diff --git a/DESCRIPTION b/DESCRIPTION
index 109c4b93..b9823397 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: AMR
-Version: 0.3.0.9001
-Date: 2018-08-21
+Version: 0.3.0.9002
+Date: 2018-08-23
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(
@@ -52,8 +52,8 @@ Imports:
xml2 (>= 1.0.0),
knitr (>= 1.0.0),
readr,
- rvest (>= 0.3.2),
- tibble
+ rlang,
+ rvest (>= 0.3.2)
Suggests:
testthat (>= 1.0.2),
covr (>= 3.0.1),
diff --git a/NAMESPACE b/NAMESPACE
index 749b2bd9..89462f30 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -127,6 +127,7 @@ importFrom(dplyr,arrange)
importFrom(dplyr,arrange_at)
importFrom(dplyr,as_tibble)
importFrom(dplyr,between)
+importFrom(dplyr,bind_cols)
importFrom(dplyr,bind_rows)
importFrom(dplyr,case_when)
importFrom(dplyr,desc)
@@ -171,7 +172,6 @@ importFrom(stats,mad)
importFrom(stats,pchisq)
importFrom(stats,predict)
importFrom(stats,sd)
-importFrom(tibble,tibble)
importFrom(utils,View)
importFrom(utils,browseVignettes)
importFrom(utils,installed.packages)
diff --git a/NEWS.md b/NEWS.md
index a8f4e1e4..7e5db04c 100755
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,13 +1,19 @@
# 0.3.0.90xx (latest development version)
#### New
-* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptibile isolates
+* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
#### Changed
* Added parameters `minimum` and `as_percent` to `portion_df`
+* Support for quasiquotation in the functions series `count_*` and `portions_*`, and `n_rsi`. This allow to check for more than 2 vectors or columns.
+ * `septic_patients %>% select(amox, cipr) %>% count_R()`
+ * `septic_patients %>% portion_S(amcl)`
+ * `septic_patients %>% portion_S(amcl, gent)`
+ * `septic_patients %>% portion_S(amcl, gent, pita)`
* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
-* Fix for `ggplot_rsi` when the `ggplot2` was not loaded
+* Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
+* Added parameter `alpha` to `ggplot_rsi` and `geom_rsi`
# 0.3.0 (latest stable version)
**Published on CRAN: 2018-08-14**
diff --git a/R/count.R b/R/count.R
index d78cadf8..50c39b3d 100644
--- a/R/count.R
+++ b/R/count.R
@@ -18,7 +18,7 @@
#' Count isolates
#'
-#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+#' @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
#' @inheritParams portion
@@ -87,11 +87,9 @@
#' group_by(hospital_id) %>%
#' count_df(translate = FALSE)
#'
-count_R <- function(ab1,
- ab2 = NULL) {
- rsi_calc(type = "R",
- ab1 = ab1,
- ab2 = ab2,
+count_R <- function(...) {
+ rsi_calc(...,
+ type = "R",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
@@ -100,11 +98,9 @@ count_R <- function(ab1,
#' @rdname count
#' @export
-count_IR <- function(ab1,
- ab2 = NULL) {
- rsi_calc(type = "R",
- ab1 = ab1,
- ab2 = ab2,
+count_IR <- function(...) {
+ rsi_calc(...,
+ type = "R",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
@@ -113,10 +109,9 @@ count_IR <- function(ab1,
#' @rdname count
#' @export
-count_I <- function(ab1) {
- rsi_calc(type = "I",
- ab1 = ab1,
- ab2 = NULL,
+count_I <- function(...) {
+ rsi_calc(...,
+ type = "I",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
@@ -125,11 +120,9 @@ count_I <- function(ab1) {
#' @rdname count
#' @export
-count_SI <- function(ab1,
- ab2 = NULL) {
- rsi_calc(type = "S",
- ab1 = ab1,
- ab2 = ab2,
+count_SI <- function(...) {
+ rsi_calc(...,
+ type = "S",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
@@ -138,11 +131,9 @@ count_SI <- function(ab1,
#' @rdname count
#' @export
-count_S <- function(ab1,
- ab2 = NULL) {
- rsi_calc(type = "S",
- ab1 = ab1,
- ab2 = ab2,
+count_S <- function(...) {
+ rsi_calc(...,
+ type = "S",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
diff --git a/R/freq.R b/R/freq.R
index 9346a233..e1dc7c88 100755
--- a/R/freq.R
+++ b/R/freq.R
@@ -55,9 +55,8 @@
#' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
#' @importFrom stats fivenum sd mad
#' @importFrom grDevices boxplot.stats
