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(v1.5.0.9032) All group generics for MICs

This commit is contained in:
dr. M.S. (Matthijs) Berends 2021-03-07 13:52:39 +01:00
parent 91dd755cac
commit 850c123de7
40 changed files with 641 additions and 203 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.5.0.9031
Date: 2021-03-05
Version: 1.5.0.9032
Date: 2021-03-07
Title: Antimicrobial Resistance Data Analysis
Authors@R: c(
person(role = c("aut", "cre"),

39
NAMESPACE
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@ -1,8 +1,10 @@
# Generated by roxygen2: do not edit by hand
S3method("!",mic)
S3method("!=",mic)
S3method("%%",mic)
S3method("%/%",mic)
S3method("&",mic)
S3method("*",mic)
S3method("+",mic)
S3method("-",mic)
@ -35,6 +37,10 @@ S3method("[[<-",mic)
S3method("[[<-",mo)
S3method("[[<-",rsi)
S3method("^",mic)
S3method("|",mic)
S3method(abs,mic)
S3method(acos,mic)
S3method(acosh,mic)
S3method(all,mic)
S3method(all.equal,isolate_identifier)
S3method(any,mic)
@ -47,6 +53,10 @@ S3method(as.rsi,data.frame)
S3method(as.rsi,default)
S3method(as.rsi,disk)
S3method(as.rsi,mic)
S3method(asin,mic)
S3method(asinh,mic)
S3method(atan,mic)
S3method(atanh,mic)
S3method(barplot,disk)
S3method(barplot,mic)
S3method(barplot,rsi)
@ -57,14 +67,30 @@ S3method(c,mic)
S3method(c,mo)
S3method(c,rsi)
S3method(ceiling,mic)
S3method(cos,mic)
S3method(cosh,mic)
S3method(cospi,mic)
S3method(cummax,mic)
S3method(cummin,mic)
S3method(cumprod,mic)
S3method(cumsum,mic)
S3method(digamma,mic)
S3method(droplevels,mic)
S3method(droplevels,rsi)
S3method(exp,mic)
S3method(expm1,mic)
S3method(floor,mic)
S3method(format,bug_drug_combinations)
S3method(gamma,mic)
S3method(hist,mic)
S3method(kurtosis,data.frame)
S3method(kurtosis,default)
S3method(kurtosis,matrix)
S3method(lgamma,mic)
S3method(log,mic)
S3method(log10,mic)
S3method(log1p,mic)
S3method(log2,mic)
S3method(max,mic)
S3method(mean,mic)
S3method(median,mic)
@ -85,18 +111,31 @@ S3method(print,mo_renamed)
S3method(print,mo_uncertainties)
S3method(print,pca)
S3method(print,rsi)
S3method(prod,mic)
S3method(quantile,mic)
S3method(range,mic)
S3method(rep,mo)
S3method(round,mic)
S3method(sign,mic)
S3method(signif,mic)
S3method(sin,mic)
S3method(sinh,mic)
S3method(sinpi,mic)
S3method(skewness,data.frame)
S3method(skewness,default)
S3method(skewness,matrix)
S3method(sort,mic)
S3method(sqrt,mic)
S3method(sum,mic)
S3method(summary,mic)
S3method(summary,mo)
S3method(summary,pca)
S3method(summary,rsi)
S3method(tan,mic)
S3method(tanh,mic)
S3method(tanpi,mic)
S3method(trigamma,mic)
S3method(trunc,mic)
S3method(unique,ab)
S3method(unique,disk)
S3method(unique,isolate_identifier)

4
NEWS.md
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@ -1,5 +1,5 @@
# AMR 1.5.0.9031
## <small>Last updated: 5 March 2021</small>
# AMR 1.5.0.9032
## <small>Last updated: 7 March 2021</small>
### New
* Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.

11
R/aa_helper_functions.R
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@ -961,15 +961,8 @@ formatted_filesize <- function(...) {
}
create_pillar_column <- function(x, ...) {
new_pillar_shaft_simple <- import_fn("new_pillar_shaft_simple", "pillar", error_on_fail = FALSE)
if (!is.null(new_pillar_shaft_simple)) {
new_pillar_shaft_simple(x, ...)
} else {
# does not exist in package 'pillar' anymore
structure(list(x),
class = "pillar_shaft_simple",
...)
}
new_pillar_shaft_simple <- import_fn("new_pillar_shaft_simple", "pillar")
new_pillar_shaft_simple(x, ...)
}
# copied from vctrs::s3_register by their permission:

2
R/ab_from_text.R
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@ -26,7 +26,7 @@
#' Retrieve Antimicrobial Drug Names and Doses from Clinical Text
#'
#' Use this function on e.g. clinical texts from health care records. It returns a [list] with all antimicrobial drugs, doses and forms of administration found in the texts.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param text text to analyse
#' @param type type of property to search for, either `"drug"`, `"dose"` or `"administration"`, see *Examples*
#' @param collapse character to pass on to `paste(, collapse = ...)` to only return one character per element of `text`, see *Examples*

2
R/ggplot_pca.R
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@ -26,7 +26,7 @@
#' PCA Biplot with `ggplot2`
#'
#' Produces a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param x an object returned by [pca()], [prcomp()] or [princomp()]
#' @inheritParams stats::biplot.prcomp
#' @param labels an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].

2
R/ggplot_rsi.R
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@ -26,7 +26,7 @@
#' AMR Plots with `ggplot2`
#'
#' Use these functions to create bar plots for AMR data analysis. All functions rely on [ggplot2][ggplot2::ggplot()] functions.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param data a [data.frame] with column(s) of class [`rsi`] (see [as.rsi()])
#' @param position position adjustment of bars, either `"fill"`, `"stack"` or `"dodge"`
#' @param x variable to show on x axis, either `"antibiotic"` (default) or `"interpretation"` or a grouping variable

