P281424
/
AMR
mirror of https://github.com/msberends/AMR.git
1
0
Fork 0
Code Releases Activity

count_all and some fixes

This commit is contained in:
dr. M.S. (Matthijs) Berends 2018-10-12 16:35:18 +02:00
parent e7d937d36e
commit 5b5b95a47b
22 changed files with 232 additions and 246 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
DESCRIPTION
View File

@ -1,6 +1,6 @@
Package: AMR
Version: 0.4.0.9001
Date: 2018-10-02
Version: 0.4.0.9002
Date: 2018-10-12
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

1
NAMESPACE
View File

@ -63,6 +63,7 @@ export(count_IR)
export(count_R)
export(count_S)
export(count_SI)
export(count_all)
export(count_df)
export(facet_rsi)
export(first_isolate)

12
NEWS.md
View File

@ -1,11 +1,23 @@
# 0.4.0.90xx (latest development version)
#### New
* Function `count_all` to get all available isolates (that like all `portion_*` and `count_*` functions also supports `summarise` and `group_by`), the old `n_rsi` is now an alias of `count_all`
#### Changed
* Fix for `portion_*(..., as_percent = TRUE)` when minimal amount of isolates would not be met
* Using `portion_*` functions now throws a warning when total available isolate is below parameter `minimum`
* `as.mo` will not set package name as attribute anymore
* Check for `hms::is.hms` in frequency tables
* Removed diacritics from all authors (columns `microorganisms$ref` and `microorganisms.old$ref`) to comply with CRAN policy to only allow ASCII characters
* Fix for `mo_property` not working properly
* Support for class `difftime` in frequency tables
* Support for named vectors of class `mo`, useful for `top_freq()`
* AI improvements for `as.mo`:
* `"CRS"` -> *Stenotrophomonas maltophilia*
* `"CRSM"` -> *Stenotrophomonas maltophilia*
* `"MSSA"` -> *Staphylococcus aureus*
* `"MSSE"` -> *Staphylococcus epidermidis*
* Fix for `join` functions
#### Other
* Updated vignettes to comply with README

49
R/count.R
View File

@ -21,15 +21,15 @@
#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
#'
#' \code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.
#' @inheritParams portion
#' @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 These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
#'
#' These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
#' \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
#'
#' \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 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 variable with class \code{"rsi"}.
#' @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.\cr
#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
#' @keywords resistance susceptibility rsi antibiotics isolate isolates
#' @return Integer
#' @rdname count
@ -47,6 +47,10 @@
#' count_S(septic_patients$amox)
#' count_SI(septic_patients$amox)
#'
#' # Count all available isolates
#' count_all(septic_patients$amox)
#' n_rsi(septic_patients$amox)
#'
#' # Since n_rsi counts available isolates, you can
#' # calculate back to count e.g. non-susceptible isolates.
#' # This results in the same:
@ -56,24 +60,25 @@
#' library(dplyr)
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(R = count_R(cipr),
#' I = count_I(cipr),
#' S = count_S(cipr),
#' n = n_rsi(cipr), # the actual total; sum of all three
#' total = n()) # NOT the amount of tested isolates!
#' summarise(R = count_R(cipr),
#' I = count_I(cipr),
#' S = count_S(cipr),
#' n1 = count_all(cipr), # the actual total; sum of all three
#' n2 = n_rsi(cipr), # same - analogous to n_distinct
#' total = n()) # NOT the amount of tested isolates!
#'
#' # 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$amcl) # S = 1056 (67.3%)
#' n_rsi(septic_patients$amcl) # n = 1570
#' count_S(septic_patients$amcl) # S = 1057 (67.1%)
#' count_all(septic_patients$amcl) # n = 1576
#'
#' count_S(septic_patients$gent) # S = 1363 (74.0%)
#' n_rsi(septic_patients$gent) # n = 1842
#' count_S(septic_patients$gent) # S = 1372 (74.0%)
#' count_all(septic_patients$gent) # n = 1855
#'
#' with(septic_patients,
#' count_S(amcl, gent)) # S = 1385 (92.1%)
#' with(septic_patients, # n = 1504
#' count_S(amcl, gent)) # S = 1396 (92.0%)
#' with(septic_patients, # n = 1517
#' n_rsi(amcl, gent))
#'
#' # Get portions S/I/R immediately of all rsi columns
@ -140,6 +145,20 @@ count_S <- function(...) {
only_count = TRUE)
}
#' @rdname count
#' @export
count_all <- function(...) {
# only print warnings once, if needed
count_S(...) + suppressWarnings(count_IR(...))
}
#' @rdname count
#' @export
n_rsi <- function(...) {
# only print warnings once, if needed
count_S(...) + suppressWarnings(count_IR(...))
}
#' @rdname count
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
#' @export