-#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct
+#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct tibble
#' @importFrom utils browseVignettes installed.packages
-#' @importFrom tibble tibble
#' @keywords summary summarise frequency freq
#' @rdname freq
#' @name freq
@@ -378,12 +377,12 @@ frequency_tbl <- function(x,
column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent', 'factor_level')
if (any(class(x) == 'factor')) {
- df <- tibble::tibble(item = x,
+ df <- tibble(item = x,
fctlvl = x %>% as.integer()) %>%
group_by(item, fctlvl)
column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
} else {
- df <- tibble::tibble(item = x) %>%
+ df <- tibble(item = x) %>%
group_by(item)
# strip factor lvl from col names
column_names <- column_names[1:length(column_names) - 1]
diff --git a/R/ggplot_rsi.R b/R/ggplot_rsi.R
index fea5588b..f4de1267 100644
--- a/R/ggplot_rsi.R
+++ b/R/ggplot_rsi.R
@@ -25,6 +25,7 @@
#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
+#' @param alpha opacity of the fill colours
#' @param fun function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}
#' @param ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
@@ -113,6 +114,7 @@ ggplot_rsi <- function(data,
fill = "Interpretation",
facet = NULL,
translate_ab = "official",
+ alpha = 1,
fun = portion_df,
...) {
@@ -126,7 +128,7 @@ ggplot_rsi <- function(data,
}
p <- ggplot2::ggplot(data = data) +
- geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun) +
+ geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, alpha = alpha, fun = fun) +
theme_rsi()
if (fill == "Interpretation") {
@@ -151,6 +153,7 @@ geom_rsi <- function(position = NULL,
x = c("Antibiotic", "Interpretation"),
fill = "Interpretation",
translate_ab = "official",
+ alpha = 1,
fun = portion_df) {
fun_name <- deparse(substitute(fun))
@@ -180,7 +183,7 @@ geom_rsi <- function(position = NULL,
ggplot2::layer(geom = "bar", stat = "identity", position = position,
mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
- data = fun, params = list())
+ data = fun, params = list(alpha = alpha))
}
diff --git a/R/n_rsi.R b/R/n_rsi.R
index cac652cd..eeffcdfa 100644
--- a/R/n_rsi.R
+++ b/R/n_rsi.R
@@ -18,10 +18,11 @@
#' Count cases with antimicrobial results
#'
-#' This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
-#' @param ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
+#' 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 The \code{\link{portion}} functions to calculate resistance and susceptibility.
+#' @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)
#'
@@ -33,22 +34,7 @@
#' genta_n = n_rsi(gent),
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent))
-n_rsi <- function(ab1, ab2 = NULL) {
- if (NCOL(ab1) > 1) {
- stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
- }
- if (!is.rsi(ab1)) {
- ab1 <- as.rsi(ab1)
- }
- if (!is.null(ab2)) {
- if (NCOL(ab2) > 1) {
- stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
- }
- if (!is.rsi(ab2)) {
- ab2 <- as.rsi(ab2)
- }
- sum(!is.na(ab1) & !is.na(ab2))
- } else {
- sum(!is.na(ab1))
- }
+n_rsi <- function(...) {
+ # only print warnings once, if needed
+ count_S(...) + suppressWarnings(count_IR(...))
}
diff --git a/R/portion.R b/R/portion.R
index c4636b6e..17d9a7d8 100755
--- a/R/portion.R
+++ b/R/portion.R
@@ -18,11 +18,10 @@
#' Calculate resistance of isolates
#'
-#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). 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{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 ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
-#' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
+#' @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 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}})
@@ -43,8 +42,10 @@
#' For two antibiotics:
#' \out{
}\figure{combi_therapy_2.png}\out{
}
#' \cr
-#' Theoretically for three antibiotics:
+#' For three antibiotics:
#' \out{}\figure{combi_therapy_3.png}\out{
}
+#' \cr
+#' And so on.
