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.
as.mic(x, na.rm = FALSE) is.mic(x)
Arguments
| x | character or numeric vector |
|---|---|
| na.rm | a logical indicating whether missing values should be removed |
Value
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.
Details
To interpret MIC values as RSI values, use 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:
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 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().
Stable Lifecycle
The lifecycle of this function is 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.
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See also
Examples
mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16")) is.mic(mic_data) # this can also coerce combined MIC/RSI values: as.mic("<=0.002; S") # will return <=0.002 # mathematical processing treats MICs as numeric values fivenum(mic_data) quantile(mic_data) all(mic_data < 512) # interpret MIC values as.rsi(x = as.mic(2), mo = as.mo("S. pneumoniae"), ab = "AMX", guideline = "EUCAST") as.rsi(x = as.mic(4), mo = as.mo("S. pneumoniae"), ab = "AMX", guideline = "EUCAST") # plot MIC values, see ?plot plot(mic_data) plot(mic_data, mo = "E. coli", ab = "cipro")