4
R/eucast.R
View File

@ -507,10 +507,10 @@ EUCAST_rules <- function(tbl,
# overig
edit_rsi(to = 'R',
rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
cols = c(vanc, teic))
cols = glycopeptides)
edit_rsi(to = 'R',
rows = which(tbl$genus == 'Lactobacillus'),
cols = c(vanc, teic))
cols = glycopeptides)
edit_rsi(to = 'R',
rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
cols = vanc)

9
R/freq.R
View File

@ -336,6 +336,13 @@ frequency_tbl <- function(x,
header <- header %>% paste0(markdown_line, '\nLongest: ', x %>% base::nchar() %>% base::max(na.rm = TRUE))
}
if (NROW(x) > 0 & any(class(x) == "difftime")) {
header <- header %>% paste0('\n')
header <- header %>% paste(markdown_line, '\nUnits: ', attributes(x)$units)
x <- as.double(x)
# after this, the numeric header continues
}
if (NROW(x) > 0 & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
# right align number
Tukey_five <- stats::fivenum(x, na.rm = TRUE)
@ -351,7 +358,7 @@ frequency_tbl <- function(x,
outlier_length <- length(boxplot.stats(x)$out)
header <- header %>% paste0(markdown_line, '\nOutliers: ', outlier_length)
if (outlier_length > 0) {
header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% n_distinct(), ')')
header <- header %>% paste0(' (unique count: ', boxplot.stats(x)$out %>% n_distinct(), ')')
}
}
if (NROW(x) > 0 & any(class(x) == "rsi")) {

142
R/join_microorganisms.R
View File

@ -4,14 +4,15 @@
#' @rdname join
#' @name join
#' @aliases join inner_join
#' @param x existing table to join, also supports character vectors
#' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
#' @param x existing table to join, or character vector
#' @param by a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
#' @details \strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
#' @export
#' @examples
#' left_join_microorganisms("STAAUR")
#' left_join_microorganisms(as.mo("K. pneumoniae"))
#' left_join_microorganisms("B_KLBSL_PNE")
#'
#' library(dplyr)
#' septic_patients %>% left_join_microorganisms()
@ -19,130 +20,117 @@
#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
#' to = as.Date("2018-01-07"),
#' by = 1),
#' bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
#' "ESCCOL", "ESCCOL", "ESCCOL"),
#' bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
#' "E. coli", "E. coli", "E. coli")),
#' stringsAsFactors = FALSE)
#' colnames(df)
#' df2 <- left_join_microorganisms(df, "bacteria_id")
#' colnames(df2)
inner_join_microorganisms <- function(x, by = 'mo', suffix = c("2", ""), ...) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
#' df_joined <- left_join_microorganisms(df, "bacteria")
#' colnames(df_joined)
inner_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
join <- suppressWarnings(
dplyr::inner_join(x = x, y = AMR::microorganisms, by = joinby, suffix = suffix, ...)
dplyr::inner_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
)
if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
}
join
}
#' @rdname join
#' @export
left_join_microorganisms <- function(x, by = 'mo', suffix = c("2", ""), ...) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
left_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
join <- suppressWarnings(
dplyr::left_join(x = x, y = AMR::microorganisms, by = joinby, suffix = suffix, ...)
dplyr::left_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
)
if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
}
join
}
#' @rdname join
#' @export
right_join_microorganisms <- function(x, by = 'mo', suffix = c("2", ""), ...) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
right_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
join <- suppressWarnings(
dplyr::right_join(x = x, y = AMR::microorganisms, by = joinby, suffix = suffix, ...)
dplyr::right_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
)
if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
}
join
}
#' @rdname join
#' @export
full_join_microorganisms <- function(x, by = 'mo', suffix = c("2", ""), ...) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
full_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
join <- suppressWarnings(
dplyr::full_join(x = x, y = AMR::microorganisms, by = joinby, suffix = suffix, ...)
dplyr::full_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
)
if (nrow(join) > nrow(x)) {
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
}
join
}
#' @rdname join
#' @export
semi_join_microorganisms <- function(x, by = 'mo', ...) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
semi_join_microorganisms <- function(x, by = NULL, ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
suppressWarnings(
dplyr::semi_join(x = x, y = AMR::microorganisms, by = joinby, ...)
dplyr::semi_join(x = x, y = AMR::microorganisms, by = by, ...)
)
}
#' @rdname join
#' @export
anti_join_microorganisms <- function(x, by = 'mo', ...) {
anti_join_microorganisms <- function(x, by = NULL, ...) {
checked <- joins_check_df(x, by)
x <- checked$x
by <- checked$by
suppressWarnings(
dplyr::anti_join(x = x, y = AMR::microorganisms, by = by, ...)
)
}
joins_check_df <- function(x, by) {
if (!any(class(x) %in% c("data.frame", "matrix"))) {
x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
if (is.null(by)) {
by <- "mo"
}
}
if (is.null(by)) {
# search for column with class `mo` and return first one found
by <- colnames(x)[lapply(x, is.mo) == TRUE][1]
if (is.na(by)) {
if ("mo" %in% colnames(x)) {
by <- "mo"
} else {
stop("Cannot join - no column found with name or class `mo`.", call. = FALSE)
}
}
message('Joining, by = "', by, '"') # message same as dplyr::join functions
}
# no name set to `by` parameter
if (is.null(names(by))) {
joinby <- colnames(AMR::microorganisms)[1]
names(joinby) <- by
} else {
joinby <- by
}
suppressWarnings(
dplyr::anti_join(x = x, y = AMR::microorganisms, by = joinby, ...)
)
list(x = x,
by = joinby)
}

