @@ exec_as.mo <- function(x,
}
if (length(mo_renamed()) > 0) {
if (has_color()) {
notes <- getOption("mo_renamed")
} else {
notes <- mo_renamed()
}
notes <- sort(notes)
for (i in 1:length(notes)) {
base::message(blue(paste("NOTE:", notes[i])))
}
print(mo_renamed())
}
x
@ -1387,9 +1388,14 @@ was_renamed <- function(name_old, name_new, ref_old = "", ref_new = "", mo = "")
} else {
mo <- ""
}
msg <- paste0(italic(name_old), ref_old, " was renamed ", italic(name_new), ref_new, mo)
msg <- gsub("et al.", italic("et al."), msg)
options(mo_renamed = c(getOption("mo_renamed"), sort(msg)))
old_values <- paste0(italic(name_old), ref_old)
old_values <- gsub("et al.", italic("et al."), old_values)
new_values <- paste0(italic(name_new), ref_new, mo)
new_values <- gsub("et al.", italic("et al."), new_values)
names(new_values) <- old_values
total <- c(getOption("mo_renamed"), new_values)
options(mo_renamed = total[order(names(total))])
}
#' @exportMethod print.mo
@ -1451,6 +1457,9 @@ mo_failures <- function() {
#' @importFrom crayon italic
#' @export
mo_uncertainties <- function() {
if (is.null(getOption("mo_uncertainties"))) {
return(NULL)
}
structure(.Data = as.data.frame(getOption("mo_uncertainties"), stringsAsFactors = FALSE),
class = c("mo_uncertainties", "data.frame"))
}
@ -1463,8 +1472,8 @@ print.mo_uncertainties <- function(x, ...) {
if (NROW(x) == 0) {
return(NULL)
}
cat(paste0(bold(nrow(x), "unique result(s) guessed with uncertainty:"),
"\n(1 = ", green("renamed"),
cat(paste0(bold(nr2char(nrow(x)), paste0("unique result", ifelse(nrow(x) > 1, "s", ""), " guessed with uncertainty:")),
"\n(1 = ", green("renamed/misspelled"),
", 2 = ", yellow("uncertain"),
", 3 = ", red("very uncertain"), ")\n"))
@ -1489,10 +1498,18 @@ print.mo_uncertainties <- function(x, ...) {
}
#' @rdname as.mo
#' @importFrom crayon strip_style
#' @export
mo_renamed <- function() {
structure(.Data = strip_style(gsub("was renamed", "->", getOption("mo_renamed"), fixed = TRUE)),
class = c("mo_renamed", "character"))
items <- getOption("mo_renamed")
if (is.null(items)) {
return(NULL)
}
items <- strip_style(items)
names(items) <- strip_style(names(items))
structure(.Data = items,
class = c("mo_renamed", "character"))
}
#' @exportMethod print.mo_renamed
@ -1500,7 +1517,8 @@ mo_renamed <- function() {
#' @export
#' @noRd
print.mo_renamed <- function(x, ...) {
cat(blue(paste(getOption("mo_renamed"), collapse = "\n")))
items <- getOption("mo_renamed")
base::message(blue(paste("NOTE:", names(items), "was renamed", items, collapse = "\n"), collapse = "\n"))
}
nr2char <- function(x) {
@ -1540,3 +1558,15 @@ translate_allow_uncertain <- function(allow_uncertain) {
}
allow_uncertain
}
get_mo_failures_uncertainties_renamed <- function() {
list(failures = getOption("mo_failures"),
uncertainties = getOption("mo_uncertainties"),
renamed = getOption("mo_renamed"))
}
load_mo_failures_uncertainties_renamed <- function(metadata) {
options("mo_failures" = metadata$failures)
options("mo_uncertainties" = metadata$uncertainties)
options("mo_renamed" = metadata$renamed)
}

46
R/mo_property.R
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@ -148,7 +148,9 @@ mo_fullname <- mo_name
#' @importFrom dplyr %>% mutate pull
#' @export
mo_shortname <- function(x, language = get_locale(), ...) {
x.mo <- as.mo(x, ...)
x.mo <- AMR::as.mo(x, ...)