#' }
#' @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/}.
#'
@@ -68,11 +69,14 @@
#' portion_S(septic_patients$amox)
#' portion_SI(septic_patients$amox)
#'
-#' # Since n_rsi counts available isolates (and is used as denominator),
-#' # you can calculate back to count e.g. non-susceptible isolates:
-#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
-#'
+
+#' # Do the above with pipes:
#' library(dplyr)
+#' septic_patients %>% portion_R(amox)
+#' septic_patients %>% portion_IR(amox)
+#' septic_patients %>% portion_S(amox)
+#' septic_patients %>% portion_SI(amox)
+#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(p = portion_S(cipr),
@@ -88,16 +92,15 @@
#'
#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy:
-#' portion_S(septic_patients$amcl) # S = 67.3%
-#' n_rsi(septic_patients$amcl) # n = 1570
+#' septic_patients %>% portion_S(amcl) # S = 67.3%
+#' septic_patients %>% n_rsi(amcl) # n = 1570
#'
-#' portion_S(septic_patients$gent) # S = 74.0%
-#' n_rsi(septic_patients$gent) # n = 1842
+#' septic_patients %>% portion_S(gent) # S = 74.0%
+#' septic_patients %>% n_rsi(gent) # n = 1842
+#'
+#' septic_patients %>% portion_S(amcl, gent) # S = 92.1%
+#' septic_patients %>% n_rsi(amcl, gent) # n = 1504
#'
-#' with(septic_patients,
-#' portion_S(amcl, gent)) # S = 92.1%
-#' with(septic_patients, # n = 1504
-#' n_rsi(amcl, gent))
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
@@ -129,13 +132,11 @@
#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
#' n = n_rsi(amox, metr))
#' }
-portion_R <- function(ab1,
- ab2 = NULL,
+portion_R <- function(...,
minimum = 30,
as_percent = FALSE) {
- rsi_calc(type = "R",
- ab1 = ab1,
- ab2 = ab2,
+ rsi_calc(...,
+ type = "R",
include_I = FALSE,
minimum = minimum,
as_percent = as_percent,
@@ -144,13 +145,11 @@ portion_R <- function(ab1,
#' @rdname portion
#' @export
-portion_IR <- function(ab1,
- ab2 = NULL,
+portion_IR <- function(...,
minimum = 30,
as_percent = FALSE) {
- rsi_calc(type = "R",
- ab1 = ab1,
- ab2 = ab2,
+ rsi_calc(...,
+ type = "R",
include_I = TRUE,
minimum = minimum,
as_percent = as_percent,
@@ -159,12 +158,11 @@ portion_IR <- function(ab1,
#' @rdname portion
#' @export
-portion_I <- function(ab1,
+portion_I <- function(...,
minimum = 30,
as_percent = FALSE) {
- rsi_calc(type = "I",
- ab1 = ab1,
- ab2 = NULL,
+ rsi_calc(...,
+ type = "I",
include_I = FALSE,
minimum = minimum,
as_percent = as_percent,
@@ -173,13 +171,11 @@ portion_I <- function(ab1,
#' @rdname portion
#' @export
-portion_SI <- function(ab1,
- ab2 = NULL,
+portion_SI <- function(...,
minimum = 30,
as_percent = FALSE) {
- rsi_calc(type = "S",
- ab1 = ab1,
- ab2 = ab2,
+ rsi_calc(...,
+ type = "S",
include_I = TRUE,
minimum = minimum,
as_percent = as_percent,
@@ -188,13 +184,11 @@ portion_SI <- function(ab1,
#' @rdname portion
#' @export
-portion_S <- function(ab1,
- ab2 = NULL,
+portion_S <- function(...,
minimum = 30,
as_percent = FALSE) {
- rsi_calc(type = "S",
- ab1 = ab1,
- ab2 = ab2,
+ rsi_calc(...,
+ type = "S",
include_I = FALSE,
minimum = minimum,
as_percent = as_percent,
@@ -257,77 +251,3 @@ portion_df <- function(data,
res
}
-
-rsi_calc <- function(type,
- ab1,
- ab2,
- include_I,
- minimum,
- as_percent,
- only_count) {
-
- if (NCOL(ab1) > 1) {
- stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
- }
- 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)
- }
-
- print_warning <- FALSE
- if (!is.rsi(ab1)) {
- ab1 <- as.rsi(ab1)
- print_warning <- TRUE
- }
- if (!is.null(ab2)) {
- # ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
- if (NCOL(ab2) > 1) {
- stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
- }
- if (!is.rsi(ab2)) {