445
R/mic.R
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@ -25,13 +25,51 @@
#' Transform Input to Minimum Inhibitory Concentrations (MIC)
#'
#' This transforms a vector to a new class [`mic`], which is an ordered [factor] with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as `NA` with a warning.
#' This ransforms vectors to a new class [`mic`], which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
#' @inheritSection lifecycle Stable Lifecycle
#' @rdname as.mic
#' @param x vector
#' @param x character or numeric vector
#' @param na.rm a logical indicating whether missing values should be removed
#' @details To interpret MIC values as RSI values, use [as.rsi()] on MIC values. It supports guidelines from EUCAST and CLSI.
#' @return Ordered [factor] with additional class [`mic`]
#'
#' This class for MIC values is a quite a special data type: formally it is an ordered factor with valid MIC values as factor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:
#'
#' ```
#' x <- random_mic(10)
#' x
#' #> Class <mic>
#' #> [1] 16 1 8 8 64 >=128 0.0625 32 32 16
#'
#' is.factor(x)
#' #> [1] TRUE
#'
#' x[1] * 2
#' #> [1] 32
#'
#' median(x)
#' #> [1] 26
#' ```
#'
#' This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering using numeric values in data analysis, e.g.:
#'
#' ```
#' x[x > 4]
#' #> Class <mic>
#' #> [1] 16 8 8 64 >=128 32 32 16
#'
#' df <- data.frame(x, hospital = "A")
#' subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)
#' #> x hospital
#' #> 1 16 A
#' #> 5 64 A
#' #> 6 >=128 A
#' #> 8 32 A
#' #> 9 32 A
#' #> 10 16 A
#' ```
#'
#' The following [generic functions][groupGeneric()] are implemented for the MIC class: `!`, `!=`, `%%`, `%/%`, `&`, `*`, `+`, `-`, `/`, `<`, `<=`, `==`, `>`, `>=`, `^`, `|`, [abs()], [acos()], [acosh()], [all()], [any()], [asin()], [asinh()], [atan()], [atanh()], [ceiling()], [cos()], [cosh()], [cospi()], [cummax()], [cummin()], [cumprod()], [cumsum()], [digamma()], [exp()], [expm1()], [floor()], [gamma()], [lgamma()], [log()], [log10()], [log1p()], [log2()], [max()], [mean()], [median()], [min()], [prod()], [quantile()], [range()], [round()], [sign()], [signif()], [sin()], [sinh()], [sinpi()], [sqrt()], [sum()], [tan()], [tanh()], [tanpi()], [trigamma()] and [trunc()].
#' @return Ordered [factor] with additional class [`mic`], that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a numeric value.
#' @aliases mic
#' @export
#' @seealso [as.rsi()]
@ -197,6 +235,7 @@ pillar_shaft.mic <- function(x, ...) {
out <- pasted
out[is.na(x)] <- font_na(NA)
out <- gsub("(<|=|>)", font_silver("\\1"), out)
out <- gsub("([.]?0+)$", font_white("\\1"), out)
create_pillar_column(out, align = "right", width = max(nchar(pasted)))
}
@ -211,22 +250,17 @@ type_sum.mic <- function(x, ...) {
print.mic <- function(x, ...) {
cat("Class <mic>\n")
print(as.character(x), quote = FALSE)
att <- attributes(x)
if ("na.action" %in% names(att)) {
cat(font_silver(paste0("(NA ", class(att$na.action), ": ", paste0(att$na.action, collapse = ", "), ")\n")))
}
}
#' @method summary mic
#' @export
#' @noRd
summary.mic <- function(object, ...) {
x <- object
n_total <- length(x)
x <- x[!is.na(x)]
n <- length(x)
value <- c("Class" = "mic",
"<NA>" = n_total - n,
"Min." = as.character(sort(x)[1]),
"Max." = as.character(sort(x)[n]))
class(value) <- c("summaryDefault", "table")
value
summary(as.double(object), ...)
}
#' @method [ mic
@ -281,85 +315,52 @@ unique.mic <- function(x, incomparables = FALSE, ...) {
y
}
#' @method range mic
#' @method sort mic
#' @export
#' @noRd
range.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
sort.mic <- function(x, decreasing = FALSE, ...) {
if (decreasing == TRUE) {
ord <- order(-as.double(x))
} else {
ord <- order(as.double(x))
}
out <- c(as.character(rng[1]), as.character(rng[length(rng)]))
as.double(as.mic(out))
x[ord]
}
#' @method min mic
#' @method hist mic
#' @export
#' @noRd
min.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
}
as.double(as.mic(as.character(rng[1])))
hist.mic <- function(x, ...) {
warning_("Use `plot()` or `ggplot()` for plotting MIC values", call = FALSE)
hist(log2(x))
}
#' @method max mic
#' @export
#' @noRd
max.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
}
as.double(as.mic(as.character(rng[length(rng)])))
# will be exported using s3_register() in R/zzz.R
get_skimmers.mic <- function(column) {
skimr::sfl(
skim_type = "mic",
min = ~min(., na.rm = TRUE),
max = ~max(., na.rm = TRUE),
median = ~median(., na.rm = TRUE),
n_unique = ~pm_n_distinct(., na.rm = TRUE),
hist_log2 = ~skimr::inline_hist(log2(stats::na.omit(.)))
)
}
#' @method sum mic
#' @export
#' @noRd
sum.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
}
sum(as.double(rng))
}
#' @method all mic
#' @export
#' @noRd
all.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
}
all(as.double(rng))
}
#' @method any mic
#' @export
#' @noRd
any.mic <- function(..., na.rm = FALSE) {
rng <- sort(c(...))
if (na.rm == TRUE) {
rng <- rng[!is.na(rng)]
}
any(as.double(rng))
}
# Miscellaneous mathematical functions ------------------------------------
#' @method mean mic
#' @export
#' @noRd
mean.mic <- function(x, na.rm = FALSE, ...) {
mean(as.double(x), na.rm = na.rm, ...)
mean.mic <- function(x, trim = 0, na.rm = FALSE, ...) {
mean(as.double(x), trim = trim, na.rm = na.rm, ...)
}
#' @method median mic
#' @export
#' @noRd
median.mic <- function(x, na.rm = FALSE, ...) {
median(as.double(x), na.rm = na.rm, ...)
stats::median(as.double(x), na.rm = na.rm, ...)
}
#' @method quantile mic
@ -367,22 +368,236 @@ median.mic <- function(x, na.rm = FALSE, ...) {
#' @noRd
quantile.mic <- function(x, probs = seq(0, 1, 0.25), na.rm = FALSE,
names = TRUE, type = 7, ...) {
quantile(as.double(x), props = props, na.rm = na.rm, names = names, type = type, ...)