metadata <- get_mo_failures_uncertainties_renamed()
# get first char of genus and complete species in English
shortnames <- paste0(substr(mo_genus(x.mo, language = NULL), 1, 1), ". ", mo_species(x.mo, language = NULL))
@ -158,6 +160,7 @@ mo_shortname <- function(x, language = get_locale(), ...) {
# exceptions for Streptococci
shortnames[shortnames %like% "S. group [ABCDFGHK]"] <- paste0("G", gsub("S. group ([ABCDFGHK])", "\\1", shortnames[shortnames %like% "S. group [ABCDFGHK]"]), "S")
load_mo_failures_uncertainties_renamed(metadata)
translate_AMR(shortnames, language = language, only_unknown = FALSE)
}
@ -218,8 +221,10 @@ mo_type <- function(x, language = get_locale(), ...) {
#' @rdname mo_property
#' @export
mo_gramstain <- function(x, language = get_locale(), ...) {
x.mo <- as.mo(x, ...)
x.phylum <- mo_phylum(x.mo, language = NULL)
x.mo <- AMR::as.mo(x, ...)
metadata <- get_mo_failures_uncertainties_renamed()
x.phylum <- mo_phylum(x.mo)
# DETERMINE GRAM STAIN FOR BACTERIA
# Source: https://itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=956097
# It says this:
@ -232,13 +237,15 @@ mo_gramstain <- function(x, language = get_locale(), ...) {
# Phylum Tenericutes (Murray, 1984)
x <- NA_character_
# make all bacteria Gram negative
x[mo_kingdom(x.mo, language = NULL) == "Bacteria"] <- "Gram-negative"
x[mo_kingdom(x.mo) == "Bacteria"] <- "Gram-negative"
# overwrite these phyla with Gram positive
x[x.phylum %in% c("Actinobacteria",
"Chloroflexi",
"Firmicutes",
"Tenericutes")
| x.mo == "B_GRAMP"] <- "Gram-positive"
load_mo_failures_uncertainties_renamed(metadata)
translate_AMR(x, language = language, only_unknown = FALSE)
}
@ -276,7 +283,9 @@ mo_rank <- function(x, ...) {
#' @export
mo_taxonomy <- function(x, language = get_locale(), ...) {
x <- AMR::as.mo(x, ...)
base::list(kingdom = AMR::mo_kingdom(x, language = language),
metadata <- get_mo_failures_uncertainties_renamed()
result <- base::list(kingdom = AMR::mo_kingdom(x, language = language),
phylum = AMR::mo_phylum(x, language = language),
class = AMR::mo_class(x, language = language),
order = AMR::mo_order(x, language = language),
@ -284,12 +293,17 @@ mo_taxonomy <- function(x, language = get_locale(), ...) {
genus = AMR::mo_genus(x, language = language),
species = AMR::mo_species(x, language = language),
subspecies = AMR::mo_subspecies(x, language = language))
load_mo_failures_uncertainties_renamed(metadata)
result
}
#' @rdname mo_property
#' @export
mo_synonyms <- function(x, ...) {
x <- as.mo(x, ...)
x <- AMR::as.mo(x, ...)
metadata <- get_mo_failures_uncertainties_renamed()
IDs <- AMR::mo_property(x = x, property = "col_id", language = NULL)
syns <- lapply(IDs, function(col_id) {
res <- sort(AMR::microorganisms.old[which(AMR::microorganisms.old$col_id_new == col_id), "fullname"])
@ -301,16 +315,21 @@ mo_synonyms <- function(x, ...) {
})
if (length(syns) > 1) {
names(syns) <- mo_fullname(x)
syns
result <- syns
} else {
unlist(syns)
result <- unlist(syns)
}
load_mo_failures_uncertainties_renamed(metadata)
result
}
#' @rdname mo_property
#' @export
mo_info <- function(x, language = get_locale(), ...) {
x <- AMR::as.mo(x, ...)