- ab2 <- as.rsi(ab2)
- print_warning <- TRUE
- }
- x <- apply(X = data.frame(ab1 = as.integer(ab1),
- ab2 = as.integer(ab2)),
- MARGIN = 1,
- FUN = min)
- } else {
- x <- ab1
- # ab_name <- deparse(substitute(ab1))
- }
-
- if (print_warning == TRUE) {
- warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
- }
-
- if (type == "S") {
- found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
- } else if (type == "I") {
- found <- sum(as.integer(x) == 2, na.rm = TRUE)
- } else if (type == "R") {
- found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
- } else {
- stop("invalid type")
- }
-
- if (only_count == TRUE) {
- return(found)
- }
-
- total <- length(x) - sum(is.na(x))
- if (total < minimum) {
- return(NA)
- }
-
- if (as_percent == TRUE) {
- percent(found / total, force_zero = TRUE)
- } else {
- found / total
- }
-}
diff --git a/R/rsi.R b/R/rsi.R
index 0ab618ad..cef5cfa1 100644
--- a/R/rsi.R
+++ b/R/rsi.R
@@ -20,9 +20,10 @@
#'
#' This function is deprecated. Use the \code{\link{portion}} functions instead.
#' @inheritParams portion
+#' @param ab1,ab2 vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
#' @param interpretation antimicrobial interpretation to check for
#' @param ... deprecated parameters to support usage on older versions
-#' @importFrom dplyr case_when
+#' @importFrom dplyr tibble case_when
#' @export
rsi <- function(ab1,
ab2 = NULL,
@@ -31,12 +32,19 @@ rsi <- function(ab1,
as_percent = FALSE,
...) {
+ if (all(is.null(ab2))) {
+ df <- tibble(ab1 = ab1)
+ } else {
+ df <- tibble(ab1 = ab1,
+ ab2 = ab2)
+ }
+
result <- case_when(
- interpretation == "S" ~ portion_S(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
- interpretation %in% c("SI", "IS") ~ portion_SI(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
- interpretation == "I" ~ portion_I(ab1 = ab1, minimum = minimum, as_percent = FALSE),
- interpretation %in% c("RI", "IR") ~ portion_IR(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
- interpretation == "R" ~ portion_R(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
+ 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) {
diff --git a/R/rsi_calc.R b/R/rsi_calc.R
new file mode 100644
index 00000000..ff6d79db
--- /dev/null
+++ b/R/rsi_calc.R
@@ -0,0 +1,115 @@
+# ==================================================================== #
+# 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. #
+# ==================================================================== #
+
+#' @importFrom dplyr %>% bind_cols pull
+rsi_calc <- function(...,
+ type,
+ include_I,
+ minimum,
+ as_percent,
+ only_count) {
+
+ 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)
+ }
+
+ dots_length <- ...length()
+ dots <- ...elt(1) # it needs this evaluation
+ dots <- rlang::exprs(...) # or this will be a list without actual values
+
+ if ("data.frame" %in% class(dots[[1]]) & dots_length > 1) {
+ # data.frame passed with other columns, like:
+ # septic_patients %>% portion_S(amcl, gent)
+ df <- dots[[1]]
+ dots_df <- data.frame(col1 = df[,1])
+ for (i in 2:dots_length) {
+ dots_col <- as.character(dots[[i]])
+ if (!dots_col %in% colnames(df)) {
+ stop("variable not found: ", dots_col)
+ }
+ dots_df <- dots_df %>% bind_cols(data.frame(df %>% pull(dots_col)))
+ }
+ x <- dots_df[, -1]
+ } else if (dots_length == 1) {
+ # only 1 variable passed (count also be data.frame), like:
+ # portion_S(septic_patients$amcl)
+ # septic_patients$amcl %>% portion_S()
+ x <- dots[[1]]
+ } else {
+ # multiple variables passed without pipe, like:
+ # portion_S(septic_patients$amcl, septic_patients$gent)
+ # with(septic_patients, portion_S(amcl, gent))
+ x <- as.data.frame(rlang::list2(...))