stats::quantile(as.double(x), props = props, na.rm = na.rm, names = names, type = type, ...)
}
# Math (see ?groupGeneric) ----------------------------------------------
#' @method abs mic
#' @export
#' @noRd
abs.mic <- function(x) {
abs(as.double(x))
}
#' @method sign mic
#' @export
#' @noRd
sign.mic <- function(x) {
sign(as.double(x))
}
#' @method sqrt mic
#' @export
#' @noRd
sqrt.mic <- function(x) {
sqrt(as.double(x))
}
#' @method floor mic
#' @export
#' @noRd
floor.mic <- function(x) {
floor(as.double(x))
}
#' @method ceiling mic
#' @export
#' @noRd
ceiling.mic <- function(x) {
ceiling(as.double(x))
}
#' @method trunc mic
#' @export
#' @noRd
trunc.mic <- function(x, ...) {
trunc(as.double(x), ...)
}
#' @method round mic
#' @export
#' @noRd
round.mic <- function(x, digits = 0) {
round(as.double(x), digits = digits)
}
#' @method signif mic
#' @export
#' @noRd
signif.mic <- function(x, digits = 6) {
signif(as.double(x), digits = digits)
}
#' @method exp mic
#' @export
#' @noRd
exp.mic <- function(x) {
exp(as.double(x))
}
#' @method log mic
#' @export
#' @noRd
log.mic <- function(x, base = exp(1)) {
log(as.double(x), base = base)
}
#' @method log10 mic
#' @export
#' @noRd
log10.mic <- function(x) {
log10(as.double(x))
}
#' @method log2 mic
#' @export
#' @noRd
log2.mic <- function(x) {
log2(as.double(x))
}
#' @method expm1 mic
#' @export
#' @noRd
expm1.mic <- function(x) {
expm1(as.double(x))
}
#' @method log1p mic
#' @export
#' @noRd
log1p.mic <- function(x) {
log1p(as.double(x))
}
#' @method cos mic
#' @export
#' @noRd
cos.mic <- function(x) {
cos(as.double(x))
}
#' @method sin mic
#' @export
#' @noRd
sin.mic <- function(x) {
sin(as.double(x))
}
#' @method tan mic
#' @export
#' @noRd
tan.mic <- function(x) {
tan(as.double(x))
}
#' @method cospi mic
#' @export
#' @noRd
cospi.mic <- function(x) {
cospi(as.double(x))
}
#' @method sinpi mic
#' @export
#' @noRd
sinpi.mic <- function(x) {
sinpi(as.double(x))
}
#' @method tanpi mic
#' @export
#' @noRd
tanpi.mic <- function(x) {
tanpi(as.double(x))
}
#' @method acos mic
#' @export
#' @noRd
acos.mic <- function(x) {
acos(as.double(x))
}
#' @method asin mic
#' @export
#' @noRd
asin.mic <- function(x) {
asin(as.double(x))
}
#' @method atan mic
#' @export
#' @noRd
atan.mic <- function(x) {
atan(as.double(x))
}
#' @method cosh mic
#' @export
#' @noRd
cosh.mic <- function(x) {
cosh(as.double(x))
}
#' @method sinh mic
#' @export
#' @noRd
sinh.mic <- function(x) {
sinh(as.double(x))
}
#' @method tanh mic
#' @export
#' @noRd
tanh.mic <- function(x) {
tanh(as.double(x))
}
#' @method acosh mic
#' @export
#' @noRd
acosh.mic <- function(x) {
acosh(as.double(x))
}
#' @method asinh mic
#' @export
#' @noRd
asinh.mic <- function(x) {
asinh(as.double(x))
}
#' @method atanh mic
#' @export
#' @noRd
atanh.mic <- function(x) {
atanh(as.double(x))
}
#' @method lgamma mic
#' @export
#' @noRd
lgamma.mic <- function(x) {
lgamma(as.double(x))
}
#' @method gamma mic
#' @export
#' @noRd
gamma.mic <- function(x) {
gamma(as.double(x))
}
#' @method digamma mic
#' @export
#' @noRd
digamma.mic <- function(x) {
digamma(as.double(x))
}
#' @method trigamma mic
#' @export
#' @noRd
trigamma.mic <- function(x) {
trigamma(as.double(x))
}
#' @method cumsum mic
#' @export
#' @noRd
cumsum.mic <- function(x) {
cumsum(as.double(x))
}
#' @method cumprod mic
#' @export
#' @noRd
cumprod.mic <- function(x) {
cumprod(as.double(x))
}
#' @method cummax mic
#' @export
#' @noRd
cummax.mic <- function(x) {
cummax(as.double(x))
}
#' @method cummin mic
#' @export
#' @noRd
cummin.mic <- function(x) {
cummin(as.double(x))
}
# Ops (see ?groupGeneric) -----------------------------------------------
#' @method + mic
#' @export
@ -433,6 +648,27 @@ ceiling.mic <- function(x) {
as.double(e1) %/% as.double(e2)
}
#' @method & mic
#' @export
#' @noRd
`&.mic` <- function(e1, e2) {
as.double(e1) & as.double(e2)
}
#' @method | mic
#' @export
#' @noRd
`|.mic` <- function(e1, e2) {
as.double(e1) | as.double(e2)
}
#' @method ! mic
#' @export
#' @noRd
`!.mic` <- function(x) {
!as.double(x)
}
#' @method == mic
#' @export
#' @noRd
@ -475,36 +711,47 @@ ceiling.mic <- function(x) {
as.double(e1) > as.double(e2)
}
#' @method sort mic
# Summary (see ?groupGeneric) -------------------------------------------
#' @method all mic
#' @export
#' @noRd
sort.mic <- function(x, decreasing = FALSE, ...) {
if (decreasing == TRUE) {
ord <- order(-as.double(x))
} else {
ord <- order(as.double(x))
}
x[ord]
all.mic <- function(..., na.rm = FALSE) {
all(as.double(c(...)), na.rm = na.rm)
}
#' @method hist mic
#' @method any mic
#' @export
#' @noRd
hist.mic <- function(x, ...) {
warning_("Use `plot()` or `ggplot()` for plotting MIC values", call = FALSE)
hist(as.double(x), ...)
any.mic <- function(..., na.rm = FALSE) {
any(as.double(c(...)), na.rm = na.rm)
}
# will be exported using s3_register() in R/zzz.R
get_skimmers.mic <- function(column) {
skimr::sfl(
skim_type = "mic",
min = ~as.character(sort(stats::na.omit(.))[1]),
max = ~as.character(sort(stats::na.omit(.))[length(stats::na.omit(.))]),
median = ~as.character(stats::na.omit(.)[as.double(stats::na.omit(.)) == median(as.double(stats::na.omit(.)))])[1],
n_unique = ~pm_n_distinct(., na.rm = TRUE),
hist_log2 = ~skimr::inline_hist(log2(as.double(stats::na.omit(.))))
)
#' @method sum mic
#' @export
#' @noRd
sum.mic <- function(..., na.rm = FALSE) {
sum(as.double(c(...)), na.rm = na.rm)
}
#' @method prod mic
#' @export
#' @noRd
prod.mic <- function(..., na.rm = FALSE) {
prod(as.double(c(...)), na.rm = na.rm)
}
#' @method min mic
#' @export
#' @noRd
min.mic <- function(..., na.rm = FALSE) {
min(as.double(c(...)), na.rm = na.rm)
}
#' @method max mic
#' @export
#' @noRd
max.mic <- function(..., na.rm = FALSE) {
max(as.double(c(...)), na.rm = na.rm)
}
#' @method range mic
#' @export
#' @noRd
range.mic <- function(..., na.rm = FALSE) {
range(as.double(c(...)), na.rm = na.rm)
}