metadata <- get_mo_failures_uncertainties_renamed()
info <- lapply(x, function(y)
c(mo_taxonomy(y, language = language),
list(synonyms = mo_synonyms(y),
@ -318,10 +337,13 @@ mo_info <- function(x, language = get_locale(), ...) {
ref = mo_ref(y))))
if (length(info) > 1) {
names(info) <- mo_fullname(x)
info
result <- info
} else {
info[[1L]]
result <- info[[1L]]
}
load_mo_failures_uncertainties_renamed(metadata)
result
}
#' @rdname mo_property
@ -330,6 +352,8 @@ mo_info <- function(x, language = get_locale(), ...) {
#' @export
mo_url <- function(x, open = FALSE, ...) {
mo <- AMR::as.mo(x = x, ... = ...)
metadata <- get_mo_failures_uncertainties_renamed()
df <- data.frame(mo, stringsAsFactors = FALSE) %>%
left_join(select(AMR::microorganisms, mo, source, species_id), by = "mo") %>%
mutate(url = case_when(source == "CoL" ~
@ -347,6 +371,8 @@ mo_url <- function(x, open = FALSE, ...) {
}
browseURL(u[1L])
}
load_mo_failures_uncertainties_renamed(metadata)
u
}

130
R/portion.R
View File

@ -27,36 +27,61 @@
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
#' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.
#' @param also_single_tested a logical to indicate whether for combination therapies also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of \code{portion_S} and R in case of \code{portion_R}). \strong{This could lead to selection bias.}
#' @param only_all_tested (for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below
#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}
#' @inheritParams ab_property
#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.
#' @param combine_IR a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}.
#' @inheritSection as.rsi Interpretation of S, I and R
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set.
#'
#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
#'
#' The function \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each group and each variable with class \code{"rsi"}.
#'
#' The function \code{rsi_df} works exactly like \code{portion_df}, but adds the number of isolates.
#' \if{html}{
# (created with https://www.latex4technics.com/)
#' \cr\cr
#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
#' To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
#' \cr
#' For two antibiotics:
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
#' \cr
#' For three antibiotics:
#' \out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
#' \cr
#' And so on.
#' @section Combination therapy:
#' When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{portion_SI} works to calculate the \%SI:
#'
#' \preformatted{
#' -------------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' Antibiotic Antibiotic ----------------------- -----------------------
#' A B include as include as include as include as
#' numerator denominator numerator denominator
#' ---------- ---------- ---------- ----------- ---------- -----------
#' S S X X X X
#' I S X X X X
#' R S X X X X
#' not tested S X X - -
#' S I X X X X
#' I I X X X X
#' R I X X X X
#' not tested I X X - -
#' S R X X X X
#' I R X X X X
#' R R - X - X
#' not tested R - - - -
#' S not tested X X - -
#' I not tested X X - -
#' R not tested - - - -
#' not tested not tested - - - -
#' -------------------------------------------------------------------------
#' }
#'
#' Please note that for \code{only_all_tested = TRUE} applies that:
#' \preformatted{
#' count_S() + count_I() + count_R() == count_all()
#' portion_S() + portion_I() + portion_R() == 1
#' }
#' and that for \code{only_all_tested = FALSE} applies that:
#' \preformatted{
#' count_S() + count_I() + count_R() >= count_all()
#' portion_S() + portion_I() + portion_R() >= 1
#' }
#'
#' Using \code{only_all_tested} has no impact when only using one antibiotic as input.
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
#'
#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
@ -89,7 +114,7 @@
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(p = portion_S(CIP),
#' summarise(p = portion_SI(CIP),
#' n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr
#'
#' septic_patients %>%
@ -103,32 +128,38 @@
#'
#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy:
#' septic_patients %>% portion_S(AMC) # S = 71.4%
#' septic_patients %>% count_all(AMC) # n = 1879
#' septic_patients %>% portion_SI(AMC) # %SI = 76.3%
#' septic_patients %>% count_all(AMC) # n = 1879
#'
#' septic_patients %>% portion_S(GEN) # S = 74.0%
#' septic_patients %>% count_all(GEN) # n = 1855
#' septic_patients %>% portion_SI(GEN) # %SI = 75.4%
#' septic_patients %>% count_all(GEN) # n = 1855
#'
#' septic_patients %>% portion_S(AMC, GEN) # S = 92.3%
#' septic_patients %>% count_all(AMC, GEN) # n = 1798
#' septic_patients %>% portion_SI(AMC, GEN) # %SI = 94.1%
#' septic_patients %>% count_all(AMC, GEN) # n = 1939
#'
#' # Using `also_single_tested` can be useful ...
