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styled, unit test fix
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147
R/mic.R
147
R/mic.R
@ -9,7 +9,7 @@
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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@ -25,20 +25,42 @@
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# these are allowed MIC values and will become [factor] levels
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ops <- c("<", "<=", "", ">=", ">")
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valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
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function(x) paste0(x, "0.00", 1:9)))),
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unique(c(t(vapply(FUN.VALUE = character(104), ops,
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function(x) paste0(x, sort(as.double(paste0("0.0",
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sort(c(1:99, 125, 128, 256, 512, 625)))))))))),
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unique(c(t(vapply(FUN.VALUE = character(103), ops,
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function(x) paste0(x, sort(as.double(paste0("0.",
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c(1:99, 125, 128, 256, 512))))))))),
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c(t(vapply(FUN.VALUE = character(10), ops,
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function(x) paste0(x, sort(c(1:9, 1.5)))))),
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c(t(vapply(FUN.VALUE = character(45), ops,
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function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])))),
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c(t(vapply(FUN.VALUE = character(17), ops,
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function(x) paste0(x, sort(c(2 ^ c(7:11), 192, 80 * c(2:12))))))))
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valid_mic_levels <- c(
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c(t(vapply(
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FUN.VALUE = character(9), ops,
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function(x) paste0(x, "0.00", 1:9)
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))),
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unique(c(t(vapply(
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FUN.VALUE = character(104), ops,
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function(x) {
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paste0(x, sort(as.double(paste0(
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"0.0",
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sort(c(1:99, 125, 128, 256, 512, 625))
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))))
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}
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)))),
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unique(c(t(vapply(
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FUN.VALUE = character(103), ops,
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function(x) {
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paste0(x, sort(as.double(paste0(
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"0.",
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c(1:99, 125, 128, 256, 512)
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))))
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}
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)))),
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c(t(vapply(
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FUN.VALUE = character(10), ops,
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function(x) paste0(x, sort(c(1:9, 1.5)))
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))),
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c(t(vapply(
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FUN.VALUE = character(45), ops,
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function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])
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))),
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c(t(vapply(
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FUN.VALUE = character(17), ops,
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function(x) paste0(x, sort(c(2^c(7:11), 192, 80 * c(2:12))))
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)))
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)
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#' Transform Input to Minimum Inhibitory Concentrations (MIC)
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#'
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@ -48,32 +70,32 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
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#' @param na.rm a [logical] indicating whether missing values should be removed
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#' @param ... arguments passed on to methods
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#' @details To interpret MIC values as RSI values, use [as.rsi()] on MIC values. It supports guidelines from EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`).
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#'
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#'
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#' 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:
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#'
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#'
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#' ```
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#' x <- random_mic(10)
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#' x
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#' #> Class <mic>
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#' #> [1] 16 1 8 8 64 >=128 0.0625 32 32 16
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#'
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#'
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#' is.factor(x)
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#' #> [1] TRUE
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#'
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#'
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#' x[1] * 2
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#' #> [1] 32
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#'
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#'
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#' median(x)
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#' #> [1] 26
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#' ```
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#'
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#'
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#' 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.:
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#'
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#'
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#' ```
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#' x[x > 4]
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#' #> Class <mic>
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#' #> [1] 16 8 8 64 >=128 32 32 16
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#'
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#'
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#' df <- data.frame(x, hospital = "A")
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#' subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)
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#' #> x hospital
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@ -84,11 +106,11 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
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#' #> 9 32 A
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#' #> 10 16 A
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#' ```
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#'
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#'
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#' 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()], [log1p()], [log2()], [log10()], [max()], [mean()], [min()], [prod()], [range()], [round()], [sign()], [signif()], [sin()], [sinh()], [sinpi()], [sqrt()], [sum()], [tan()], [tanh()], [tanpi()], [trigamma()] and [trunc()]. Some functions of the `stats` package are also implemented: [median()], [quantile()], [mad()], [IQR()], [fivenum()]. Also, [boxplot.stats()] is supported. Since [sd()] and [var()] are non-generic functions, these could not be extended. Use [mad()] as an alternative, or use e.g. `sd(as.numeric(x))` where `x` is your vector of MIC values.
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#'
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#'
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#' Using [as.double()] or [as.numeric()] on MIC values will remove the operators and return a numeric vector. Do **not** use [as.integer()] on MIC values as by the \R convention on [factor]s, it will return the index of the factor levels (which is often useless for regular users).
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#'
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#'
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#' Use [droplevels()] to drop unused levels. At default, it will return a plain factor. Use `droplevels(..., as.mic = TRUE)` to maintain the `<mic>` class.
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#' @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.