2
R/pca.R
View File

@ -26,7 +26,7 @@
#' Principal Component Analysis (for AMR)
#'
#' Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [data.frame] containing numeric columns
#' @param ... columns of `x` to be selected for PCA, can be unquoted since it supports quasiquotation.
#' @inheritParams stats::prcomp

21
R/plot.R
View File

@ -108,7 +108,6 @@ plot.mic <- function(x,
fn = as.mic,
language = language,
...)
barplot(x,
col = cols_sub$cols,
main = main,
@ -116,7 +115,7 @@ plot.mic <- function(x,
ylab = ylab,
xlab = xlab,
axes = FALSE)
axis(2, seq(0, max(as.double(x))))
axis(2, seq(0, max(x)))
if (!is.null(cols_sub$sub)) {
mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
}
@ -124,15 +123,15 @@ plot.mic <- function(x,
if (any(colours_RSI %in% cols_sub$cols)) {
legend_txt <- character(0)
legend_col <- character(0)
if (colours_RSI[2] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
legend_txt <- "Susceptible"
legend_col <- colours_RSI[2]
}
if (colours_RSI[3] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
legend_col <- c(legend_col, colours_RSI[3])
}
if (colours_RSI[1] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
legend_txt <- c(legend_txt, "Resistant")
legend_col <- c(legend_col, colours_RSI[1])
}
@ -317,15 +316,15 @@ plot.disk <- function(x,
if (any(colours_RSI %in% cols_sub$cols)) {
legend_txt <- character(0)
legend_col <- character(0)
if (colours_RSI[1] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
legend_txt <- "Resistant"
legend_col <- colours_RSI[1]
}
if (colours_RSI[3] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
legend_col <- c(legend_col, colours_RSI[3])
}
if (colours_RSI[2] %in% cols_sub$cols) {
if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
legend_txt <- c(legend_txt, "Susceptible")
legend_col <- c(legend_col, colours_RSI[2])
}
@ -459,8 +458,8 @@ plot_prepare_table <- function(x, expand) {
if (is.mic(x)) {
if (expand == TRUE) {
# expand range for MIC by adding factors of 2 from lowest to highest so all MICs in between also print
extra_range <- max(as.double(x)) / 2
while (min(extra_range) / 2 > min(as.double(x))) {
extra_range <- max(x) / 2
while (min(extra_range) / 2 > min(x)) {
extra_range <- c(min(extra_range) / 2, extra_range)
}
nms <- extra_range
@ -525,7 +524,7 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_RSI, f
cols <- "#BEBEBE"
sub <- NULL
}
list(cols = cols, sub = sub, guideline = guideline)
list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
}

12
R/random.R
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@ -26,7 +26,7 @@
#' Random MIC Values/Disk Zones/RSI Generation
#'
#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param size desired size of the returned vector
#' @param mo any character that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any character that can be coerced to a valid antimicrobial agent code with [as.ab()]
@ -119,7 +119,15 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3))))
set_range <- valid_mics[!is.na(valid_mics)]
}
return(as.mic(sample(set_range, size = size, replace = TRUE)))
out <- as.mic(sample(set_range, size = size, replace = TRUE))
# 50% chance that lowest will get <= and highest will get >=
if (runif(1) > 0.5) {
out[out == min(out)] <- paste0("<=", out[out == min(out)])
}
if (runif(1) > 0.5) {
out[out == max(out)] <- paste0(">=", out[out == max(out)])
}
return(out)
} else if (type == "DISK") {
set_range <- seq(from = as.integer(min(df$breakpoint_R) / 1.25),
to = as.integer(max(df$breakpoint_S) * 1.25),

2
R/resistance_predict.R
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@ -26,7 +26,7 @@
#' Predict antimicrobial resistance
#'
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min` and `se_max`. See *Examples* for a real live example.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Stable Lifecycle
#' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date`

BIN
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BIN
data-raw/AMR_latest.tar.gz
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2
data-raw/translations.tsv
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@ -33,7 +33,7 @@ no .*growth TRUE FALSE FALSE keine? .*wachstum geen .*groei no .*crecimientonon
no|not TRUE FALSE FALSE keine? geen|niet no|sin sem non sem
Susceptible TRUE FALSE FALSE Empfindlich Gevoelig Susceptible
Intermediate TRUE FALSE FALSE Mittlere Intermediair Intermedio
Incr. exposure TRUE FALSE FALSE Empfindlich, erh Belastung Gevoelig, 'incr. exposure' Susceptible, 'incr. exposure'
Incr. exposure TRUE FALSE FALSE Empfindlich, erh Belastung 'Incr. exposure' 'Incr. exposure'
Resistant TRUE FALSE FALSE Resistent Resistent Resistente
antibiotic TRUE TRUE FALSE Antibiotikum antibioticum antibiótico
Antibiotic TRUE TRUE FALSE Antibiotikum Antibioticum Antibiótico