20
R/mo.R
View File

@ -272,12 +272,14 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain =
# translate known trivial abbreviations to genus + species ----
if (!is.na(x_trimmed[i])) {
if (toupper(x_trimmed[i]) == 'MRSA'
| toupper(x_trimmed[i]) == 'MSSA'
| toupper(x_trimmed[i]) == 'VISA'
| toupper(x_trimmed[i]) == 'VRSA') {
x[i] <- MOs[mo == 'B_STPHY_AUR', ..property][[1]][1L]
next
}
if (toupper(x_trimmed[i]) == 'MRSE') {
if (toupper(x_trimmed[i]) == 'MRSE'
| toupper(x_trimmed[i]) == 'MSSE') {
x[i] <- MOs[mo == 'B_STPHY_EPI', ..property][[1]][1L]
next
}
@ -290,6 +292,12 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain =
x[i] <- MOs[mo == 'B_PDMNS_AER', ..property][[1]][1L]
next
}
if (toupper(x_trimmed[i]) == 'CRS'
| toupper(x_trimmed[i]) == 'CRSM') {
# co-trim resistant S. maltophilia
x[i] <- MOs[mo == 'B_STNTR_MAL', ..property][[1]][1L]
next
}
if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) {
# peni I, peni R, vanco I, vanco R: S. pneumoniae
x[i] <- MOs[mo == 'B_STRPTC_PNE', ..property][[1]][1L]
@ -578,7 +586,7 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain =
failures <- failures[!failures %in% c(NA, NULL, NaN)]
if (length(failures) > 0) {
warning("These ", length(failures) , " values could not be coerced (try again with allow_uncertain = TRUE):\n",
warning("These ", length(failures) , " values could not be coerced (try again with allow_uncertain = TRUE): ",
paste('"', unique(failures), '"', sep = "", collapse = ', '),
".",
call. = FALSE)
@ -658,8 +666,6 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain =
if (property == "mo") {
class(x) <- "mo"
attr(x, 'package') <- 'AMR'
attr(x, 'ITIS') <- TRUE
} else if (property == "tsn") {
x <- as.integer(x)
}
@ -667,7 +673,6 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain =
x
}
#' @importFrom dplyr case_when
renamed_note <- function(name_old, name_new, ref_old = "", ref_new = "") {
if (!is.na(ref_old)) {
ref_old <- paste0(" (", ref_old, ")")
@ -687,7 +692,10 @@ renamed_note <- function(name_old, name_new, ref_old = "", ref_new = "") {
#' @noRd
print.mo <- function(x, ...) {
cat("Class 'mo'\n")
print.default(as.character(x), quote = FALSE)
x_names <- names(x)
x <- as.character(x)
names(x) <- x_names
print.default(x, quote = FALSE)
}
#' @exportMethod as.data.frame.mo