#'
#' # See Details on how `only_all_tested` works. Example:
#' septic_patients %>%
#' portion_S(AMC, GEN,
#' also_single_tested = TRUE) # S = 92.6%
#' # ... but can also lead to selection bias - the data only has 2,000 rows:
#' summarise(numerator = count_SI(AMC, GEN),
#' denominator = count_all(AMC, GEN),
#' portion = portion_SI(AMC, GEN))
#' # numerator denominator portion
#' # 1764 1936 0.9408
#' septic_patients %>%
#' count_all(AMC, GEN,
#' also_single_tested = TRUE) # n = 2555
#' summarise(numerator = count_SI(AMC, GEN, only_all_tested = TRUE),
#' denominator = count_all(AMC, GEN, only_all_tested = TRUE),
#' portion = portion_SI(AMC, GEN, only_all_tested = TRUE))
#' # numerator denominator portion
#' # 1687 1798 0.9383
#'
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(cipro_p = portion_S(CIP, as_percent = TRUE),
#' summarise(cipro_p = portion_SI(CIP, as_percent = TRUE),
#' cipro_n = count_all(CIP),
#' genta_p = portion_S(GEN, as_percent = TRUE),
#' genta_p = portion_SI(GEN, as_percent = TRUE),
#' genta_n = count_all(GEN),
#' combination_p = portion_S(CIP, GEN, as_percent = TRUE),
#' combination_p = portion_SI(CIP, GEN, as_percent = TRUE),
#' combination_n = count_all(CIP, GEN))
#'
#' # Get portions S/I/R immediately of all rsi columns
@ -155,13 +186,12 @@
portion_R <- function(...,
minimum = 30,
as_percent = FALSE,
also_single_tested = FALSE) {
only_all_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = FALSE,
ab_result = "R",
minimum = minimum,
as_percent = as_percent,
also_single_tested = also_single_tested,
only_all_tested = only_all_tested,
only_count = FALSE)
}
@ -170,13 +200,12 @@ portion_R <- function(...,
portion_IR <- function(...,
minimum = 30,
as_percent = FALSE,
also_single_tested = FALSE) {
only_all_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = TRUE,
ab_result = c("I", "R"),
minimum = minimum,
as_percent = as_percent,
also_single_tested = also_single_tested,
only_all_tested = only_all_tested,
only_count = FALSE)
}
@ -185,13 +214,12 @@ portion_IR <- function(...,
portion_I <- function(...,
minimum = 30,
as_percent = FALSE,
also_single_tested = FALSE) {
only_all_tested = FALSE) {
rsi_calc(...,
type = "I",
include_I = FALSE,
ab_result = "I",
minimum = minimum,
as_percent = as_percent,
also_single_tested = also_single_tested,
only_all_tested = only_all_tested,
only_count = FALSE)
}
@ -200,13 +228,12 @@ portion_I <- function(...,
portion_SI <- function(...,
minimum = 30,
as_percent = FALSE,
also_single_tested = FALSE) {
only_all_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = TRUE,
ab_result = c("S", "I"),
minimum = minimum,
as_percent = as_percent,
also_single_tested = also_single_tested,
only_all_tested = only_all_tested,
only_count = FALSE)
}
@ -215,13 +242,12 @@ portion_SI <- function(...,
portion_S <- function(...,
minimum = 30,
as_percent = FALSE,
also_single_tested = FALSE) {
only_all_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = FALSE,
ab_result = "S",
minimum = minimum,
as_percent = as_percent,
also_single_tested = also_single_tested,
only_all_tested = only_all_tested,
only_count = FALSE)
}

6
R/rsi.R
View File

@ -36,7 +36,7 @@
#'
#' The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
#' @section Interpretation of S, I and R:
#' In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below. Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
#' In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
#'
#' \itemize{
#' \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
@ -46,9 +46,7 @@
#'
#' Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
#'
#' Source: \url{http://www.eucast.org/newsiandr/}.
#'
#' \strong{This AMR package honours this new insight.}
#' This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates.