1 pattern regular_expr case_sensitive affect_mo_name de nl es it fr pt
33 no|not TRUE FALSE FALSE keine? geen|niet no|sin sem non sem
34 Susceptible TRUE FALSE FALSE Empfindlich Gevoelig Susceptible
35 Intermediate TRUE FALSE FALSE Mittlere Intermediair Intermedio
36 Incr. exposure TRUE FALSE FALSE Empfindlich, erh Belastung Gevoelig, 'incr. exposure' 'Incr. exposure' Susceptible, 'incr. exposure' 'Incr. exposure'
37 Resistant TRUE FALSE FALSE Resistent Resistent Resistente
38 antibiotic TRUE TRUE FALSE Antibiotikum antibioticum antibiótico
39 Antibiotic TRUE TRUE FALSE Antibiotikum Antibioticum Antibiótico

2
docs/404.html
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

2
docs/LICENSE-text.html
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@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

2
docs/articles/index.html
View File

@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

2
docs/authors.html
View File

@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

2
docs/index.html
View File

@ -43,7 +43,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

12
docs/news/index.html
View File

@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -236,13 +236,13 @@
<small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="amr-1509031" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9031">
<a href="#amr-1509031" class="anchor"></a>AMR 1.5.0.9031<small> Unreleased </small>
<div id="amr-1509032" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9032">
<a href="#amr-1509032" class="anchor"></a>AMR 1.5.0.9032<small> Unreleased </small>
</h1>
<div id="last-updated-5-march-2021" class="section level2">
<div id="last-updated-7-march-2021" class="section level2">
<h2 class="hasAnchor">
<a href="#last-updated-5-march-2021" class="anchor"></a><small>Last updated: 5 March 2021</small>
<a href="#last-updated-7-march-2021" class="anchor"></a><small>Last updated: 7 March 2021</small>
</h2>
<div id="new" class="section level3">
<h3 class="hasAnchor">

2
docs/pkgdown.yml
View File

@ -12,7 +12,7 @@ articles:
datasets: datasets.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
last_built: 2021-03-05T14:27Z
last_built: 2021-03-07T12:50Z
urls:
reference: https://msberends.github.io/AMR//reference
article: https://msberends.github.io/AMR//articles

2
docs/reference/AMR.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

9
docs/reference/ab_from_text.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -301,12 +301,13 @@
<p>With using <code>collapse</code>, this function will return a <a href='https://rdrr.io/r/base/character.html'>character</a>:<br />
<code>df %&gt;% mutate(abx = ab_from_text(clinical_text, collapse = "|"))</code></p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>