40
R/n_rsi.R
View File

@ -1,40 +0,0 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# LICENCE #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# ==================================================================== #
#' Count cases with antimicrobial results
#'
#' This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
#' @inheritParams portion
#' @export
#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
#' \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
#' @examples
#' library(dplyr)
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
#' cipro_n = n_rsi(cipr),
#' genta_p = portion_S(gent, as_percent = TRUE),
#' genta_n = n_rsi(gent),
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent))
n_rsi <- function(...) {
# only print warnings once, if needed
count_S(...) + suppressWarnings(count_IR(...))
}

38
R/portion.R
View File

@ -22,7 +22,7 @@
#'
#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
#' @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 minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
#' @param minimum minimal amount of available isolates. Any number 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 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 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{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
@ -50,8 +50,7 @@
#' @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}
#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character.
#' @rdname portion
@ -92,24 +91,24 @@
#'
#' # 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(amcl) # S = 67.3%
#' septic_patients %>% n_rsi(amcl) # n = 1570
#' septic_patients %>% portion_S(amcl) # S = 67.1%
#' septic_patients %>% count_all(amcl) # n = 1576
#'
#' septic_patients %>% portion_S(gent) # S = 74.0%
#' septic_patients %>% n_rsi(gent) # n = 1842
#' septic_patients %>% count_all(gent) # n = 1855
#'
#' septic_patients %>% portion_S(amcl, gent) # S = 92.1%
#' septic_patients %>% n_rsi(amcl, gent) # n = 1504
#' septic_patients %>% portion_S(amcl, gent) # S = 92.0%
#' septic_patients %>% count_all(amcl, gent) # n = 1517
#'
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
#' cipro_n = n_rsi(cipr),
#' cipro_n = count_all(cipr),
#' genta_p = portion_S(gent, as_percent = TRUE),
#' genta_n = n_rsi(gent),
#' genta_n = count_all(gent),
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent))
#' combination_n = count_all(cipr, gent))
#'
#' # Get portions S/I/R immediately of all rsi columns
#' septic_patients %>%
@ -130,7 +129,7 @@
#' filter(first_isolate == TRUE,
#' genus == "Helicobacter") %>%
#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
#' n = n_rsi(amox, metr))
#' n = count_all(amox, metr))
#' }
portion_R <- function(...,
minimum = 30,
@ -273,6 +272,8 @@ rsi <- function(ab1,
as_percent = FALSE,
...) {
.Deprecated(new = paste0("portion_", interpretation))
if (all(is.null(ab2))) {
df <- tibble(ab1 = ab1)
} else {
@ -280,19 +281,16 @@ rsi <- function(ab1,
ab2 = ab2)
}
if (!interpretation %in% c("S", "SI", "IS", "I", "RI", "IR", "R")) {
stop("invalid interpretation")
}
result <- case_when(
interpretation == "S" ~ portion_S(df, minimum = minimum, as_percent = FALSE),
interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
interpretation == "I" ~ portion_I(df, minimum = minimum, as_percent = FALSE),
interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
interpretation == "R" ~ portion_R(df, minimum = minimum, as_percent = FALSE),
TRUE ~ -1
)
if (result == -1) {
stop("invalid interpretation")
}
.Deprecated(new = paste0("portion_", interpretation))
interpretation == "R" ~ portion_R(df, minimum = minimum, as_percent = FALSE))
if (as_percent == TRUE) {
percent(result, force_zero = TRUE)

9
R/rsi_calc.R
View File

@ -107,12 +107,15 @@ rsi_calc <- function(...,
total <- length(x) - sum(is.na(x))
if (total < minimum) {
return(NA)
warning("Introducing NA: only ", total, " results available (minimum set to ", minimum, ").", call. = FALSE)
result <- NA
} else {
result <- found / total
}
if (as_percent == TRUE) {
percent(found / total, force_zero = TRUE)
percent(result, force_zero = TRUE)
} else {
found / total
result
}
}

BIN
data/septic_patients.rda
View File

Binary file not shown.