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#' @aliases mic
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@ -101,26 +123,30 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
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#'
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#' # this can also coerce combined MIC/RSI values:
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#' as.mic("<=0.002; S")
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#'
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#'
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#' # mathematical processing treats MICs as numeric values
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#' fivenum(mic_data)
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#' quantile(mic_data)
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#' all(mic_data < 512)
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#'
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#' # interpret MIC values
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#' as.rsi(x = as.mic(2),
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#' mo = as.mo("Streptococcus pneumoniae"),
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#' ab = "AMX",
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#' guideline = "EUCAST")
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#' as.rsi(x = as.mic(c(0.01, 2, 4, 8)),
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#' mo = as.mo("Streptococcus pneumoniae"),
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#' ab = "AMX",
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#' guideline = "EUCAST")
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#' as.rsi(
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#' x = as.mic(2),
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#' mo = as.mo("Streptococcus pneumoniae"),
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#' ab = "AMX",
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#' guideline = "EUCAST"
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#' )
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#' as.rsi(
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#' x = as.mic(c(0.01, 2, 4, 8)),
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#' mo = as.mo("Streptococcus pneumoniae"),
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#' ab = "AMX",
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#' guideline = "EUCAST"
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#' )
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#'
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#' # plot MIC values, see ?plot
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#' plot(mic_data)
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#' plot(mic_data, mo = "E. coli", ab = "cipro")
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#'
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#'
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#' if (require("ggplot2")) {
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#' autoplot(mic_data, mo = "E. coli", ab = "cipro")
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#' }
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@ -133,7 +159,7 @@ valid_mic_levels <- c(c(t(vapply(FUN.VALUE = character(9), ops,
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as.mic <- function(x, na.rm = FALSE) {
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meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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if (is.mic(x)) {
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x
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} else {
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@ -143,7 +169,7 @@ as.mic <- function(x, na.rm = FALSE) {
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}
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x[trimws(x) == ""] <- NA
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x.bak <- x
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# comma to period
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x <- gsub(",", ".", x, fixed = TRUE)
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# transform Unicode for >= and <=
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@ -177,27 +203,30 @@ as.mic <- function(x, na.rm = FALSE) {
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x <- gsub("[.]$", "", x, perl = TRUE)
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# trim it
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x <- trimws(x)
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## previously unempty values now empty - should return a warning later on
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x[x.bak != "" & x == ""] <- "invalid"
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na_before <- x[is.na(x) | x == ""] %pm>% length()
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x[!x %in% valid_mic_levels] <- NA
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na_after <- x[is.na(x) | x == ""] %pm>% length()
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if (na_before != na_after) {
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list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ""] %pm>%
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unique() %pm>%
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sort() %pm>%
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vector_and(quotes = TRUE)
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warning_("in `as.mic()`: ", na_after - na_before, " results truncated (",
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round(((na_after - na_before) / length(x)) * 100),
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"%) that were invalid MICs: ",
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list_missing, call = FALSE)
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round(((na_after - na_before) / length(x)) * 100),
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"%) that were invalid MICs: ",
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list_missing,
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call = FALSE
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)
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}
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set_clean_class(factor(x, levels = valid_mic_levels, ordered = TRUE),
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new_class = c("mic", "ordered", "factor"))
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new_class = c("mic", "ordered", "factor")
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)
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}
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}
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@ -206,7 +235,8 @@ all_valid_mics <- function(x) {
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return(FALSE)
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}
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x_mic <- tryCatch(suppressWarnings(as.mic(x[!is.na(x)])),
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error = function(e) NA)
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error = function(e) NA
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)
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!any(is.na(x_mic)) && !all(is.na(x))
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}
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@ -215,7 +245,8 @@ all_valid_mics <- function(x) {
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#' @format NULL
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#' @export
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NA_mic_ <- set_clean_class(factor(NA, levels = valid_mic_levels, ordered = TRUE),
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new_class = c("mic", "ordered", "factor"))
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new_class = c("mic", "ordered", "factor")
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)
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#' @rdname as.mic
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#' @export
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@ -271,8 +302,10 @@ type_sum.mic <- function(x, ...) {
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#' @noRd
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print.mic <- function(x, ...) {
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cat("Class <mic>",
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ifelse(length(levels(x)) < length(valid_mic_levels), font_red(" with dropped levels"), ""),
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"\n", sep = "")
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ifelse(length(levels(x)) < length(valid_mic_levels), font_red(" with dropped levels"), ""),
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"\n",
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sep = ""
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)
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print(as.character(x), quote = FALSE)
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att <- attributes(x)
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if ("na.action" %in% names(att)) {
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@ -378,12 +411,12 @@ hist.mic <- function(x, ...) {
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get_skimmers.mic <- function(column) {
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skimr::sfl(
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skim_type = "mic",
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p0 = ~stats::quantile(., probs = 0, na.rm = TRUE, names = FALSE),
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p25 = ~stats::quantile(., probs = 0.25, na.rm = TRUE, names = FALSE),
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p50 = ~stats::quantile(., probs = 0.5, na.rm = TRUE, names = FALSE),
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p75 = ~stats::quantile(., probs = 0.75, na.rm = TRUE, names = FALSE),
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p100 = ~stats::quantile(., probs = 1, na.rm = TRUE, names = FALSE),
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hist = ~skimr::inline_hist(log2(stats::na.omit(.)), 5)
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p0 = ~ stats::quantile(., probs = 0, na.rm = TRUE, names = FALSE),
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p25 = ~ stats::quantile(., probs = 0.25, na.rm = TRUE, names = FALSE),
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p50 = ~ stats::quantile(., probs = 0.5, na.rm = TRUE, names = FALSE),
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p75 = ~ stats::quantile(., probs = 0.75, na.rm = TRUE, names = FALSE),
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p100 = ~ stats::quantile(., probs = 1, na.rm = TRUE, names = FALSE),
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hist = ~ skimr::inline_hist(log2(stats::na.omit(.)), 5)
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)
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}
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@ -679,7 +712,7 @@ is_lower <- function(el) {
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#' @export
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#' @noRd
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`^.mic` <- function(e1, e2) {
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as.double(e1) ^ as.double(e2)
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as.double(e1)^as.double(e2)
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}
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#' @method %% mic
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