+ }
+
+ print_warning <- FALSE
+ # check integrity of columns: force rsi class
+ if (is.data.frame(x)) {
+ for (i in 1:ncol(x)) {
+ if (!is.rsi(x %>% pull(i))) {
+ x[, i] <- as.rsi(x[, i])
+ print_warning <- TRUE
+ }
+ x[, i] <- x %>% pull(i) %>% as.integer()
+ }
+ x <- apply(X = x,
+ MARGIN = 1,
+ FUN = min)
+ } else {
+ if (!is.rsi(x)) {
+ x <- as.rsi(x)
+ print_warning <- TRUE
+ }
+ }
+
+ if (print_warning == TRUE) {
+ warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
+ call. = FALSE)
+ }
+
+ if (type == "S") {
+ found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
+ } else if (type == "I") {
+ found <- sum(as.integer(x) == 2, na.rm = TRUE)
+ } else if (type == "R") {
+ found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
+ } else {
+ stop("invalid type")
+ }
+
+ if (only_count == TRUE) {
+ return(found)
+ }
+
+ total <- length(x) - sum(is.na(x))
+ if (total < minimum) {
+ return(NA)
+ }
+
+ if (as_percent == TRUE) {
+ percent(found / total, force_zero = TRUE)
+ } else {
+ found / total
+ }
+}
diff --git a/man/count.Rd b/man/count.Rd
index 68b9c12d..38507440 100644
--- a/man/count.Rd
+++ b/man/count.Rd
@@ -13,23 +13,21 @@
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
}
\usage{
-count_R(ab1, ab2 = NULL)
+count_R(...)
-count_IR(ab1, ab2 = NULL)
+count_IR(...)
-count_I(ab1)
+count_I(...)
-count_SI(ab1, ab2 = NULL)
+count_SI(...)
-count_S(ab1, ab2 = NULL)
+count_S(...)
count_df(data, translate_ab = getOption("get_antibiotic_names",
"official"))
}
\arguments{
-\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
-
-\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this 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. 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{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
@@ -39,7 +37,7 @@ count_df(data, translate_ab = getOption("get_antibiotic_names",
Integer
}
\description{
-These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+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
}
diff --git a/man/ggplot_rsi.Rd b/man/ggplot_rsi.Rd
index af6d23be..38e620ee 100644
--- a/man/ggplot_rsi.Rd
+++ b/man/ggplot_rsi.Rd
@@ -11,10 +11,10 @@
\usage{
ggplot_rsi(data, position = NULL, x = "Antibiotic",
fill = "Interpretation", facet = NULL, translate_ab = "official",
- fun = portion_df, ...)
+ alpha = 1, fun = portion_df, ...)
geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
- fill = "Interpretation", translate_ab = "official",
+ fill = "Interpretation", translate_ab = "official", alpha = 1,
fun = portion_df)
facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
@@ -38,6 +38,8 @@ theme_rsi()
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.}
+\item{alpha}{opacity of the fill colours}
+
\item{fun}{function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}}
\item{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
diff --git a/man/n_rsi.Rd b/man/n_rsi.Rd
index f581e7fa..ab5fc099 100644
--- a/man/n_rsi.Rd
+++ b/man/n_rsi.Rd
@@ -4,13 +4,13 @@
\alias{n_rsi}
\title{Count cases with antimicrobial results}
\usage{
-n_rsi(ab1, ab2 = NULL)
+n_rsi(...)
}
\arguments{
-\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
+\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. Its use is equal to \code{\link{n_distinct}}.
+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)
@@ -25,5 +25,6 @@ septic_patients \%>\%
combination_n = n_rsi(cipr, gent))
}
\seealso{
-The \code{\link{portion}} functions to calculate resistance and susceptibility.