#' @return Ordered factor with new class \code{rsi}
#' @keywords rsi
#' @export

80
R/rsi_calc.R
View File

@ -38,30 +38,29 @@ dots2vars <- function(...) {
#' @importFrom dplyr %>% pull all_vars any_vars filter_all funs mutate_all
rsi_calc <- function(...,
type,
include_I,
minimum,
as_percent,
also_single_tested,
only_count) {
ab_result,
minimum = 0,
as_percent = FALSE,
only_all_tested = FALSE,
only_count = FALSE) {
data_vars <- dots2vars(...)
if (!is.logical(include_I)) {
stop('`include_I` must be logical', call. = FALSE)
}
if (!is.numeric(minimum)) {
stop('`minimum` must be numeric', call. = FALSE)
}
if (!is.logical(as_percent)) {
stop('`as_percent` must be logical', call. = FALSE)
}
if (!is.logical(also_single_tested)) {
stop('`also_single_tested` must be logical', call. = FALSE)
if (!is.logical(only_all_tested)) {
stop('`only_all_tested` must be logical', call. = FALSE)
}
dots_df <- ...elt(1) # it needs this evaluation
dots <- base::eval(base::substitute(base::alist(...)))
if ("also_single_tested" %in% names(dots)) {
stop("`also_single_tested` was replaced by `only_all_tested`. Please read Details in the help page (`?portion`) as this may have a considerable impact on your analysis.", call. = FALSE)
}
ndots <- length(dots)
if ("data.frame" %in% class(dots_df)) {
@ -99,8 +98,7 @@ rsi_calc <- function(...,
print_warning <- FALSE
type_trans <- as.integer(as.rsi(type))
type_others <- base::setdiff(1:3, type_trans)
ab_result <- as.rsi(ab_result)
if (is.data.frame(x)) {
rsi_integrity_check <- character(0)
@ -108,43 +106,38 @@ rsi_calc <- function(...,
# check integrity of columns: force rsi class
if (!is.rsi(x %>% pull(i))) {
rsi_integrity_check <- c(rsi_integrity_check, x %>% pull(i) %>% as.character())
x[, i] <- suppressWarnings(as.rsi(x[, i])) # warning will be given later
x[, i] <- suppressWarnings(x %>% pull(i) %>% as.rsi()) # warning will be given later
print_warning <- TRUE
}
x[, i] <- x %>% pull(i) %>% as.integer()
#x[, i] <- x %>% pull(i)
}
if (length(rsi_integrity_check) > 0) {
# this will give a warning for invalid results, of all input columns (so only 1 warning)
rsi_integrity_check <- as.rsi(rsi_integrity_check)
}
if (include_I == TRUE) {
x <- x %>% mutate_all(funs(ifelse(. == 2, type_trans, .)))
}
if (also_single_tested == TRUE) {
# THE CHANCE THAT AT LEAST ONE RESULT IS type
found <- x %>% filter_all(any_vars(. == type_trans)) %>% nrow()
# THE CHANCE THAT AT LEAST ONE RESULT IS type OR ALL ARE TESTED
total <- found + x %>% filter_all(all_vars(. %in% type_others)) %>% nrow()
# THE CHANCE THAT AT LEAST ONE RESULT IS ab_result
#numerator <- x %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
if (only_all_tested == TRUE) {
# THE NUMBER OF ISOLATES WHERE *ALL* ABx ARE S/I/R
x_filtered <- x %>% filter_all(all_vars(!is.na(.)))