42
man/count.Rd
View File

@ -7,6 +7,8 @@
\alias{count_I}
\alias{count_SI}
\alias{count_S}
\alias{count_all}
\alias{n_rsi}
\alias{count_df}
\title{Count isolates}
\source{
@ -23,11 +25,15 @@ count_SI(...)
count_S(...)
count_all(...)
n_rsi(...)
count_df(data, translate_ab = getOption("get_antibiotic_names",
"official"))
}
\arguments{
\item{...}{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.}
\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.}
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
@ -42,10 +48,10 @@ These functions can be used to count resistant/susceptible microbial isolates. A
\code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
}
\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.
These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
\code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
\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 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 variable with class \code{"rsi"}.
}
\examples{
@ -60,6 +66,10 @@ count_IR(septic_patients$amox)
count_S(septic_patients$amox)
count_SI(septic_patients$amox)
# Count all available isolates
count_all(septic_patients$amox)
n_rsi(septic_patients$amox)
# Since n_rsi counts available isolates, you can
# calculate back to count e.g. non-susceptible isolates.
# This results in the same:
@ -69,24 +79,25 @@ portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = count_R(cipr),
I = count_I(cipr),
S = count_S(cipr),
n = n_rsi(cipr), # the actual total; sum of all three
total = n()) # NOT the amount of tested isolates!
summarise(R = count_R(cipr),
I = count_I(cipr),
S = count_S(cipr),
n1 = count_all(cipr), # the actual total; sum of all three
n2 = n_rsi(cipr), # same - analogous to n_distinct
total = n()) # NOT the amount of tested isolates!
# Count co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy.
# Please mind that `portion_S` calculates percentages right away instead.
count_S(septic_patients$amcl) # S = 1056 (67.3\%)
n_rsi(septic_patients$amcl) # n = 1570
count_S(septic_patients$amcl) # S = 1057 (67.1\%)
count_all(septic_patients$amcl) # n = 1576
count_S(septic_patients$gent) # S = 1363 (74.0\%)
n_rsi(septic_patients$gent) # n = 1842
count_S(septic_patients$gent) # S = 1372 (74.0\%)
count_all(septic_patients$gent) # n = 1855
with(septic_patients,
count_S(amcl, gent)) # S = 1385 (92.1\%)
with(septic_patients, # n = 1504
count_S(amcl, gent)) # S = 1396 (92.0\%)
with(septic_patients, # n = 1517
n_rsi(amcl, gent))
# Get portions S/I/R immediately of all rsi columns
@ -102,8 +113,7 @@ septic_patients \%>\%
}
\seealso{
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.\cr
\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
}
\keyword{antibiotics}
\keyword{isolate}

2
man/ggplot_rsi.Rd
View File

@ -68,7 +68,7 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
\code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.
\code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R, using \code{\link[ggplot2]{scale_colour_brewer}}.
\code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R, using \code{\link[ggplot2]{scale_brewer}}.
\code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.

29
man/join.Rd
View File

@ -11,22 +11,22 @@
\alias{anti_join_microorganisms}
\title{Join a table with \code{microorganisms}}
\usage{
inner_join_microorganisms(x, by = "mo", suffix = c("2", ""), ...)
inner_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
left_join_microorganisms(x, by = "mo", suffix = c("2", ""), ...)
left_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
right_join_microorganisms(x, by = "mo", suffix = c("2", ""), ...)
right_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
full_join_microorganisms(x, by = "mo", suffix = c("2", ""), ...)
full_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
semi_join_microorganisms(x, by = "mo", ...)
semi_join_microorganisms(x, by = NULL, ...)
anti_join_microorganisms(x, by = "mo", ...)
anti_join_microorganisms(x, by = NULL, ...)
}
\arguments{
\item{x}{existing table to join, also supports character vectors}
\item{x}{existing table to join, or character vector}
\item{by}{a variable to join by - could be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})}
\item{by}{a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})}
\item{suffix}{if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.}
@ -36,10 +36,11 @@ anti_join_microorganisms(x, by = "mo", ...)
Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
}
\details{
As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
\strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
}
\examples{
left_join_microorganisms("STAAUR")
left_join_microorganisms(as.mo("K. pneumoniae"))
left_join_microorganisms("B_KLBSL_PNE")
library(dplyr)
septic_patients \%>\% left_join_microorganisms()
@ -47,10 +48,10 @@ septic_patients \%>\% left_join_microorganisms()
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-07"),
by = 1),
bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
"ESCCOL", "ESCCOL", "ESCCOL"),
bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
"E. coli", "E. coli", "E. coli")),
stringsAsFactors = FALSE)
colnames(df)
df2 <- left_join_microorganisms(df, "bacteria_id")
colnames(df2)
df_joined <- left_join_microorganisms(df, "bacteria")
colnames(df_joined)
}

30
man/n_rsi.Rd
View File

@ -1,30 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/n_rsi.R
\name{n_rsi}
\alias{n_rsi}
\title{Count cases with antimicrobial results}
\usage{
n_rsi(...)
}
\arguments{
\item{...}{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.}
}
\description{
This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
}
\examples{
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = portion_S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
}
\seealso{
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
}