41
docs/reference/as.mic.html
View File

@ -49,7 +49,7 @@
<script src="../extra.js"></script>
<meta property="og:title" content="Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic" />
<meta property="og:description" content="This transforms a vector to a new class mic, which is an ordered factor with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as NA with a warning." />
<meta property="og:description" content="This ransforms vectors to a new class mic, which treats the input as decimal numbers, while maintaining operators (such as &quot;&amp;gt;=&quot;) and only allowing valid MIC values known to the field of (medical) microbiology." />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
@ -82,7 +82,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -239,7 +239,7 @@
</div>
<div class="ref-description">
<p>This transforms a vector to a new class <code>mic</code>, which is an ordered <a href='https://rdrr.io/r/base/factor.html'>factor</a> with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as <code>NA</code> with a warning.</p>
<p>This ransforms vectors to a new class <code>mic</code>, which treats the input as decimal numbers, while maintaining operators (such as "&gt;=") and only allowing valid MIC values known to the field of (medical) microbiology.</p>
</div>
<pre class="usage"><span class='fu'>as.mic</span><span class='op'>(</span><span class='va'>x</span>, na.rm <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>
@ -251,7 +251,7 @@
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>x</th>
<td><p>vector</p></td>
<td><p>character or numeric vector</p></td>
</tr>
<tr>
<th>na.rm</th>
@ -261,10 +261,41 @@
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>Ordered <a href='https://rdrr.io/r/base/factor.html'>factor</a> with additional class <code>mic</code></p>
<p>Ordered <a href='https://rdrr.io/r/base/factor.html'>factor</a> with additional class <code>mic</code>, that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a numeric value.</p>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>To interpret MIC values as RSI values, use <code><a href='as.rsi.html'>as.rsi()</a></code> on MIC values. It supports guidelines from EUCAST and CLSI.</p>
<p>This class for MIC values is a quite a special data type: formally it is an ordered factor with valid MIC values as factor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:</p><pre><span class='va'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='random.html'>random_mic</a></span><span class='op'>(</span><span class='fl'>10</span><span class='op'>)</span>
<span class='va'>x</span>
<span class='co'>#&gt; Class &lt;mic&gt;</span>
<span class='co'>#&gt; [1] 16 1 8 8 64 &gt;=128 0.0625 32 32 16</span>
<span class='fu'><a href='https://rdrr.io/r/base/factor.html'>is.factor</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>
<span class='co'>#&gt; [1] TRUE</span>
<span class='va'>x</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>]</span> <span class='op'>*</span> <span class='fl'>2</span>
<span class='co'>#&gt; [1] 32</span>
<span class='fu'><a href='https://rdrr.io/r/stats/median.html'>median</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>
<span class='co'>#&gt; [1] 26</span>
</pre>
<p>This makes it possible to maintain operators that often come with MIC values, such "&gt;=" and "&lt;=", even when filtering using numeric values in data analysis, e.g.:</p><pre><span class='va'>x</span><span class='op'>[</span><span class='va'>x</span> <span class='op'>&gt;</span> <span class='fl'>4</span><span class='op'>]</span>
<span class='co'>#&gt; Class &lt;mic&gt;</span>
<span class='co'>#&gt; [1] 16 8 8 64 &gt;=128 32 32 16</span>
<span class='va'>df</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span><span class='op'>(</span><span class='va'>x</span>, hospital <span class='op'>=</span> <span class='st'>"A"</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>df</span>, <span class='va'>x</span> <span class='op'>&gt;</span> <span class='fl'>4</span><span class='op'>)</span> <span class='co'># or with dplyr: df %&gt;% filter(x &gt; 4)</span>
<span class='co'>#&gt; x hospital</span>
<span class='co'>#&gt; 1 16 A</span>
<span class='co'>#&gt; 5 64 A</span>
<span class='co'>#&gt; 6 &gt;=128 A</span>
<span class='co'>#&gt; 8 32 A</span>
<span class='co'>#&gt; 9 32 A</span>
<span class='co'>#&gt; 10 16 A</span>
</pre>
<p>The following <a href='https://rdrr.io/r/base/groupGeneric.html'>generic functions</a> are implemented for the MIC class: <code><a href='https://rdrr.io/r/base/Logic.html'>!</a></code>, <code><a href='https://rdrr.io/r/base/Comparison.html'>!=</a></code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>%%</a></code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>%/%</a></code>, <code>&amp;</code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>+</a></code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>-</a></code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>/</a></code>, <code>&lt;</code>, <code>&lt;=</code>, <code><a href='https://rdrr.io/r/base/Comparison.html'>==</a></code>, <code>&gt;</code>, <code>&gt;=</code>, <code><a href='https://rdrr.io/r/base/Arithmetic.html'>^</a></code>, <code><a href='https://rdrr.io/r/base/Logic.html'>|</a></code>, <code><a href='https://rdrr.io/r/base/MathFun.html'>abs()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>acos()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>acosh()</a></code>, <code><a href='https://rdrr.io/r/base/all.html'>all()</a></code>, <code><a href='https://rdrr.io/r/base/any.html'>any()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>asin()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>asinh()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>atan()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>atanh()</a></code>, <code><a href='https://rdrr.io/r/base/Round.html'>ceiling()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>cos()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>cosh()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>cospi()</a></code>, <code><a href='https://rdrr.io/r/base/cumsum.html'>cummax()</a></code>, <code><a href='https://rdrr.io/r/base/cumsum.html'>cummin()</a></code>, <code><a href='https://rdrr.io/r/base/cumsum.html'>cumprod()</a></code>, <code><a href='https://rdrr.io/r/base/cumsum.html'>cumsum()</a></code>, <code><a href='https://rdrr.io/r/base/Special.html'>digamma()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>exp()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>expm1()</a></code>, <code><a href='https://rdrr.io/r/base/Round.html'>floor()</a></code>, <code><a href='https://rdrr.io/r/base/Special.html'>gamma()</a></code>, <code><a href='https://rdrr.io/r/base/Special.html'>lgamma()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>log()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>log10()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>log1p()</a></code>, <code><a href='https://rdrr.io/r/base/Log.html'>log2()</a></code>, <code><a href='https://rdrr.io/r/base/Extremes.html'>max()</a></code>, <code><a href='https://rdrr.io/r/base/mean.html'>mean()</a></code>, <code><a href='https://rdrr.io/r/stats/median.html'>median()</a></code>, <code><a href='https://rdrr.io/r/base/Extremes.html'>min()</a></code>, <code><a href='https://rdrr.io/r/base/prod.html'>prod()</a></code>, <code><a href='https://rdrr.io/r/stats/quantile.html'>quantile()</a></code>, <code><a href='https://rdrr.io/r/base/range.html'>range()</a></code>, <code><a href='https://rdrr.io/r/base/Round.html'>round()</a></code>, <code><a href='https://rdrr.io/r/base/sign.html'>sign()</a></code>, <code><a href='https://rdrr.io/r/base/Round.html'>signif()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>sin()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>sinh()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>sinpi()</a></code>, <code><a href='https://rdrr.io/r/base/MathFun.html'>sqrt()</a></code>, <code><a href='https://rdrr.io/r/base/sum.html'>sum()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>tan()</a></code>, <code><a href='https://rdrr.io/r/base/Hyperbolic.html'>tanh()</a></code>, <code><a href='https://rdrr.io/r/base/Trig.html'>tanpi()</a></code>, <code><a href='https://rdrr.io/r/base/Special.html'>trigamma()</a></code> and <code><a href='https://rdrr.io/r/base/Round.html'>trunc()</a></code>.</p>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>

9
docs/reference/ggplot_pca.html
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@ -82,7 +82,7 @@
</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">1.5.0.9013</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -384,12 +384,13 @@
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The colours for labels and points can be changed by adding another scale layer for colour, like <code><a href='https://ggplot2.tidyverse.org/reference/scale_viridis.html'>scale_colour_viridis_d()</a></code> or <code><a href='https://ggplot2.tidyverse.org/reference/scale_brewer.html'>scale_colour_brewer()</a></code>.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># `example_isolates` is a data set available in the AMR package.</span>

9
docs/reference/ggplot_rsi.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -415,12 +415,13 @@
<p><code>labels_rsi_count()</code> print datalabels on the bars with percentage and amount of isolates using <code><a href='https://ggplot2.tidyverse.org/reference/geom_text.html'>ggplot2::geom_text()</a></code>.</p>
<p><code>ggplot_rsi()</code> is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (<code>%&gt;%</code>). See <em>Examples</em>.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>

2
docs/reference/index.html
View File

@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

9
docs/reference/pca.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9016</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -311,12 +311,13 @@
<p>The <code>pca()</code> function takes a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> as input and performs the actual PCA with the <span style="R">R</span> function <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp()</a></code>.</p>
<p>The result of the <code>pca()</code> function is a <a href='https://rdrr.io/r/stats/prcomp.html'>prcomp</a> object, with an additional attribute <code>non_numeric_cols</code> which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by <code><a href='ggplot_pca.html'>ggplot_pca()</a></code>.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>

9
docs/reference/random.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -280,12 +280,13 @@
<p>The base R function <code><a href='https://rdrr.io/r/base/sample.html'>sample()</a></code> is used for generating values.</p>
<p>Generated values are based on the latest EUCAST guideline implemented in the <a href='rsi_translation.html'>rsi_translation</a> data set. To create specific generated values per bug or drug, set the <code>mo</code> and/or <code>ab</code> argument.</p>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>