numerator <- x_filtered %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
denominator <- x_filtered %>% nrow()
} else {
x <- apply(X = x,
MARGIN = 1,
FUN = min)
found <- sum(as.integer(x) == type_trans, na.rm = TRUE)
total <- length(x) - sum(is.na(x))
# THE NUMBER OF ISOLATES WHERE *ANY* ABx IS S/I/R
other_values <- base::setdiff(c(NA, levels(ab_result)), ab_result)
other_values_filter <- base::apply(x, 1, function(y) { base::all(y %in% other_values) & base::any(is.na(y)) })
numerator <- x %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
denominator <- x %>% filter(!other_values_filter) %>% nrow()
}
} else {
# x is not a data.frame
if (!is.rsi(x)) {
x <- as.rsi(x)
print_warning <- TRUE
}
x <- as.integer(x)
if (include_I == TRUE) {
x[x == 2] <- type_trans
}
found <- sum(x == type_trans, na.rm = TRUE)
total <- length(x) - sum(is.na(x))
numerator <- sum(x %in% ab_result, na.rm = TRUE)
denominator <- sum(x %in% levels(ab_result), na.rm = TRUE)
}
if (print_warning == TRUE) {
@ -153,20 +146,23 @@ rsi_calc <- function(...,
}
if (only_count == TRUE) {
return(found)
return(numerator)
}
if (total < minimum) {
warning("Introducing NA: only ", total, " results available for ", data_vars, " (minimum set to ", minimum, ").", call. = FALSE)
result <- NA
if (denominator < minimum) {
if (data_vars != "") {
data_vars <- paste(" for", data_vars)
}
warning("Introducing NA: only ", denominator, " results available", data_vars, " (minimum set to ", minimum, ").", call. = FALSE)
fraction <- NA
} else {
result <- found / total
fraction <- numerator / denominator
}
if (as_percent == TRUE) {
percent(result, force_zero = TRUE)
percent(fraction, force_zero = TRUE)
} else {
result
fraction
}
}

2
docs/LICENSE-text.html
View File

@ -78,7 +78,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9004</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
</span>
</div>

531
docs/articles/AMR.html
View File

@ -40,7 +40,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9003</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
</span>
</div>
@ -192,7 +192,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">23 June 2019</h4>
<h4 class="date">01 July 2019</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -201,7 +201,7 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 23 June 2019.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 01 July 2019.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -217,21 +217,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2019-06-23</td>
<td align="center">2019-07-01</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2019-06-23</td>
<td align="center">2019-07-01</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2019-06-23</td>
<td align="center">2019-07-01</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -327,69 +327,69 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2017-10-01</td>
<td align="center">O3</td>
<td align="center">2011-09-06</td>
<td align="center">Z5</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2011-03-09</td>
<td align="center">U5</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">2015-03-21</td>
<td align="center">E7</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2010-08-11</td>
<td align="center">X6</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2011-03-26</td>
<td align="center">N5</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<tr class="even">
<td align="center">2012-06-16</td>
<td align="center">E10</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2016-12-29</td>
<td align="center">J3</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2013-03-11</td>
<td align="center">O1</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">2010-04-09</td>
<td align="center">Q3</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2016-05-24</td>
<td align="center">V5</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2016-09-21</td>
<td align="center">Z8</td>
<td align="center">Hospital A</td>
<td align="center">Klebsiella pneumoniae</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
</tbody>
@ -411,8 +411,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,366 51.8% 10,366 51.8%
# 2 F 9,634 48.2% 20,000 100.0%</code></pre>
# 1 M 10,408 52.0% 10,408 52.0%
# 2 F 9,592 48.0% 20,000 100.0%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> function of the <code>dplyr</code> package makes this really easy:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span></a>
@ -442,14 +442,14 @@
<a class="sourceLine" id="cb14-18" title="18"><span class="co"># Pasteurella multocida (no new changes)</span></a>
<a class="sourceLine" id="cb14-19" title="19"><span class="co"># Staphylococcus (no new changes)</span></a>
<a class="sourceLine" id="cb14-20" title="20"><span class="co"># Streptococcus groups A, B, C, G (no new changes)</span></a>
<a class="sourceLine" id="cb14-21" title="21"><span class="co"># Streptococcus pneumoniae (1,453 new changes)</span></a>
<a class="sourceLine" id="cb14-21" title="21"><span class="co"># Streptococcus pneumoniae (1,443 new changes)</span></a>
<a class="sourceLine" id="cb14-22" title="22"><span class="co"># Viridans group streptococci (no new changes)</span></a>
<a class="sourceLine" id="cb14-23" title="23"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-24" title="24"><span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span></a>
<a class="sourceLine" id="cb14-25" title="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,298 new changes)</span></a>
<a class="sourceLine" id="cb14-25" title="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,332 new changes)</span></a>
<a class="sourceLine" id="cb14-26" title="26"><span class="co"># Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no new changes)</span></a>
<a class="sourceLine" id="cb14-27" title="27"><span class="co"># Table 03: Intrinsic resistance in other Gram-negative bacteria (no new changes)</span></a>