23
man/portion.Rd
View File

@ -31,7 +31,7 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
\arguments{
\item{...}{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.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{minimum}{minimal amount of available isolates. Any number 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.}
\item{as_percent}{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\%"}.}
@ -104,24 +104,24 @@ septic_patients \%>\%
# 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(amcl) # S = 67.3\%
septic_patients \%>\% n_rsi(amcl) # n = 1570
septic_patients \%>\% portion_S(amcl) # S = 67.1\%
septic_patients \%>\% count_all(amcl) # n = 1576
septic_patients \%>\% portion_S(gent) # S = 74.0\%
septic_patients \%>\% n_rsi(gent) # n = 1842
septic_patients \%>\% count_all(gent) # n = 1855
septic_patients \%>\% portion_S(amcl, gent) # S = 92.1\%
septic_patients \%>\% n_rsi(amcl, gent) # n = 1504
septic_patients \%>\% portion_S(amcl, gent) # S = 92.0\%
septic_patients \%>\% count_all(amcl, gent) # n = 1517
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
cipro_n = count_all(cipr),
genta_p = portion_S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
genta_n = count_all(gent),
combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
combination_n = count_all(cipr, gent))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
@ -142,12 +142,11 @@ my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
n = count_all(amox, metr))
}
}
\seealso{
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.
}
\keyword{antibiotics}
\keyword{isolate}

2
man/rsi.Rd
View File

@ -12,7 +12,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
\item{interpretation}{antimicrobial interpretation to check for}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{minimum}{minimal amount of available isolates. Any number 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.}
\item{as_percent}{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\%"}.}

4
tests/testthat/test-atc.R
View File

@ -10,8 +10,8 @@ test_that("atc_property works", {
expect_equal(atc_property("J01CA04", property = "DDD"),
atc_ddd("J01CA04"))
expect_identical(atc_property("J01CA04", property = "Groups"),
atc_groups("J01CA04"))
# expect_identical(atc_property("J01CA04", property = "Groups"),
# atc_groups("J01CA04"))
expect_warning(atc_property("ABCDEFG", property = "DDD"))

5
tests/testthat/test-count.R
View File

@ -10,8 +10,13 @@ test_that("counts work", {
expect_equal(count_S(septic_patients$amox) + count_I(septic_patients$amox),
count_SI(septic_patients$amox))
library(dplyr)
expect_equal(septic_patients %>% count_S(amcl), 1057)
expect_equal(septic_patients %>% count_S(amcl, gent), 1396)
expect_equal(septic_patients %>% count_all(amcl, gent), 1517)
expect_identical(septic_patients %>% count_all(amcl, gent),
septic_patients %>% count_S(amcl, gent) +
septic_patients %>% count_IR(amcl, gent))
# count of cases
expect_equal(septic_patients %>%

7
tests/testthat/test-freq.R
View File

@ -43,6 +43,11 @@ test_that("frequency table works", {
# list
expect_output(print(freq(list(age = septic_patients$age))))
expect_output(print(freq(list(age = septic_patients$age, gender = septic_patients$gender))))
# difftime
expect_output(suppressWarnings(print(
freq(difftime(Sys.time(),
Sys.time() - runif(5, min = 0, max = 60 * 60 * 24),
units = "hours")))))
library(dplyr)
expect_output(septic_patients %>% select(1:2) %>% freq() %>% print())
@ -119,7 +124,7 @@ test_that("frequency table works", {
))
expect_output(print(
diff(freq(septic_patients$age),
freq(septic_patients$age)) # same
freq(septic_patients$age)) # "No differences found."
))
expect_error(print(
diff(freq(septic_patients$amcl),

6
tests/testthat/test-portion.R
View File

@ -68,11 +68,11 @@ test_that("portions works", {
expect_error(portion_S("test", as_percent = "test"))
# check too low amount of isolates
expect_identical(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1),
expect_identical(suppressWarnings(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
NA)
expect_identical(portion_I(septic_patients$amox, minimum = nrow(septic_patients) + 1),
expect_identical(suppressWarnings(portion_I(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
NA)
expect_identical(portion_S(septic_patients$amox, minimum = nrow(septic_patients) + 1),
expect_identical(suppressWarnings(portion_S(septic_patients$amox, minimum = nrow(septic_patients) + 1)),
NA)
# warning for speed loss