+\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
+\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
}
diff --git a/man/portion.Rd b/man/portion.Rd
index 80477540..a2cb59cd 100644
--- a/man/portion.Rd
+++ b/man/portion.Rd
@@ -15,23 +15,21 @@
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
}
\usage{
-portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_R(..., minimum = 30, as_percent = FALSE)
-portion_IR(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_IR(..., minimum = 30, as_percent = FALSE)
-portion_I(ab1, minimum = 30, as_percent = FALSE)
+portion_I(..., minimum = 30, as_percent = FALSE)
-portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_SI(..., minimum = 30, as_percent = FALSE)
-portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_S(..., minimum = 30, as_percent = FALSE)
portion_df(data, translate_ab = getOption("get_antibiotic_names",
"official"), minimum = 30, as_percent = FALSE)
}
\arguments{
-\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
-
-\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this 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. 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.}
@@ -45,7 +43,7 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
-These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). 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{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
}
@@ -66,8 +64,10 @@ The old \code{\link{rsi}} function is still available for backwards compatibilit
For two antibiotics:
\out{}\figure{combi_therapy_2.png}\out{
}
\cr
- Theoretically for three antibiotics:
+ For three antibiotics:
\out{}\figure{combi_therapy_3.png}\out{
}
+ \cr
+ And so on.
}
}
\examples{
@@ -82,11 +82,13 @@ portion_IR(septic_patients$amox)
portion_S(septic_patients$amox)
portion_SI(septic_patients$amox)
-# Since n_rsi counts available isolates (and is used as denominator),
-# you can calculate back to count e.g. non-susceptible isolates:
-portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
-
+# Do the above with pipes:
library(dplyr)
+septic_patients \%>\% portion_R(amox)
+septic_patients \%>\% portion_IR(amox)
+septic_patients \%>\% portion_S(amox)
+septic_patients \%>\% portion_SI(amox)
+
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = portion_S(cipr),
@@ -102,16 +104,15 @@ septic_patients \%>\%
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
-portion_S(septic_patients$amcl) # S = 67.3\%
-n_rsi(septic_patients$amcl) # n = 1570
+septic_patients \%>\% portion_S(amcl) # S = 67.3\%
+septic_patients \%>\% n_rsi(amcl) # n = 1570
-portion_S(septic_patients$gent) # S = 74.0\%
-n_rsi(septic_patients$gent) # n = 1842
+septic_patients \%>\% portion_S(gent) # S = 74.0\%
+septic_patients \%>\% n_rsi(gent) # n = 1842
+
+septic_patients \%>\% portion_S(amcl, gent) # S = 92.1\%
+septic_patients \%>\% n_rsi(amcl, gent) # n = 1504
-with(septic_patients,
- portion_S(amcl, gent)) # S = 92.1\%
-with(septic_patients, # n = 1504
- n_rsi(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
diff --git a/man/rsi.Rd b/man/rsi.Rd
index 41de4bb1..60430bc0 100644
--- a/man/rsi.Rd
+++ b/man/rsi.Rd
@@ -8,9 +8,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
as_percent = FALSE, ...)