9
docs/reference/resistance_predict.html
View File

@ -82,7 +82,7 @@
</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">1.5.0.9019</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>
@ -364,12 +364,13 @@
<li><p><code>"lin"</code> or <code>"linear"</code>: a linear regression model</p></li>
</ul>
<h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
<h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
<h2 class="hasAnchor" id="interpretation-of-r-and-s-i"><a class="anchor" href="#interpretation-of-r-and-s-i"></a>Interpretation of R and S/I</h2>

2
docs/survey.html
View File

@ -81,7 +81,7 @@
</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">1.5.0.9031</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
</span>
</div>

8
man/ab_from_text.Rd
View File

@ -54,10 +54,12 @@ With using \code{collapse}, this function will return a \link{character}:\cr
\code{df \%>\% mutate(abx = ab_from_text(clinical_text, collapse = "|"))}
}
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{

38
man/as.mic.Rd
View File

@ -11,18 +11,50 @@ as.mic(x, na.rm = FALSE)
is.mic(x)
}
\arguments{
\item{x}{vector}
\item{x}{character or numeric vector}
\item{na.rm}{a logical indicating whether missing values should be removed}
}
\value{
Ordered \link{factor} with additional class \code{\link{mic}}
Ordered \link{factor} with additional class \code{\link{mic}}, that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a numeric value.
}
\description{
This transforms a vector to a new class \code{\link{mic}}, which is an ordered \link{factor} with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as \code{NA} with a warning.
This ransforms vectors to a new class \code{\link{mic}}, which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
}
\details{
To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST and CLSI.
This class for MIC values is a quite a special data type: formally it is an ordered factor with valid MIC values as factor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:\preformatted{x <- random_mic(10)
x
#> Class <mic>
#> [1] 16 1 8 8 64 >=128 0.0625 32 32 16
is.factor(x)
#> [1] TRUE
x[1] * 2
#> [1] 32
median(x)
#> [1] 26
}
This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering using numeric values in data analysis, e.g.:\preformatted{x[x > 4]
#> Class <mic>
#> [1] 16 8 8 64 >=128 32 32 16
df <- data.frame(x, hospital = "A")
subset(df, x > 4) # or with dplyr: df \%>\% filter(x > 4)
#> x hospital
#> 1 16 A
#> 5 64 A
#> 6 >=128 A
#> 8 32 A
#> 9 32 A
#> 10 16 A
}
The following \link[=groupGeneric]{generic functions} are implemented for the MIC class: \code{!}, \code{!=}, \code{\%\%}, \code{\%/\%}, \code{&}, \code{*}, \code{+}, \code{-}, \code{/}, \code{<}, \code{<=}, \code{==}, \code{>}, \code{>=}, \code{^}, \code{|}, \code{\link[=abs]{abs()}}, \code{\link[=acos]{acos()}}, \code{\link[=acosh]{acosh()}}, \code{\link[=all]{all()}}, \code{\link[=any]{any()}}, \code{\link[=asin]{asin()}}, \code{\link[=asinh]{asinh()}}, \code{\link[=atan]{atan()}}, \code{\link[=atanh]{atanh()}}, \code{\link[=ceiling]{ceiling()}}, \code{\link[=cos]{cos()}}, \code{\link[=cosh]{cosh()}}, \code{\link[=cospi]{cospi()}}, \code{\link[=cummax]{cummax()}}, \code{\link[=cummin]{cummin()}}, \code{\link[=cumprod]{cumprod()}}, \code{\link[=cumsum]{cumsum()}}, \code{\link[=digamma]{digamma()}}, \code{\link[=exp]{exp()}}, \code{\link[=expm1]{expm1()}}, \code{\link[=floor]{floor()}}, \code{\link[=gamma]{gamma()}}, \code{\link[=lgamma]{lgamma()}}, \code{\link[=log]{log()}}, \code{\link[=log10]{log10()}}, \code{\link[=log1p]{log1p()}}, \code{\link[=log2]{log2()}}, \code{\link[=max]{max()}}, \code{\link[=mean]{mean()}}, \code{\link[=median]{median()}}, \code{\link[=min]{min()}}, \code{\link[=prod]{prod()}}, \code{\link[=quantile]{quantile()}}, \code{\link[=range]{range()}}, \code{\link[=round]{round()}}, \code{\link[=sign]{sign()}}, \code{\link[=signif]{signif()}}, \code{\link[=sin]{sin()}}, \code{\link[=sinh]{sinh()}}, \code{\link[=sinpi]{sinpi()}}, \code{\link[=sqrt]{sqrt()}}, \code{\link[=sum]{sum()}}, \code{\link[=tan]{tan()}}, \code{\link[=tanh]{tanh()}}, \code{\link[=tanpi]{tanpi()}}, \code{\link[=trigamma]{trigamma()}} and \code{\link[=trunc]{trunc()}}.
}
\section{Stable Lifecycle}{

8
man/ggplot_pca.Rd
View File

@ -108,10 +108,12 @@ Produces a \code{ggplot2} variant of a so-called \href{https://en.wikipedia.org/
\details{
The colours for labels and points can be changed by adding another scale layer for colour, like \code{scale_colour_viridis_d()} or \code{scale_colour_brewer()}.
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\examples{

8
man/ggplot_rsi.Rd
View File

@ -140,10 +140,12 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
\code{\link[=ggplot_rsi]{ggplot_rsi()}} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\verb{\%>\%}). See \emph{Examples}.
}
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{

8
man/pca.Rd
View File

@ -59,10 +59,12 @@ The \code{\link[=pca]{pca()}} function takes a \link{data.frame} as input and pe
The result of the \code{\link[=pca]{pca()}} function is a \link{prcomp} object, with an additional attribute \code{non_numeric_cols} which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by \code{\link[=ggplot_pca]{ggplot_pca()}}.
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{

8
man/random.Rd
View File

@ -35,10 +35,12 @@ The base R function \code{\link[=sample]{sample()}} is used for generating value
Generated values are based on the latest EUCAST guideline implemented in the \link{rsi_translation} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument.
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{

8
man/resistance_predict.Rd
View File

@ -100,10 +100,12 @@ Valid options for the statistical model (argument \code{model}) are:
\item \code{"lin"} or \code{"linear"}: a linear regression model
}
}
\section{Maturing Lifecycle}{
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Interpretation of R and S/I}{