<a class="sourceLine" id="cb14-28" title="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,747 new changes)</span></a>
<a class="sourceLine" id="cb14-28" title="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,723 new changes)</span></a>
<a class="sourceLine" id="cb14-29" title="29"><span class="co"># Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no new changes)</span></a>
<a class="sourceLine" id="cb14-30" title="30"><span class="co"># Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no new changes)</span></a>
<a class="sourceLine" id="cb14-31" title="31"><span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no new changes)</span></a>
@ -457,24 +457,24 @@
<a class="sourceLine" id="cb14-33" title="33"><span class="co"># Table 13: Interpretive rules for quinolones (no new changes)</span></a>
<a class="sourceLine" id="cb14-34" title="34"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-35" title="35"><span class="co"># Other rules</span></a>
<a class="sourceLine" id="cb14-36" title="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,176 new changes)</span></a>
<a class="sourceLine" id="cb14-37" title="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (121 new changes)</span></a>
<a class="sourceLine" id="cb14-36" title="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,213 new changes)</span></a>
<a class="sourceLine" id="cb14-37" title="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (127 new changes)</span></a>
<a class="sourceLine" id="cb14-38" title="38"><span class="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no new changes)</span></a>
<a class="sourceLine" id="cb14-39" title="39"><span class="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no new changes)</span></a>
<a class="sourceLine" id="cb14-40" title="40"><span class="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no new changes)</span></a>
<a class="sourceLine" id="cb14-41" title="41"><span class="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no new changes)</span></a>
<a class="sourceLine" id="cb14-42" title="42"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-43" title="43"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-44" title="44"><span class="co"># EUCAST rules affected 6,468 out of 20,000 rows, making a total of 7,795 edits</span></a>
<a class="sourceLine" id="cb14-44" title="44"><span class="co"># EUCAST rules affected 6,513 out of 20,000 rows, making a total of 7,838 edits</span></a>
<a class="sourceLine" id="cb14-45" title="45"><span class="co"># =&gt; added 0 test results</span></a>
<a class="sourceLine" id="cb14-46" title="46"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-47" title="47"><span class="co"># =&gt; changed 7,795 test results</span></a>
<a class="sourceLine" id="cb14-48" title="48"><span class="co"># - 107 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-49" title="49"><span class="co"># - 4,725 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-50" title="50"><span class="co"># - 1,040 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-51" title="51"><span class="co"># - 329 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-52" title="52"><span class="co"># - 1,579 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-53" title="53"><span class="co"># - 15 test results changed from R to I</span></a>
<a class="sourceLine" id="cb14-47" title="47"><span class="co"># =&gt; changed 7,838 test results</span></a>
<a class="sourceLine" id="cb14-48" title="48"><span class="co"># - 115 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-49" title="49"><span class="co"># - 4,719 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-50" title="50"><span class="co"># - 1,077 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-51" title="51"><span class="co"># - 335 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-52" title="52"><span class="co"># - 1,573 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-53" title="53"><span class="co"># - 19 test results changed from R to I</span></a>
<a class="sourceLine" id="cb14-54" title="54"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-55" title="55"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-56" title="56"><span class="co"># Use verbose = TRUE to get a data.frame with all specified edits instead.</span></a></code></pre></div>
@ -502,8 +502,8 @@
<a class="sourceLine" id="cb16-3" title="3"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<a class="sourceLine" id="cb16-4" title="4"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a>
<a class="sourceLine" id="cb16-5" title="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb16-6" title="6"><span class="co"># =&gt; Found 5,644 first isolates (28.2% of total)</span></a></code></pre></div>
<p>So only 28.2% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<a class="sourceLine" id="cb16-6" title="6"><span class="co"># =&gt; Found 5,719 first isolates (28.6% of total)</span></a></code></pre></div>
<p>So only 28.6% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-2" title="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(first <span class="op">==</span><span class="st"> </span><span class="ot">TRUE</span>)</a></code></pre></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -513,7 +513,7 @@
<div id="first-weighted-isolates" class="section level2">
<h2 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h2>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient M3, sorted on date:</p>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient S7, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -529,74 +529,74 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-01-24</td>
<td align="center">M3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-17</td>
<td align="center">M3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>