}
\arguments{
-\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
-
-\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
+\item{ab1, ab2}{vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.}
\item{interpretation}{antimicrobial interpretation to check for}
diff --git a/tests/testthat/test-atc.R b/tests/testthat/test-atc.R
index b1a0395d..180d8176 100755
--- a/tests/testthat/test-atc.R
+++ b/tests/testthat/test-atc.R
@@ -1,8 +1,7 @@
context("atc.R")
test_that("atc_property works", {
- skip_on_travis() # relies on internet connection of server, don't test
-
+ skip_on_cran() # relies on internet connection of server, don't test
skip_on_appveyor() # security error on AppVeyor
if (!is.null(curl::nslookup("www.whocc.no", error = FALSE))) {
diff --git a/tests/testthat/test-count.R b/tests/testthat/test-count.R
new file mode 100644
index 00000000..67d48a5c
--- /dev/null
+++ b/tests/testthat/test-count.R
@@ -0,0 +1,41 @@
+context("count.R")
+
+test_that("counts work", {
+ # amox resistance in `septic_patients`
+ expect_equal(count_R(septic_patients$amox), 659)
+ expect_equal(count_I(septic_patients$amox), 3)
+ expect_equal(count_S(septic_patients$amox), 336)
+ expect_equal(count_R(septic_patients$amox) + count_I(septic_patients$amox),
+ count_IR(septic_patients$amox))
+ expect_equal(count_S(septic_patients$amox) + count_I(septic_patients$amox),
+ count_SI(septic_patients$amox))
+
+ expect_equal(septic_patients %>% count_S(amcl), 1056)
+ expect_equal(septic_patients %>% count_S(amcl, gent), 1385)
+
+ # count of cases
+ expect_equal(septic_patients %>%
+ group_by(hospital_id) %>%
+ summarise(cipro = count_S(cipr),
+ genta = count_S(gent),
+ combination = count_S(cipr, gent)) %>%
+ pull(combination),
+ c(192, 440, 184, 474))
+
+ # warning for speed loss
+ expect_warning(count_R(as.character(septic_patients$amcl)))
+ expect_warning(count_I(as.character(septic_patients$amcl)))
+ expect_warning(count_S(as.character(septic_patients$amcl,
+ septic_patients$gent)))
+ expect_warning(count_S(septic_patients$amcl,
+ as.character(septic_patients$gent)))
+
+ # check for errors
+ expect_error(count_IR("test", minimum = "test"))
+ expect_error(count_IR("test", as_percent = "test"))
+ expect_error(count_I("test", minimum = "test"))
+ expect_error(count_I("test", as_percent = "test"))
+ expect_error(count_S("test", minimum = "test"))
+ expect_error(count_S("test", as_percent = "test"))
+
+})
diff --git a/tests/testthat/test-portion.R b/tests/testthat/test-portion.R
index c577e627..d5edcd72 100755
--- a/tests/testthat/test-portion.R
+++ b/tests/testthat/test-portion.R
@@ -11,12 +11,19 @@ test_that("portions works", {
expect_equal(portion_S(septic_patients$amox) + portion_I(septic_patients$amox),
portion_SI(septic_patients$amox))
- # pita+genta susceptibility around 98.09%
- expect_equal(suppressWarnings(rsi(septic_patients$pita,
+ expect_equal(septic_patients %>% portion_S(amcl),
+ 0.673,
+ tolerance = 0.001)
+ expect_equal(septic_patients %>% portion_S(amcl, gent),
+ 0.921,
+ tolerance = 0.001)
+
+ # amcl+genta susceptibility around 92.1%
+ expect_equal(suppressWarnings(rsi(septic_patients$amcl,
septic_patients$gent,
interpretation = "S")),
- 0.9535,
- tolerance = 0.0001)
+ 0.9208777,
+ tolerance = 0.000001)
# percentages
expect_equal(septic_patients %>%
@@ -46,25 +53,19 @@ test_that("portions works", {
expect_warning(portion_S(as.character(septic_patients$amcl)))
expect_warning(portion_S(as.character(septic_patients$amcl,
septic_patients$gent)))
- expect_equal(n_rsi(as.character(septic_patients$amcl,
- septic_patients$gent)),
+ expect_warning(n_rsi(as.character(septic_patients$amcl,
+ septic_patients$gent)))
+ expect_equal(suppressWarnings(n_rsi(as.character(septic_patients$amcl,
+ septic_patients$gent))),
1570)
-
# check for errors
- expect_error(portion_IR(septic_patients %>% select(amox, amcl)))
expect_error(portion_IR("test", minimum = "test"))
expect_error(portion_IR("test", as_percent = "test"))
- expect_error(portion_I(septic_patients %>% select(amox, amcl)))
expect_error(portion_I("test", minimum = "test"))
expect_error(portion_I("test", as_percent = "test"))
expect_error(portion_S("test", minimum = "test"))
expect_error(portion_S("test", as_percent = "test"))
- expect_error(portion_S(septic_patients %>% select(amox, amcl)))
- expect_error(portion_S("R", septic_patients %>% select(amox, amcl)))
- expect_error(n_rsi(septic_patients %>% select(amox, amcl)))
- expect_error(n_rsi(septic_patients$amox, septic_patients %>% select(amox, amcl)))
-
# check too low amount of isolates
expect_identical(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1),