85
tests/testthat/test-mic.R
View File

@ -31,12 +31,12 @@ test_that("mic works", {
expect_true(as.mic("1") > as.mic("<=0.0625"))
expect_true(as.mic("1") < as.mic(">=32"))
expect_true(is.mic(as.mic(8)))
expect_equal(as.double(as.mic(">=32")), 32)
expect_equal(as.numeric(as.mic(">=32")), 32)
expect_equal(as.integer(as.mic(">=32")), 32)
expect_equal(suppressWarnings(as.logical(as.mic("INVALID VALUE"))), NA)
# all levels should be valid MICs
x <- as.mic(c(2, 4))
expect_s3_class(x[1], "mic")
@ -60,12 +60,83 @@ test_that("mic works", {
}
expect_output(print(as.mic(c(1, 2, 4, 8))))
expect_equal(summary(as.mic(c(2, 8))),
structure(c("Class" = "mic",
"<NA>" = "0",
"Min." = "2",
"Max." = "8"), class = c("summaryDefault", "table")))
expect_s3_class(summary(as.mic(c(2, 8))), c("summaryDefault", "table"))
library(dplyr, warn.conflicts = FALSE)
expect_output(print(tibble(m = as.mic(2:4))))
})
test_that("mathematical functions on mic work", {
skip_on_cran()
x <- random_mic(50)
x_double <- as.double(gsub("[<=>]+", "", as.character(x)))
suppressWarnings(expect_identical(mean(x), mean(x_double)))
suppressWarnings(expect_identical(median(x), median(x_double)))
suppressWarnings(expect_identical(quantile(x), quantile(x_double)))
suppressWarnings(expect_identical(abs(x), abs(x_double)))
suppressWarnings(expect_identical(sign(x), sign(x_double)))
suppressWarnings(expect_identical(sqrt(x), sqrt(x_double)))
suppressWarnings(expect_identical(floor(x), floor(x_double)))
suppressWarnings(expect_identical(ceiling(x), ceiling(x_double)))
suppressWarnings(expect_identical(trunc(x), trunc(x_double)))
suppressWarnings(expect_identical(round(x), round(x_double)))
suppressWarnings(expect_identical(signif(x), signif(x_double)))
suppressWarnings(expect_identical(exp(x), exp(x_double)))
suppressWarnings(expect_identical(log(x), log(x_double)))
suppressWarnings(expect_identical(log10(x), log10(x_double)))
suppressWarnings(expect_identical(log2(x), log2(x_double)))
suppressWarnings(expect_identical(expm1(x), expm1(x_double)))
suppressWarnings(expect_identical(log1p(x), log1p(x_double)))
suppressWarnings(expect_identical(cos(x), cos(x_double)))
suppressWarnings(expect_identical(sin(x), sin(x_double)))
suppressWarnings(expect_identical(tan(x), tan(x_double)))
suppressWarnings(expect_identical(cospi(x), cospi(x_double)))
suppressWarnings(expect_identical(sinpi(x), sinpi(x_double)))
suppressWarnings(expect_identical(tanpi(x), tanpi(x_double)))
suppressWarnings(expect_identical(acos(x), acos(x_double)))
suppressWarnings(expect_identical(asin(x), asin(x_double)))
suppressWarnings(expect_identical(atan(x), atan(x_double)))
suppressWarnings(expect_identical(cosh(x), cosh(x_double)))
suppressWarnings(expect_identical(sinh(x), sinh(x_double)))
suppressWarnings(expect_identical(tanh(x), tanh(x_double)))
suppressWarnings(expect_identical(acosh(x), acosh(x_double)))
suppressWarnings(expect_identical(asinh(x), asinh(x_double)))
suppressWarnings(expect_identical(atanh(x), atanh(x_double)))
suppressWarnings(expect_identical(lgamma(x), lgamma(x_double)))
suppressWarnings(expect_identical(gamma(x), gamma(x_double)))
suppressWarnings(expect_identical(digamma(x), digamma(x_double)))
suppressWarnings(expect_identical(trigamma(x), trigamma(x_double)))
suppressWarnings(expect_identical(cumsum(x), cumsum(x_double)))
suppressWarnings(expect_identical(cumprod(x), cumprod(x_double)))
suppressWarnings(expect_identical(cummax(x), cummax(x_double)))
suppressWarnings(expect_identical(cummin(x), cummin(x_double)))
suppressWarnings(expect_identical(!x, !(x_double)))
suppressWarnings(expect_identical(all(x), all(x_double)))
suppressWarnings(expect_identical(any(x), any(x_double)))
suppressWarnings(expect_identical(sum(x), sum(x_double)))
suppressWarnings(expect_identical(prod(x), prod(x_double)))
suppressWarnings(expect_identical(min(x), min(x_double)))
suppressWarnings(expect_identical(max(x), max(x_double)))
suppressWarnings(expect_identical(range(x), range(x_double)))
el1 <- random_mic(50)
el1_double <- as.double(gsub("[<=>]+", "", as.character(el1)))
el2 <- random_mic(50)
el2_double <- as.double(gsub("[<=>]+", "", as.character(el2)))
suppressWarnings(expect_identical(el1 + el2, el1_double + el2_double))
suppressWarnings(expect_identical(el1 - el2, el1_double - el2_double))
suppressWarnings(expect_identical(el1 * el2, el1_double * el2_double))
suppressWarnings(expect_identical(el1 / el2, el1_double / el2_double))
suppressWarnings(expect_identical(el1 ^ el2, el1_double ^ el2_double))
suppressWarnings(expect_identical(el1 %% el2, el1_double %% el2_double))
suppressWarnings(expect_identical(el1 %/% el2, el1_double %/% el2_double))
suppressWarnings(expect_identical(el1 & el2, el1_double & el2_double))
suppressWarnings(expect_identical(el1 | el2, el1_double | el2_double))
suppressWarnings(expect_identical(el1 == el2, el1_double == el2_double))
suppressWarnings(expect_identical(el1 != el2, el1_double != el2_double))
suppressWarnings(expect_identical(el1 < el2, el1_double < el2_double))
suppressWarnings(expect_identical(el1 <= el2, el1_double <= el2_double))
suppressWarnings(expect_identical(el1 >= el2, el1_double >= el2_double))
suppressWarnings(expect_identical(el1 > el2, el1_double > el2_double))
})